2025Activity report​Project-TeamI4S

6.1.1 Supervisor

• Name:
  Supervisor​​
• Keywords:
  Middleware, Sensors, Python,​​​‌ Digital twin, SHM (Structural​ Health Monitoring), Control system​‌
• Functional Description:
  Supervisor is​​ a middleware designed for​​​‌ efficiently handling data fetched​ from sensors or online​‌ sources, backing up this​​ data in a secure​​​‌ manner, and launching calculation​ programs in a synchronized​‌ way.
• Release Contributions:
  First​​ complete version.
• Contact:
  Jean​​​‌ Dumoulin
• Participants:
  Jean Dumoulin,​ Thibaud Toullier, Mathias Malandain,​‌ 3 anonymous participants
• Partner:​​
  Université Gustave Eiffel

6.1.2​​​‌ Spycic

• Name:
  Spycic library​
• Keywords:
  Python, C++, Binding​‌
• Scientific Description:
  Spycic is​​ a header-only C++ library​​​‌ for fetching and calling​ Python functions from C++​‌ code. Designed as a​​ wrapper of the C/Python​​​‌ API, Spycic strongly relies​ on variadic templates to​‌ make it possible to​​ call in a simple​​​‌ way Python functions with​ different signatures and an​‌ arbitrary number of arguments.​​ GIL handling, exception handling​​​‌ and type casting are​ performed under the hood,​‌ so as to make​​ use as simple as​​​‌ possible.
• Functional Description:

  The​ Spycic (Simple Python Calls​‌ In C++) header-only library​​ is a wrapper around​​​‌ the C/Python API that​ provides a handful of​‌ functions allowing for simple​​ calls to Python functions​​ from a C++ code.​​​‌ Python functions to be‌ used may be declared‌​‌ in several *independent* source​​ files.

  Spycic provides the​​​‌ following functions:

  * `fetchFunction(const‌ char* functionName, const char*‌​‌ sourceCode)` is used to​​ fetch the Python function​​​‌ called `functionName` from a‌ given Python code `sourceCode`‌​‌ provided as a C-style​​ string. The function is​​​‌ returned as a `PyObject*`.‌ * `fetchFunction(const char* functionName,‌​‌ std::string& sourceCode)` is used​​ to fetch the Python​​​‌ function called `functionName` from‌ a given Python code‌​‌ `sourceCode` provided as a​​ `std::string`. The function is​​​‌ returned as a `PyObject*`.‌ * `runFunction<returnType>(PyObject* func, Values...‌​‌ values)` is used to​​ call a Python function​​​‌ (imported as a `PyObject*`)‌ with an arbitrary number‌​‌ of arguments. The return​​ type has to be​​​‌ specified as a template‌ argument, for example, `runFunction<double>(f,‌​‌ arg1, arg2)`. The return​​ type can be `void`,​​​‌ as in `runFunction<void>(f, arg1,‌ arg2)`. All necessary operations‌​‌ (GIL handling, formatting, type​​ castings, etc.) are performed​​​‌ under the hood. The‌ arguments are provided as‌​‌ C++ POD (Plain Old​​ Data) values and/or `std::vector`​​​‌ containers, and the output‌ (if any) is a‌​‌ C++ POD or vector​​ as well.

  A fresh​​​‌ exception class called 'PythonError'‌ is also defined in‌​‌ order to handle errors​​ that occur during calls​​​‌ to functions provided by‌ the C/Python API itself.‌​‌

  Client code must still​​ call functions `Py_Initialize()` and​​​‌ `Py_Finalize()` to be able‌ to use the Python‌​‌ interpreter.

• Release Contributions:
  The​​ handling of `std::vector` objects​​​‌ (containing floating point numbers‌ or integers) as both‌​‌ function inputs and function​​ outputs, and the handling​​​‌ of functions returning `void`,‌ were added since version‌​‌ 0.1.
• News of the​​ Year:
  Regroupement sous la​​​‌ forme d'une bibliothèque header-only,‌ appels transparents de fonctions‌​‌ ne retournant aucune valeur,​​ vérification des overflows lors​​​‌ du casting de valeurs‌ de retour (Python vers‌​‌ C), mémoisation pour l'import​​ des modules, pipeline CI,​​​‌ passage en REUSE-compliant en‌ vue d'une ouverture open‌​‌ source.
• Contact:
  Mathias Malandain​​
• Participant:
  Mathias Malandain

6.1.3​​​‌ PythonFMUGenerator

• Keywords:
  Cosimulation, FMI,‌ Cyber-physical systems
• Scientific Description:‌​‌
  PythonFMUGenerator is a tool​​ for the automatic encapsulation​​​‌ of Python code into‌ C++-based standardized cosimulation units‌​‌ (FMUs). It only relies​​ on a Python source​​​‌ file and a JSON‌ description of the properties‌​‌ of the generated FMU.​​ This makes it possible​​​‌ to integrate on-demand FMU‌ generation to a system‌​‌ model assembly and simulation​​ pipeline, contrary to existing​​​‌ tools that create templates‌ to be populated by‌​‌ hand before compilation and​​ FMU generation.
• Functional Description:​​​‌

  FMI is a fast-growing‌ standard for the cosimulation‌​‌ of large mutli- and​​ cyberphysical system models. It​​​‌ relies on the encapsulation‌ of source code, written‌​‌ in various tools and​​ languages, into cosimulation units​​​‌ called FMUs that share‌ a common interface. However,‌​‌ the encapsulation of Python​​ code into an FMU​​​‌ is still a technical‌ challenge that very few‌​‌ tools try to address.​​

  PythonFMUGenerator is a tool​​​‌ for the automatic encapsulation‌ of Python code into‌​‌ C++-based FMUs for cosimulation.​​ It only relies on​​​‌ a Python source file‌ and a JSON description‌​‌ of the properties of​​​‌ the generated FMU. This​ makes it possible to​‌ integrate on-demand FMU generation​​ to a system model​​​‌ assembly and simulation pipeline,​ contrary to existing tools​‌ that create templates to​​ be populated by hand​​​‌ before compilation and FMU​ generation.

  PythonFMUGenerator relies on​‌ the Spycic library (from​​ the same author), that​​​‌ acts as a wrapper​ around the C/Python API​‌ so as to considerably​​ simplify Python function calls​​​‌ from C or C++​ code. It is based​‌ on FMICodeGenerator, a tool​​ developed by Andreas Nicolai​​​‌ (ghorwin) and coworkers, itself​ under a BSD3 license.​‌

• News of the Year:​​
  Passage au standard FMI3​​​‌ (API intégrale + logiques​ d'exécution pour l'initialisation, l'avancement​‌ temporel et l'entrée/sortie de​​ valeurs de tous les​​​‌ types gérés par le​ standard), gestion des vecteurs​‌ en entrées/sorties des FMU,​​ pipeline CI pour de​​​‌ nombreux tests de l'intégralité​ de la chaîne de​‌ génération des FMU.
• Contact:​​
  Mathias Malandain
• Participants:
  Benoit​​​‌ Caillaud, Mathias Malandain, Thibaud​ Toullier

7.1.1 Strain​​​‌ Transfer Modeling and Post-Processing​ for Distributed Fiber Optic​‌ Sensors

Participants: Mira Kabbara​​, Xavier Chapeleau,​​​‌ Qinghua Zhang.

Distributed​ fiber optic sensors are​‌ widely used in structural​​ health monitoring due to​​​‌ their high spatial resolution.​ Accurate interpretation of their​‌ measurements requires careful consideration​​ of strain transfer through​​​‌ protective coatings. A simplified​ one-dimensional strain transfer model,​‌ incorporating a strain lag​​ parameter dependent on cable​​​‌ properties and installation conditions,​ has been commonly used.​‌ This study extends the​​ model to robust four-layered​​​‌ cables, including steel-reinforced and​ polymer-coated designs, and assesses​‌ its performance under strain​​ gradients using finite element​​​‌ simulations. Results show that​ the model accurately predicts​‌ strain in steel-reinforced cables​​ but is less precise​​​‌ for polymer-coated cables, highlighting​ the importance of interlayer​‌ stiffness and strain lag​​ calibration.

To enable practical​​​‌ use in high-resolution measurements,​ efficient post-processing algorithms are​‌ developed to solve the​​ 1D strain transfer equation​​​‌ and its inverse problem.​ These methods allow rapid​‌ reconstruction of the actual​​ strain profile from raw​​​‌ sensor data, supporting applications​ such as real-time vibration​‌ analysis and structural health​​ monitoring. Together, the modeling​​​‌ and post-processing framework improves​ both the accuracy and​‌ usability of fiber optic​​ sensors in complex environments.​​​‌ 24, 47

7.1.2​ Subpixel motion estimation for​‌ video-based target-free vibration monitoring​​ under complex environmental conditions​​​‌

Participants: Zhilei Luo,​ Boualem Merainani, Vincent​‌ Baltazart, Qinghua Zhang​​, Michael Doehler.​​​‌

An emerging technique to​ measure structural vibrations is​‌ based on motion signals​​ extracted from video images.​​​‌ The use of video​ cameras offers multiple advantages​‌ over traditional mechanical sensors:​​ contactless measurement, large coverage,​​​‌ easy installation and maintenance.​ This work proposes a​‌ new method for real-time​​ motion signal extraction from​​​‌ video images with subpixel​ accuracy. It aims to​‌ address the challenges posed​​ by complex operating conditions,​​​‌ namely illumination variations and​ background interference. Illumination robustness​‌ is achieved by efficiently​​ combining image intensity interpolation​​​‌ with an affine brightness​ and contrast tuning transformation.​‌ Background robustness is obtained​​ by automatically selecting active​​ pixels in the processed​​​‌ images. Moreover, the on-line‌ numerical computations are fast‌​‌ enough for real-time applications.​​ Results of theoretical analysis​​​‌ ensure that the considered‌ optimization criteria are well-posed‌​‌ and that, in the​​ main step of subpixel​​​‌ motion estimation, the involved‌ nonlinear optimization problem is‌​‌ efficiently solved in closed-form​​ through a linear least​​​‌ squares problem. The performance‌ of the proposed method‌​‌ is evaluated both on​​ simulated images and on​​​‌ laboratory experiments with a‌ target-free cantilever beam in‌​‌ comparison with existing methods.​​ The reported results demonstrate​​​‌ the robustness and computational‌ efficiency of the proposed‌​‌ method under complex environmental​​ conditions, allowing real-time computation​​​‌ on a standard laptop‌ for vibration monitoring with‌​‌ more than 100 virtual​​ sensors at 600 frames​​​‌ per second. 25

7.1.3‌ Monitoring for sustainable and‌​‌ inclusive urban areas

Participants:​​ Jean Dumoulin.

Urban​​​‌ resilience requires continuous monitoring‌ of critical services and‌​‌ infrastructure to respond effectively​​ to hazards and multi-risk​​​‌ scenarios, including climate change‌ and pandemics. Advanced monitoring‌​‌ approaches now integrate Earth​​ observation, positioning, navigation, ICT​​​‌ technologies, and citizen-sourced or‌ “non-sensor” data to enhance‌​‌ situational awareness. By combining​​ these diverse data streams​​​‌ and processing them with‌ artificial intelligence and high-performance‌​‌ computing, cities can achieve​​ more informed intervention planning,​​​‌ improved service continuity, and‌ ultimately safer, smarter, and‌​‌ more inclusive urban environments.​​ 55

7.1.4 Ozone Concentration​​​‌ Estimation from Infrared Images‌ Using Extinction Coefficient

Participants:‌​‌ Jean Dumoulin.

Air​​ quality assessment requires concentration​​​‌ measurement of various polluting‌ gases, typically requiring multiple‌​‌ expensive sensors, each dedicated​​ to a specific pollutant.​​​‌ Therefore, there is a‌ need for costeffective sensors‌​‌ capable of detecting one​​ or more pollutants, with​​​‌ less reliability, such as‌ camera-based sensors, but enabling‌​‌ denser sampling. In this​​ work, we investigate how​​​‌ the extinction coefficients estimated‌ from an infrared camera‌​‌ may be useful for​​ predicting ground-level ozone concentration.​​​‌ In addition to these‌ coefficients, we show how‌​‌ weather and pollution measures​​ collected from stations near​​​‌ the studied area are‌ useful, with different machine‌​‌ learning methods, to better​​ predict ozone concentration. The​​​‌ performance of the models‌ is validated through a‌​‌ comprehensive evaluation using MAE,​​ RMSE and R-Squared metrics.​​​‌ Parameter selection methods are‌ also used to study‌​‌ the impact of different​​ meteorological parameters and other​​​‌ pollutant concentrations on the‌ prediction of ozone concentration.‌​‌ 42

7.1.5 Outdoor Hybrid​​ Solar Road Demonstrator Monitoring​​​‌ Using Infrared Thermography with‌ Embedded Local Probes for‌​‌ Energy Harvesting Performance Evaluation​​

Participants: Jean Dumoulin,​​​‌ Domenico Vizzari, Lucas‌ Czamanski Meireles, Thibaud‌​‌ Toullier.

This study​​ investigates, in a natural​​​‌ environment, the thermal behavior‌ of an innovative pavement‌​‌ system with thermal and​​ solar energy collection functionalities.​​​‌ The whole structure is‌ continuously monitored using temperature‌​‌ and heat flux sensor​​ probes integrated inside the​​​‌ structure. Local weather conditions‌ are also monitored. Infrared‌​‌ Thermography is used as​​ a complementary non-invasive technique​​​‌ to monitor temperature surface‌ distributions with time and‌​‌ assess the efficiency of​​ heat transfer within the​​​‌ pavement structure. All sensors‌ are connected to a‌​‌ newly developed platform that​​​‌ centralized data access, visualization,​ and storage, enabling seamless​‌ management and user interactions.​​ The obtained results are​​​‌ presented and discussed. 43​

7.1.6 Comparative study of​‌ image quality acquired on​​ a new portable dynamic​​​‌ laboratory test bench using​ two thermal time constant​‌ uncooled IRFPA microbolometric camera​​

Participants: Boualem Merainani,​​​‌ Thibaud Toullier, Jean​ Dumoulin.

An experimental​‌ setup dedicated to assessing​​ and comparing the image​​​‌ quality of uncooled IRFPA​ microbolometer cameras with different​‌ thermal time constants has​​ been designed and realized.​​​‌ Initially, we explored a​ concept involving heated objects​‌ mounted on two opposing​​ arms rotating at different​​​‌ temperatures; However, practical constraints,​ such as the need​‌ for electrical brushes to​​ transfer current, as well​​​‌ as significant challenges related​ to mass balance and​‌ installation complexity, made this​​ approach impractical. Consequently, we​​​‌ adopted a new solution​ comprising fixed thermal panels​‌ and a self-designed rotating​​ disk inspired by optical​​​‌ choppers. We designed the​ test bench to be​‌ compact and portable, enhancing​​ its versatility and making​​​‌ it adaptable to various​ testing environments. Figure 1​‌ illustrates both the CAD​​ design and the real​​​‌ implementation of the test​ bench. 51

7.1.7 PEGASE4​‌ : a generic board​​ for embedded SHM applications​​​‌

Participants: Vincent Le Cam​, Arthur Bouché,​‌ Adji Toure, Antoine​​ Barre, Lucas Doula​​​‌.

This work presents​ the latest version of​‌ PEGASE, a versatile wireless​​ board designed for Structural​​​‌ Health Monitoring (SHM) applications.​ The first part highlights​‌ the technological advances of​​ PEGASE, including its generic​​​‌ processing capabilities with an​ embedded Buildroot Linux system,​‌ an inertial measurement unit​​ (IMU), GNSS receivers, and​​​‌ multiple I/O interfaces. The​ discussion then focuses on​‌ key scientific innovations, such​​ as the novel use​​​‌ of the GNSS/PPS signal​ to achieve microsecond-level synchronization​‌ within Linux, and energy​​ management strategies optimized for​​​‌ efficient battery and solar-cell​ usage. Finally, practical SHM​‌ applications are presented to​​ illustrate PEGASE in action,​​​‌ including its deployment in​ a wireless acoustic emission​‌ monitoring system. 48

7.1.8​​ Advanced optical and distributed​​​‌ sensing for monitoring interfaces​ and structural behavior

Participants:​‌ Arij Fawaz, Xavier​​ Chapeleau.

Recent developments​​​‌ in fiber-optic and distributed​ sensing technologies are enabling​‌ new strategies for monitoring​​ interfaces, durability, and load​​​‌ transfer mechanisms across structural​ and material systems. These​‌ contributions illustrate how embedded​​ and bonded optical sensors​​​‌ can provide high-resolution measurements​ under static, cyclic, and​‌ dynamic loading conditions.

Fiber​​ Bragg Grating (FBG) sensors​​​‌ were embedded in castable​ polyurethane resins to evaluate​‌ their suitability for ultrasonic​​ applications. The study examined​​​‌ strain evolution during polymerization​ and showed spectral shifts​‌ of up to 10​​ nm, highlighting the need​​​‌ to account for material​ shrinkage when selecting interrogation​‌ wavelengths. After curing, FBGs​​ successfully detected 100 kHz​​​‌ ultrasonic waves, with acrylate​ and polyimide coatings transmitting​‌ comparable energy to the​​ fiber core, and preliminary​​​‌ signs of possible crosstalk​ between gratings. These results​‌ confirm the feasibility of​​ using embedded FBGs for​​​‌ ultrasonic sensing in castable​ materials. 21

A new​‌ adhesively bonded specimen configuration​​ was developed to allow​​ both creep testing and​​​‌ fracture-mechanics characterization within the‌ same geometry. The End-Loaded‌​‌ Split (ELS) test was​​ adapted for mode II​​​‌ fracture, while the creep‌ frame ensured a constant‌​‌ stress state in the​​ adhesive layer. Digital Image​​​‌ Correlation and optical fiber‌ sensors were used together‌​‌ to monitor crack propagation​​ and evaluate energy release,​​​‌ enabling more consistent assessment‌ of bonded joints before‌​‌ and after creep aging.​​ 44

Full-scale experimental pavement​​​‌ sections were instrumented to‌ study the response of‌​‌ surface layers under different​​ axle configurations and interface​​​‌ bonding conditions. Strain gauges‌ and embedded optical fibers‌​‌ placed at several depths​​ captured the influence of​​​‌ temperature, wheel interaction, and‌ debonding on strain fields.‌​‌ Tridem axles generated higher​​ strains than single axles,​​​‌ and interface debonding led‌ to tensile strains at‌​‌ the bottom of the​​ surface layer, contrasting with​​​‌ compressive strains in well-bonded‌ cases. Numerical simulations using‌​‌ Viscoroute 2.0 supported the​​ interpretation of these measurements.​​​‌ 18

Finally, the bond‌ behavior of thermoplastic GFRP‌​‌ rebars embedded in concrete​​ was examined through pull-out​​​‌ testing. The effects of‌ rebar diameter and surface‌​‌ geometry on force–displacement response​​ and average pull-out strength​​​‌ were analyzed, and some‌ specimens were equipped with‌​‌ optical fiber cables to​​ record strain distributions along​​​‌ the interface in real‌ time. Comparisons with thermoset‌​‌ GFRP and conventional steel​​ reinforcement provided further insight​​​‌ into differences in bond‌ mechanisms and durability potential.‌​‌ 45

7.2.1 Optimizing‌ bifurcations and singularities for‌​‌ performance enhancement and mitigation​​ of the adverse dynamics​​​‌ of nonlinear energy sinks‌

Participants: Adrien Mélot.‌​‌

This work introduces a​​ general computational framework for​​​‌ optimizing the performances and‌ mitigating the adverse dynamics‌​‌ of nonlinear energy sinks​​ (NESs) under harmonic external​​​‌ forcing. It is well‌ known that attaching small,‌​‌ essentially nonlinear, subsystems to​​ a linear host system​​​‌ can provide efficient passive‌ vibration mitigation. However, the‌​‌ introduced nonlinearity can induce​​ adverse dynamics, in the​​​‌ form of isolated response‌ curves, which are detrimental‌​‌ to the performances of​​ the system. Several multi-objective​​​‌ optimization problems are formulated,‌ which consist in minimizing‌​‌ objective functionals derived from​​ bifurcation and singularity theory​​​‌ in order to control‌ such phenomena and improve‌​‌ the performances of NESs.​​ The methodology is demonstrated​​​‌ on a two-degree-offreedom system‌ consisting of a linear‌​‌ oscillator coupled to a​​ nonlinear energy sink with​​​‌ cubic stiffness. However, its‌ fully computational nature makes‌​‌ it applicable to arbitrarily​​ complex NES configurations and​​​‌ lays the foundation for‌ extending the design optimization‌​‌ of NESs to full​​ finite element models. These​​​‌ results are compared to‌ those obtained with the‌​‌ inclusion of a nonlinear​​ damping term, which is​​​‌ a common way of‌ mitigating the issue of‌​‌ isolated response curves in​​ the literature. We report​​​‌ significant computational speedups compared‌ to existing methodologies for‌​‌ controlling isolated response curves​​ induced by nonlinear energy​​​‌ sinks. 30, 60‌

7.2.2 An efficient neural‌​‌ network-based surrogate model for​​ predicting static gear contact​​​‌ conditions

Participants: Adrien Mélot‌.

Gears are an‌​‌ essential component of numerous​​​‌ mechanical systems across a​ wide range of engineeringapplications.​‌ However, they may be​​ associated to high levels​​​‌ of radiated noise which​ can limit their use.Accurately​‌ predicting this noise is​​ of paramount importance for​​​‌ the design, optimization and​ health mon-itoring of gear​‌ transmissions. System identification is​​ therefore needed to reach​​​‌ a sufficiently high levelof​ accuracy. However, this usually​‌ comes at the cost​​ of high computational burden.​​​‌ Using traditionalmodeling assumptions, it​ is widely accepted that​‌ the radiated noise stems​​ from the dynamic response​​​‌ ofthe gears which is​ itself induced by the​‌ static transmission error (STE)​​ and time-varying mesh stiffness.These​​​‌ physical quantities are governed​ by the local contact​‌ conditions between the gear​​ teeth. An accu-rate computation​​​‌ of these physical quantities​ is therefore crucial. However,​‌ this is a difficult​​ problem asgear contact resolution​​​‌ is intrinsically nonlinear and​ multiscale. Even considering simplifying​‌ assump-tions, the computation of​​ this physical quantities entails​​​‌ a significant computational effort​ when coupledto optimization procedures.​‌ In this work, we​​ introduce an efficient neural​​​‌ network-based surrogate modelfor predicting​ static gear contact conditions​‌ in near real time​​ in order to facilitate​​​‌ the identification andoptimization of​ mechanical systems equipped with​‌ geared systems. 52

7.2.3​​ Computing the dynamic response​​​‌ of periodic waveguides with​ nonlinear boundaries using the​‌ Wave Finite Element Method​​

Participants: Vincent Mahé,​​​‌ Adrien Mélot, Christophe​ Droz.

Nonlinear effects​‌ are increasingly relevant in​​ modern mechanical systems due​​​‌ to lighter, slender structures​ and the desire to​‌ exploit nonlinearities for enhanced​​ performance. Accurately capturing these​​​‌ effects is challenging, as​ numerical models of periodic​‌ waveguides can involve millions​​ of degrees of freedom,​​​‌ and localized nonlinearities further​ complicate simulations. This work​‌ proposes an extension of​​ the Wave Finite Element​​​‌ Method (WFEM) that combines​ Floquet-Bloch theory with finite-element​‌ discretisation of complex unit​​ cells, enabling efficient computation​​​‌ of the dynamic response​ of periodic waveguides with​‌ nonlinear boundaries. Nonlinear forces​​ are treated via an​​​‌ alternating frequency-time procedure and​ higher harmonics are captured​‌ using the Harmonic Balance​​ Method, with the system​​​‌ solved through numerical continuation.​

The method reduces problem​‌ size while providing richer​​ physical insight than classical​​​‌ finite element approaches. Validation​ against standard FEM with​‌ Craig-Bampton reduction shows excellent​​ agreement and an 83%​​​‌ reduction in computational time.​ Application to a locally​‌ resonant metamaterial demonstrates that​​ nonlinear effects can shift​​​‌ band-edge resonances into bandgaps,​ producing high-amplitude, spatially localised​‌ vibrations not predicted by​​ linear theory. This approach​​​‌ offers a versatile and​ efficient framework for simulating​‌ complex metamaterials, civil engineering​​ structures, and other systems​​​‌ with nonlinear interfaces or​ singularities, supporting system identification,​‌ R&D cycles, and digital​​ twinning applications. 29,​​​‌ 50, 59

7.2.4​ Reduced Frequency-driven Bloch Wave​‌ Decomposition for Harmonic Analysis​​ of Finite Periodic Structures​​​‌

Participants: Christophe Droz,​ Alvaro Gavilan Rojas.​‌

A frequency-driven, reduced wave​​ finite element method is​​​‌ proposed for periodic structures​ which are solely defined​‌ by their dynamic stiffness​​ matrices. Using an S-S​​​‌${}^{-1}$ transform combined​ with an adaptive eigenvector​‌ sampling and a ω-driven​​ Bloch wave decomposition, we​​ achieve dispersion and harmonic​​​‌ analyses with enhanced accuracy‌ and efficiency. 36

7.2.5‌​‌ Controlling dispersion in lattice​​ waveguides using positive-stiffness-only non-local​​​‌ interactions

Participants: Lucas Rouhi‌, Christophe Droz.‌​‌

This study presents a​​ systematic approach to engineering​​​‌ the dispersion relation of‌ one-dimensional monoatomic lattices through‌​‌ the use of long-range​​ interactions constrained to strictly​​​‌ positive stiffness values. Departing‌ from classical cosine fitting‌​‌ techniques, the proposed framework​​ employs interpolation at prescribed​​​‌ frequency-wave number pairs, leading‌ to a linear system‌​‌ formulation. The stiffness coefficients​​ are then determined using​​​‌ non-negative least squares, ensuring‌ the physical admissibility of‌​‌ the design. This method​​ enables the realization of​​​‌ dispersion curves featuring nonstandard‌ characteristics, such as negative‌​‌ group velocity, roton-like stationary​​ points, or locally flat​​​‌ bands, without resorting to‌ non-physical parameters. The concept‌​‌ of admissibility domains is​​ introduced to characterize the​​​‌ feasible set of target‌ values under positivity constraints,‌​‌ and several numerical examples​​ illustrate the trade-offs between​​​‌ design flexibility and physical‌ realizability. The framework provides‌​‌ a versatile tool for​​ the inverse design of​​​‌ dispersion in non-local lattices,‌ with potential extensions to‌​‌ higher-dimensional and multi-physical systems.​​ 54

7.2.6 Adjustable surface​​​‌ tension independent of the‌ collision operator for pseudo-potential‌​‌ lattice Boltzmann methods

Participants:​​ Romain Noel.

In​​​‌ this work, we propose‌ an alternative surface tension‌​‌ adjustment approach in the​​ pseudo-potential lattice Boltzmann (LB)​​​‌ model, which is not‌ only decoupled from the‌​‌ density ratio but also​​ independent of the collision​​​‌ operator. This is achieved‌ by incorporating a generic‌​‌ source term obtained from​​ the difference between a​​​‌ modified moment equilibrium and‌ an original moment equilibrium‌​‌ distribution function in the​​ LB equation. The explicit​​​‌ form of the source‌ term is obtained through‌​‌ a third-order Chapman-Enskog analysis​​ of the LB equation,​​​‌ aiming to recover the‌ targeted governing equations from‌​‌ a modified Landau free​​ energy theory using a​​​‌ hybrid density-pseudo-potential form. The‌ source term can be‌​‌ easily and straightforwardly incorporated​​ into different widely used​​​‌ collision operators, such as‌ single relaxation time (SRT‌​‌ or LBGK), multiple relaxation​​ time (MRT) and entropic-MRT​​​‌ (KBC) operators. The proposed‌ method is validated by‌​‌ three benchmarks: (i) the​​ liquid-gas co-existence density, (ii)​​​‌ surface tension adjustment via‌ the static droplet case‌​‌ and (iii) droplet deformation​​ via oscillations. It is​​​‌ shown that thermodynamic consistency‌ is restored in a‌​‌ large range of temperature​​ ratio, and, moreover, a​​​‌ remarkable tunable surface tension‌ range of 140 times‌​‌ can be achieved. Benefiting​​ from the proposed surface​​​‌ tension adjustment method, we‌ have successfully modeled the‌​‌ droplet impact and splashing​​ dynamics with a Weber​​​‌ number up to 10‌ 500, achieving one order‌​‌ of magnitude higher than​​ LB simulations reported in​​​‌ the literature. 31

7.2.7‌ Topology Optimization of Isolated‌​‌ Response Curves in 3D​​ Geometrically-nonlinear Beam

Participants: Adrien​​​‌ Mélot.

Topology optimisation‌ is a powerful tool‌​‌ for designing efficient and​​ light structures. However, classical​​​‌ topology optimisation methods (SIMP,‌ LSF), which are gradient-based,‌​‌ are not adapted to​​ deal with nonlinear vibrations​​​‌ in the context of‌ geometrical nonlinearities as the‌​‌ simulation of such systems​​​‌ is computationally expensive, and​ the strong nonlinearbehaviour makes​‌ the objective function non-convex​​ with many local minima.​​​‌ The present work investigates​ the potential of using​‌ global optimisation methods to​​ topology optimise those structures.​​​‌ To provide more robust​ nonlinear features in the​‌ optimisation, the bifurcations are​​ directly tracked and optimised.​​​‌ The strategy is applied​ to a 3D finite​‌ element model of a​​ beam 41

7.3.1 Variance estimation​‌ of modal parameters from​​ the poly-reference least-squares complex​​​‌ frequency-domain algorithm

Participants: Mikkel​ Tandrup Steffensen, Michael​‌ Doehler.

Modal parameter​​ estimation from input/output data​​​‌ is a fundamental task​ in engineering. The poly-reference​‌ least-squares complex frequency-domain (pLSCF)​​ algorithm is a fast​​​‌ and robust method for​ this task, and is​‌ extensively used in research​​ and industry. As with​​​‌ any method using noisy​ measurement data, the modal​‌ parameter estimates are afflicted​​ with uncertainty. However, their​​​‌ uncertainty quantification has been​ incomplete, in particular for​‌ the case of real-valued​​ polynomial coefficients in the​​​‌ modelling of the frequency​ response functions (FRFs) in​‌ the pLSCF algorithm, and​​ no expressions have been​​​‌ available for the covariance​ of participation vectors and​‌ mode shapes that are​​ subsequently estimated with the​​​‌ least-squares frequency domain (LSFD)​ approach. This work closes​‌ these gaps. Uncertainty expressions​​ for the modal parameters,​​​‌ including participation vectors and​ mode shapes, are derived​‌ and presented. It is​​ shown how to estimate​​​‌ the covariance between different​ modal parameters, and a​‌ complete method is provided​​ for modal parameter covariance​​​‌ estimation from pLSCF. The​ method is propagating the​‌ uncertainty of FRFs through​​ the algorithm using first-order​​​‌ perturbation theory and the​ delta method. The method​‌ is validated via extensive​​ Monte-Carlo simulations and the​​​‌ applicability is illustrated using​ a laboratory experiment. 34​‌

7.3.2 New joint estimation​​ method for emissivity and​​​‌ temperature distribution based on​ a Kriged Marginalized Particle​‌ Filter : application to​​ simulated infrared thermal image​​​‌ sequences

Participants: Thibaud Toullier​, Jean Dumoulin,​‌ Laurent Mevel.

This​​ work addresses the challenge​​​‌ of simultaneously estimating temperature​ and emissivity for infrared​‌ thermography in natural environment,​​ aiming for near real-time​​​‌ performance. Existing methods, mainly​ in satellite observation field,​‌ rely on restrictive physical​​ assumptions unsuitable for ground-based​​​‌ application context (Structures and​ Infrastructures monitoring). Other generic​‌ methods are nonetheless computationally​​ intensive, making them impractical​​​‌ for real-time use. Our​ objective is to provide​‌ a method with effective​​ realtime calculation performance while​​​‌ still giving results comparable​ to those reference methods​‌ under the same hypotheses,​​ finally achieving both good​​​‌ accuracy and performance. The​ proposed method is based​‌ on a dynamical state-space​​ modeling for the temperature,​​​‌ where the state vector​ is assumed to be​‌ split into a dynamic​​ component for the temperature​​​‌ and a stationary component​ representing the emissivity. Then​‌ the dynamical component is​​ estimated by a Kalman​​​‌ filter approach, whereas the​ parameterized model and the​‌ emissivity component are estimated​​ through a particle filtering​​​‌ framework resulting in a​ bank of Kalman filters,​‌ also called marginalized particle​​ filter. A spatial assumption​​ of homogeneity for the​​​‌ temperature yields to the‌ addition of a Kriging‌​‌ step to the Marginalized​​ Particle Filter to overcome​​​‌ the ill-posed nature of‌ the problem and to‌​‌ compute the necessary physical​​ estimates in a reasonable​​​‌ amount of time while‌ providing fair results compared‌​‌ to reference methods from​​ the literature. A comparison​​​‌ with two state-of-the-art methods,‌ MCMC and CMA-ES, is‌​‌ presented. The results indicate​​ that the proposed method​​​‌ estimates the true value‌ within a maximum deviation‌​‌ of 3K, similar to​​ CMA-ES, while MCMC achieves​​​‌ a more accurate estimate‌ with a maximum deviation‌​‌ of 0.5K. How- ever,​​ the computational efficiency of​​​‌ the proposed method is‌ significantly improved, reducing the‌​‌ processing time by seven​​ orders of magnitude com-​​​‌ pared to MCMC and‌ three orders of magnitude‌​‌ compared to CMA-ES. This​​ remarkable efficiency highlights the​​​‌ method’s feasibility for real-time‌ monitoring of temperature and‌​‌ emissivity. 35

7.3.3 Subspace-based​​ wavenumber identification in periodic​​​‌ waveguides adapted to full-field‌ vibration measurements

Participants: Alvaro‌​‌ Gavilán Rojas, Qinghua​​ Zhang, Christophe Droz​​​‌.

Identifying wave propagation‌ properties in periodic media,‌​‌ such as composite or​​ architected materials, is critical​​​‌ for characterizing complex structures‌ experimentally. Subspace identification algorithms‌​‌ are commonly applied to​​ Operational Modal Analysis (OMA)​​​‌ data in the time‌ domain; here, the focus‌​‌ is on frequency-domain data​​ collected from successive periodic​​​‌ unit cells in 1D-periodic‌ waveguides. Instead of estimating‌​‌ modal parameters like natural​​ frequencies, the aim is​​​‌ to determine the real‌ and imaginary parts of‌​‌ the structural wavenumber, related​​ to wavelength and spatial​​​‌ decay, as well as‌ Bloch wave modes, which‌​‌ describe physical wave propagation​​ such as torsion and​​​‌ compression. Recent advances in‌ full-field vibration measurement techniques‌​‌ provide multiple measurement points​​ per unit cell, allowing​​​‌ statistical mitigation of the‌ limitations due to a‌​‌ small number of unit​​ cells. This study proposes​​​‌ a subspace identification framework‌ that leverages these dense‌​‌ measurements for improved wavenumber​​ estimation. The approach is​​​‌ illustrated both through numerical‌ simulations of a periodic‌​‌ beam and via experimental​​ validation using optical deflectometry​​​‌ on periodic structures. By‌ constructing a state-space representation‌​‌ derived from a wave-based​​ finite element model, the​​​‌ method enhances the accuracy‌ of wavenumber identification within‌​‌ each unit cell, offering​​ a practical tool for​​​‌ characterizing periodic or architected‌ materials. 46, 58‌​‌

7.3.4 Subspace System Identification​​ with Unknown Disturbance Rejection​​​‌

Participants: Qinghua Zhang.‌

System identification usually assumes‌​‌ that the considered system​​ is driven by known​​​‌ inputs and/or stationary random‌ noises. This work considers‌​‌ the case involving unknown​​ inputs, which are arbitrary​​​‌ disturbances. A typical example‌ is a mechanical structure‌​‌ naturally excited by wind,​​ which changes direction from​​​‌ time to time. To‌ address such disturbances, subspace‌​‌ methods for system identification​​ will incorporate techniques of​​​‌ unknown input observers, which‌ are state estimators with‌​‌ the ability to reject​​ arbitrary unknown disturbances, provided​​​‌ a mathematical model of‌ the system under consideration‌​‌ is available. Simulation results​​ are reported to illustrate​​​‌ the proposed method for‌ system identification while rejecting‌​‌ unknown disturbances. 57

7.4.1​ Physics-Informed Neural Networks for​‌ Structural Health Monitoring

Participants:​​ Nikhil Mahar, Laurent​​​‌ Mevel.

Structural Health​ Monitoring (SHM) is crucial​‌ for ensuring the safety​​ and durability of engineering​​​‌ structures. Physics-Informed Neural Networks​ (PINNs) provide a promising​‌ approach by combining data-driven​​ models with physical knowledge,​​​‌ bridging the gap between​ purely model-based and purely​‌ data-driven methods. The works​​ presented here explore complementary​​​‌ PINN-based strategies for SHM,​ focusing on inverse estimation,​‌ scalability, and robustness to​​ unknown inputs.

An attention-augmented​​​‌ Long Short-Term Memory network​ (Pi-Attn-LSTM) is introduced for​‌ inverse parameter estimation without​​ requiring complete state measurements.​​​‌ By integrating a temporal​ attention mechanism in an​‌ encoder-decoder setup, the network​​ adaptively focuses on critical​​​‌ features in sequential data,​ improving accuracy in dynamic​‌ environments. Validation on multi-degree-of-freedom​​ numerical simulations and a​​​‌ scaled aluminium frame demonstrates​ faster convergence and superior​‌ robustness compared to a​​ conventional Pi-LSTM, enabling reliable​​​‌ identification of localized structural​ degradation. 27

A parallel​‌ PINN framework addresses computational​​ efficiency and scalability by​​​‌ decomposing high-dimensional structural systems​ into coupled lower-dimensional subproblems,​‌ one per degree of​​ freedom, while maintaining global​​​‌ consistency through shared matrices.​ This approach supports efficient​‌ state and parameter estimation​​ for both linear and​​​‌ nonlinear systems under harmonic​ and real earthquake excitations.​‌ Experimental validation on a​​ scaled shear frame confirms​​​‌ robustness under sparse data,​ high noise, and varying​‌ system complexity. 26

An​​ input-robust LSTM (rPi-LSTM) integrates​​​‌ an output-injection strategy with​ physics-informed modeling to estimate​‌ both system states and​​ spatial health parameters in​​​‌ the presence of unknown​ or unmeasured input forces,​‌ such as wind or​​ variable loads. By preserving​​​‌ temporal dependencies while complying​ with system physics, the​‌ framework demonstrates strong robustness​​ to unknown inputs, noise,​​​‌ and data sparsity. Validation​ on numerical simulations and​‌ laboratory-scale experiments highlights its​​ practical potential for real-world​​​‌ SHM applications. 28

A​ complementary approach combines Stochastic​‌ System Identification (SSI) with​​ Physics-Informed Neural Networks (SSI-Pi-LSTM)​​​‌ for joint input-state-parameter estimation.​ SSI uses statistical and​‌ subspace-based techniques to estimate​​ state-space matrices and dominant​​​‌ modal parameters from structural​ response data. Incorporating these​‌ parameters into the PINN​​ framework reduces computation time​​​‌ and improves accuracy, enabling​ efficient and robust parameter​‌ identification for complex structural​​ systems. 49

7.4.2 Mitigating​​​‌ high dimensionality in damage​ identification for plate-like structures​‌ through substructuring with interacting​​ filtering-based approaches

Participants: Shereena​​​‌ OA, Laurent Mevel​.

High-dimensional plate-like structures,​‌ such as aircraft wings,​​ building floors, or wind​​​‌ turbine blades, require early​ detection of damage to​‌ prevent sudden catastrophic failures.​​ Traditional structural health monitoring​​​‌ methods rely on dense​ instrumentation and high-dimensional support​‌ models parameterized with damage​​ attributes, which can be​​​‌ computationally expensive and costly​ to implement. This work​‌ introduces a substructuring approach​​ combined with a robust​​​‌ Interacting Particle Kalman filter​ (IPEnKF) framework, enabling health​‌ estimation on manageable subdomains​​ independently of the rest​​​‌ of the structure. The​ process model incorporates output​‌ injection to achieve robustness​​ against unknown boundary forces​​​‌ on the substructures, while​ stage-wise monitoring reduces the​‌ required sensor coverage. Validation​​ was performed through numerical​​ simulations and experiments on​​​‌ a scaled trapezoidal Mindlin‌ plate representing the NASA‌​‌ CRM wing. The subdomain​​ approach reduced sensor requirements​​​‌ by 60% while maintaining‌ 95% accuracy in damage‌​‌ detection and assessment. Computational​​ costs were also significantly​​​‌ lowered, as each substructure‌ is monitored independently. The‌​‌ method demonstrated high reliability​​ with minimal false alarms,​​​‌ highlighting both the robustness‌ of the IPEnKF algorithm‌​‌ and the critical role​​ of strategic sensor placement​​​‌ in effective damage identification.‌ 32

7.4.3 Bayesian Filtering‌​‌ Approaches for SHM with​​ Sparse Instrumentation exploiting time-lagged​​​‌ measurements

Participants: Shereena OA‌, Laurent Mevel.‌​‌

Model-based strategies for Structural​​ Health Monitoring (SHM) often​​​‌ rely on comprehensive system‌ models and extensive instrumentation,‌​‌ which can be computationally​​ and financially demanding. Bayesian​​​‌ filtering techniques provide a‌ framework for estimating unobserved‌​‌ structural states and health​​ parameters from available measurements.​​​‌ However, sparse instrumentation can‌ limit observability, potentially leading‌​‌ to unrealistic or non-physical​​ estimates.

To address this​​​‌ challenge, a time-lagged virtual‌ sensor approach is introduced,‌​‌ leveraging delay-embedded measurement models​​ based on Taken’s theorem.​​​‌ By constructing virtual sensors‌ from time-delayed measurements, the‌​‌ dimensionality of the measurement​​ vector is enhanced, improving​​​‌ state observability. This method‌ is integrated within a‌​‌ Bayesian filtering framework using​​ an interacting particle-Kalman filter​​​‌ (IPKF), termed VS-IPKF. Unlike‌ conventional spatial virtual sensors,‌​‌ which rely on model-based​​ predictions at unmeasured locations,​​​‌ this approach uses actual‌ measurements to generate virtual‌​‌ data, improving fidelity and​​ reliability. Numerical validation on​​​‌ cantilever beam models and‌ experimental tests demonstrate significant‌​‌ improvements in state and​​ joint state-parameter estimation, especially​​​‌ under sparse instrumentation conditions.‌ 33

Building on this‌​‌ concept, the measurement model​​ is further enhanced for​​​‌ accurate mode shape reconstruction‌ by embedding time-lagged measurement‌​‌ layers. The Interacting Particle​​ Kalman Filter (IPKF) updates​​​‌ the model in the‌ time domain, allowing for‌​‌ refined system matrices and​​ more precise mode shapes.​​​‌ This method addresses challenges‌ such as non-collocated or‌​‌ insufficient instrumentation, data loss,​​ and dependence on Finite​​​‌ Element Method-based expansions. Numerical‌ experiments on a simply‌​‌ supported beam under ambient​​ vibration highlight the method’s​​​‌ improved accuracy and computational‌ efficiency compared to traditional‌​‌ approaches. 56

Extending these​​ ideas, a novel lagged​​​‌ estimation framework in the‌ modal domain combines time-lagged‌​‌ embeddings with reduced-order modeling​​ for computational efficiency. Structural​​​‌ dynamics are characterized by‌ mode shapes and natural‌​‌ frequencies modulated with location-specific​​ health variables, forming a​​​‌ simplified state-space model. Integrated‌ into a Bayesian filtering‌​‌ framework, this approach achieves​​ precise state estimation even​​​‌ for sparsely monitored or‌ complex time-varying systems. Observability‌​‌ analysis informs sensor allocation,​​ and tests on linear​​​‌ time-invariant systems show that‌ the method reduces sensor‌​‌ requirements while maintaining accuracy,​​ demonstrating its potential for​​​‌ advanced SHM and parameter‌ estimation. 53

7.4.4 Identification‌​‌ and monitoring of stochastic​​ linear subsystems with unknown​​​‌ local nonlinearities via output‌ injection

Participants: Neha Aswal‌​‌, Adrien Mélot,​​ Laurent Mevel, Qinghua​​​‌ Zhang.

Most civil‌ and mechanical structures exhibit‌​‌ nonlinear stochastic behaviour, which​​ is difficult to model​​​‌ accurately, but necessary for‌ conventional model-based structural health‌​‌ monitoring techniques. Although various​​​‌ methods have been developed​ to estimate nonlinear systems,​‌ they require information about​​ the external excitation and​​​‌ are susceptible to sensor​ noise and modelling inaccuracies.​‌ This knowledge is challenging​​ to acquire in practice.​​​‌ Hence, this work presents​ a novel nonlinearity model-agnostic​‌ approach to detect damage​​ in mechanical systems with​​​‌ localized nonlinearities. The proposed​ method utilises output injection​‌ to reject the unknown​​ nonlinearities as if they​​​‌ were unknown disturbances. By​ applying an existing disturbance​‌ rejection technique, the need​​ for a priori knowledge​​​‌ about the functional form​ of nonlinearities is avoided.​‌ Besides, a switching strategy​​ is employed to determine​​​‌ the most probable location​ of the nonlinearities, thereby​‌ eliminating the need for​​ a priori knowledge about​​​‌ their location. The method​ makes use of interacting​‌ particle Kalman filter, where​​ the particle filter estimates​​​‌ the parameters (health indices)​ in order to detect​‌ possible damage while the​​ Kalman filter simultaneously estimates​​​‌ the states. The efficiency​ of the proposed method​‌ is demonstrated with the​​ help of numerical experiments​​​‌ on a spring-mass-damper oscillator​ chain with attached localized​‌ nonlinearity. The proposed approach​​ is further validated against​​​‌ experimental data of jointed​ beams. 19

7.4.5 Damage​‌ detection and localization method​​ for wind turbine rotor​​​‌ based on Operational Modal​ Analysis and anisotropy tracking​‌

Participants: Ambroise Cadoret,​​ Laurent Mevel.

Subspace-based​​​‌ damage detection methods are​ widely used for civil​‌ engineering structures modeled as​​ linear time-invariant systems. For​​​‌ operating wind turbines modeled​ as linear time-periodic systems,​‌ these methods cannot be​​ theoretically used, due to​​​‌ the inherent assumptions associated​ with these methods in​‌ the context of linear​​ time-invariant systems. Based on​​​‌ a model approximation of​ time-periodic systems as time-invariant​‌ ones, these methods can​​ still be applied and​​​‌ adapted to perform change​ detection for time-periodic systems,​‌ through a Gaussian residual​​ built upon damage sensitive​​​‌ parameters coming from the​ identified modal parameters. The​‌ proposed method is tested​​ and validated on data​​​‌ simulated with an aero-servo-elastic​ model of an operating​‌ wind turbine, with the​​ detection of local stiffness​​​‌ reductions on a blade​ and the localization of​‌ the damaged blade. Furthermore,​​ two selections of sensors​​​‌ are tested, to evaluate​ the impact of the​‌ sensor choice on the​​ performance of the detection​​​‌ and localization methods. 20​

7.4.6 Model Updating of​‌ Rotating Wind Turbines Using​​ Operational Modal Analysis and​​​‌ Floquet Mode Decomposition

Participants:​ Nina Delette, Laurent​‌ Mevel.

The structural​​ complexity of modern wind​​​‌ turbines, combined with numerous​ uncertain or unknown parameters,​‌ presents significant challenges for​​ accurate predictive modeling. Model​​​‌ updating, which refines numerical​ model parameters using measurement​‌ data, offers a means​​ to mitigate these discrepancies.​​​‌ While extensively applied to​ stationary structures, its extension​‌ to rotating wind turbines​​ remains limited, as their​​​‌ time-periodic dynamics violate key​ assumptions underlying conventional methods.​‌ This study develops a​​ numerical framework for model​​​‌ updating of rotating wind​ turbines based on an​‌ equivalent Linear Time-Invariant (LTI)​​ approximation, derived through a​​​‌ Fourier decomposition of the​ system’s Floquet modes. A​‌ simplified 5 Degrees of​​ Freedom (DoF) turbine model​​ is employed to evaluate​​​‌ the effectiveness of a‌ deterministic model updating strategy‌​‌ leveraging this approximation. Synthetic​​ vibration data, generated from​​​‌ the model using a‌ predefined parameter set, serve‌​‌ as reference measurements for​​ assessing parameter recovery accuracy.​​​‌ Modal features extracted via‌ Operational Modal Analysis (OMA)‌​‌ are used to construct​​ the cost function that​​​‌ quantifies discrepancies between predicted‌ and observed modes. The‌​‌ results underscore the potential​​ of equivalent LTI representations​​​‌ in facilitating model updating‌ for rotating systems, as‌​‌ they effectively capture the​​ modal characteristics identified via​​​‌ OMA. This study establishes‌ a foundation for extending‌​‌ this methodology to more​​ complex, industrial-scale wind turbine​​​‌ models, provided that the‌ computational cost of model‌​‌ evaluation remains manageable. 39​​, 40

7.4.7 An​​​‌ indirect data-driven model-updating framework‌ to estimate soil–pile interaction‌​‌ parameters using output-only data​​

Participants: Michael Doehler.​​​‌

A data-driven model updating‌ framework is developed to‌​‌ estimate the operational parameters​​ of a laterally-impacted pile.​​​‌ The goal is to‌ facilitate the estimation of‌​‌ soil-pile interaction parameters such​​ as the mobilized mass​​​‌ and stiffness, as well‌ as geometrical data such‌​‌ as embedded pile length,​​ using output-only information. Accurate​​​‌ knowledge of mass, stiffness,‌ and pile embedded length‌​‌ is essential for understanding​​ foundation behavior when developing​​​‌ digital-twin models of structures‌ for the purpose of‌​‌ damage detection. The method​​ first employs subspace identification​​​‌ to determine modal parameters‌ and quantifies their uncertainties‌​‌ using output-only data. The​​ covariance matrix adaptation evolution​​​‌ strategy (CMA-ES), a stochastic‌ evolutionary algorithm, is subsequently‌​‌ used to update the​​ model. The effectiveness of​​​‌ the approach is demonstrated‌ through its application to‌​‌ numerical models in this​​ work, to quantify errors,​​​‌ and subsequently to data‌ from a documented full-scale‌​‌ field test of a​​ pile subjected to an​​​‌ impact load. The work‌ underscores the potential of‌​‌ statistical updating in advancing​​ the accuracy and reliability​​​‌ of soil-structure interaction parameter‌ estimation for systems where‌​‌ only output data might​​ exist. 23

7.4.8 Subspace-Based​​​‌ Noise Covariance Estimation for‌ Bayesian Filters in SHM‌​‌

Participants: Michael Doehler,​​ Neha Aswal, Laurent​​​‌ Mevel, Qinghua Zhang‌.

The performance of‌​‌ Kalman and Bayesian filters​​ in state and parameter​​​‌ estimation critically depends on‌ accurate knowledge of process‌​‌ and measurement noise covariances.​​ In practice, these covariances​​​‌ are often unknown and‌ heuristically tuned, which can‌​‌ be cumbersome and may​​ not yield optimal results.​​​‌ Optimization-based or matrix inversion‌ methods exist but are‌​‌ computationally demanding and potentially​​ numerically unstable.

A subspace-based​​​‌ identification approach provides an‌ efficient method to estimate‌​‌ the covariance of potentially​​ correlated process and measurement​​​‌ noises, particularly in virtual‌ sensing applications. Virtual sensors‌​‌ are used to reconstruct​​ system responses at unmeasured​​​‌ locations, enabling the monitoring‌ of structural states where‌​‌ physical instrumentation is limited.​​ The subspace-based method outperforms​​​‌ traditional autocovariance least-squares schemes‌ and provides reliable initial‌​‌ covariance estimates even in​​ the presence of model​​​‌ errors. Laboratory experiments demonstrate‌ that predictions at sensor‌​‌ locations not used in​​ the identification procedure closely​​​‌ match actual measurements, validating‌ both the covariance estimation‌​‌ and the virtual sensing​​​‌ framework.

Integrating the estimated​ covariances into Bayesian filtering​‌ strategies enhances structural health​​ monitoring (SHM) performance. Comparative​​​‌ studies show that using​ subspace-based covariance estimates improves​‌ the accuracy and efficiency​​ of state and damage​​​‌ estimation under conditions of​ sensor noise, modeling errors,​‌ and unmeasured inputs, highlighting​​ the value of combining​​​‌ virtual sensing with robust​ noise covariance identification. 22​‌, 37

SNCF: Hot​‌ boxes detection

Participants: Jean​​ Dumoulin, Thibaud Toullier​​​‌, Boualem Merainani.​

The main strategic issue​‌ is the maintenance in​​ operational condition of the​​​‌ Hot Box Detectors (DBC).​ The removal of the​‌ DBC from the track​​ is part of Tech4Rail's​​​‌ ambition: reducing equipment to​ the track. The innovation​‌ aimed at in this​​ project is to study​​​‌ and develop a measurement​ solution to be deployed​‌ at the edge of​​ a lane out of​​​‌ danger zone and independent​ of track equipment. Among​‌ the scientific obstacles identified​​ are the following three:​​​‌

• the behavior of the​ measurement system in deteriorated​‌ meteorological conditions in a​​ real site,
• the design​​​‌ and implementation of an​ automated prototype for in-situ​‌ deployment (connection to an​​ existing announcement system, hardware​​​‌ packaging of the system,​ study and design of​‌ a scalable software solution​​ allowing pre-processing data),
• the​​​‌ development of automatic processing​ tools for the analysis​‌ of massive data generated​​ by in-situ measurement systems.​​​‌

Siemens: Proof of concept​ monitoring coupled with prediction​‌ model for de-icing metro​​ lane surface

Participants: Jean​​​‌ Dumoulin, Thibaud Toullier​, Mathias Malandain.​‌

A proof of concept​​ study aims at combining​​​‌ real site monitoring solutions​ with adjoint state FE​‌ thermal model approach to​​ predict optimal heating required​​​‌ to preserve surface from​ icing in winter conditions.​‌ Furthermore, we introduced in​​ our prediction model connection​​​‌ with in-line weather forecast​ provided by Meteo France​‌ Geoservice at different time​​ horizons and spatial scales.​​​‌ Total amount: 124 k€.​

CETIM: fiber optic monitoring​‌

Participants: Xavier Chapeleau.​​

CETIM is conducting fatigue​​​‌ testing on a tank​ until it bursts, using​‌ existing fiber optic sensors​​ based on Bragg gratings​​​‌ for deformation measurement points.​ This expertise project aims​‌ to enhance this setup​​ with additional fiber optic​​​‌ sensors for distributed deformation​ measurements in a testing​‌ campaign, with the goal​​ to compare the effectiveness​​​‌ of these two monitoring​ technologies. Total amount: 20k€,​‌ until 2025.

Hottinger Brüel​​ & Kjær (HBK): uncertainty​​​‌ quantification for frequency-domain modal​ analysis

Participants: Michael Doehler​‌, Mikkel Steffensen.​​

In the context of​​​‌ the PhD of Mikkel​ Steffensen (DTU Denmark /​‌ HBK), a research collaboration​​ with HBK has started​​​‌ on developping methods for​ uncertainty quantification for input/output​‌ frequency-domain modal analysis. Mikkel​​ has spent one month​​​‌ at Inria in 2024​ for joint work on​‌ the subject. The developed​​ codes have been transferred​​​‌ to HBK's equipment software​ in 2025.

9.1.1 Inria associate​​​‌ team not involved in​ an IIL or an​‌ international program

9.1.2 Participation‌ in other International Programs‌​‌

9.2.1 Visits‌​‌ of international scientists

9.2.2 Visits to international​​ teams

9.3.1 Horizon Europe

ANR PRC SWEAT-City‌​‌

Participants: Romain Noel,​​ Jean Dumoulin.

• Duration:​​​‌ 2024 – 2028
• Budget:‌ 409 k€
• Title: Simulation‌​‌ of Water Evaporation within​​ Artificial ground for Thermo-regulation​​​‌ of the City
• Abstract:‌ The global warming and‌​‌ the more extreme events​​ related implies that cities​​​‌ will be concerned by‌ Urban Heat Island (UHI)‌​‌ effect more often and​​ more intensively. Pavements cover​​​‌ between 30% and 40%‌ of city areas and‌​‌ have a strong effect​​ on the UHI. Studies​​​‌ are showing that two‌ major phenomena can be‌​‌ used and then must​​ be studied to reduce​​​‌ the effect of pavements‌ on UHI: The albedo‌​‌ and the evaporation of​​ water. The increase in​​​‌ albedo has a beneficial‌ effect on the surface‌​‌ temperature of pavements but​​ increases radiation on the​​​‌ vertical surfaces of the‌ city. The present proposal‌​‌ focuses on the effect​​ of water evaporation on​​​‌ UHI. Research on that‌ topic is increasing in‌​‌ the recent years, and​​ only few papers are​​​‌ available on the numerical‌ simulation. However, the evaporation‌​‌ of porous media is​​ a complex phenomenon by​​​‌ its geometry, its interactions‌ between the matrix and‌​‌ fluids, the phase change​​ etc. This complexity leads​​​‌ to macroscopic models with‌ numerous parameters that are‌​‌ hard to obtain experimentally​​​‌ and to optimize. The​ aim of the project​‌ is to develop a​​ heat and mass numerical​​​‌ model that considers evaporation​ in a construction material.​‌ The model is based​​ on a multiscale approach​​​‌ combining the ability of​ Lattice Bolzman Method at​‌ the pore scale and​​ Finite Element at the​​​‌ macro scale. Experiments will​ be carried out at​‌ different scales to validate​​ the modelings. Finally experiments​​​‌ in simulated real life​ situation within the Sense​‌ City facility will be​​ performed and simulated in​​​‌ order to validate the​ models.

ANR JCJC Archi-Noise​‌

Participants: Christophe Droz.​​

• Duration: 2024 – 2028​​​‌
• Budget: 286 k€
• Title:​ Architected materials with meso-scale​‌ interactions for Noise and​​ vibration control
• Abstract: Noise,​​​‌ vibration, and harshness (NVH)​ impact multiple industries, affecting​‌ health, system longevity, and​​ sustainability. Despite advances in​​​‌ NVH mitigation materials, performance​ gains are plateauing due​‌ to constraints like cost,​​ compactness, adaptability, and structural​​​‌ integrity. ArchiNoise seeks to​ redefine material design by​‌ exploring architected meta-structures at​​ meso- and macro-scales, surpassing​​​‌ traditional vibro-acoustic limits. It​ will adapt nano-scale physics​‌ and electromagnetism concepts to​​ structural engineering, creating scattering​​​‌ effects akin to Bragg​ and locally resonant bandgaps,​‌ but independent of periodic​​ unit-cell dimensions or oscillator​​​‌ mass. These novel waveguiding​ phenomena will target broadband​‌ NVH control. ArchiNoise will​​ develop theoretical, computational, and​​​‌ phenomenological tools to design​ and optimize these materials.​‌ By integrating enriched continuum​​ theories, vibroacoustics, inverse wave-based​​​‌ identification, and lattice-based periodic​ modeling, it will pioneer​‌ lightweight NVH solutions.

ANR​​ France 2030 ExcellenceS City-FAB​​​‌ / CD 92

Participants:​ Jean Dumoulin, Thibaud​‌ Toullier, Mathias Malandain​​.

• Duration: 2024 –​​​‌ 2028
• Partners: CD 92,​ UGE laboratories
• Budget: 600​‌ k€, 80 k€ for​​ the team
• Title: Analysis​​​‌ of uses, and study​ of comfort and urban​‌ atmosphere on an avenue​​ scale
• Abstract: The objective​​​‌ is to anticipate and​ adapt road redevelopment projects​‌ by aiming at better​​ sharing of mobility spaces,​​​‌ making travel safer and​ enhancing the environment. These​‌ objectives meet the issues​​ of sustainable cities and​​​‌ territories. This project focuses​ on the environmental effects​‌ of developments, in particular​​ concerning thermal comfort, air​​​‌ quality and acoustic comfort.​ Our contribution to this​‌ project focuses on in-situ​​ monitoring and data-driven studies.​​​‌

ANR SCaNING

Participants: Vincent​ Le Cam.

• Duration:​‌ 2021 – 2025
• Partners:​​ UGE (Coordinator), Université de​​​‌ Toulouse, Aix-Marseille Université, Université​ de Bordeaux, Andra, EDF​‌
• Inria contact: Vincent Le​​ Cam
• Abstract: Using embedded​​​‌ sensors which will provide​ information similar to that​‌ used in NDE while​​ allowing to continuously evaluate​​​‌ performance indicators (compressive strength​ and Young’s modulus) and​‌ the concrete conditions (porosity​​ and water content) to​​​‌ improve indicator reliability and​ optimize diagnosis and communicating​‌ sensors through fully autonomous,​​ low-power networks makes it​​​‌ possible to consider systems​ with low installation and​‌ operation costs. The project​​ is lead by MAST​​​‌ LAMES laboratory of UGE.​ The instrumentation part is​‌ ensured by I4S.

ANR​​ Convinces

Participants: Jean Dumoulin​​​‌, Romain Noël.​

• Duration: 11/2021 – 10/2025​‌
• Partners: Univ. Lorraine (coordinator),​​ CERTES (UPEC), Univ. Strasbourg,​​ UGE, Cerema.
• Abstract: The​​​‌ ANR project CONVINCES is‌ investigating the influence of‌​‌ convection in suspensions of​​ micro-encapsulated phase change material​​​‌ (mPCM) in urban civil‌ engineering applications. This project‌​‌ will include LBM (Lattice​​ Boltzmann Method) and DEM​​​‌ (Discrete Element Method) in‌ multi-scale simulations plus series‌​‌ of experiments at different​​ scales to study the​​​‌ thermal impact of such‌ mPCM suspensions in porous‌​‌ media. The final objective​​ is the thermal regulation​​​‌ of pavements.

ANR RESBIOBAT‌

Participants: Jean Dumoulin.‌​‌

• Duration: 01/2022 – 12/2025​​
• Partners: UGE (coordinator), CERTES​​​‌ (UPEC), LNE, CSTB, Cerema,‌ Themacs Ingénierie.
• Abstract: The‌​‌ ANR project RESBIOBAT addresses​​ energy and environmental issues.​​​‌ Major advances are expected‌ in the building sector.‌​‌ Reliable in-situ thermal characterization​​ of buildings before and​​​‌ after a renovation action‌ are required. Moreover, construction‌​‌ must be more "sustainable",​​ notably by using bio-sourced​​​‌ materials and raw earth.‌ In this project, we‌​‌ propose an inter-disciplinary technical​​ solution combining modeling, simulations​​​‌ and measurements for a‌ better in-situ evaluation of‌​‌ the energy performances of​​ conventional and sustainable walls.​​​‌ The identification of the‌ thermal characteristics will be‌​‌ performed by an inverse​​ method combining a hygro-thermal​​​‌ model solved in real‌ time by a "reduced‌​‌ bases" technique and sensors​​ selected by "optimal experimental​​​‌ design". After a robustness‌ study via virtual tests,‌​‌ a prototype will be​​ realized and tested on​​​‌ real walls in laboratory‌ and in the Equipment‌​‌ of Excellence Sense-City.

PIA4:​​ MINERVE

Participants: Vincent Le​​​‌ Cam.

• Duration: 2022–2027‌
• 22 partners, coordinator: SNCF.‌​‌ Budget: 40 M€, 743​​ k€ for the team​​​‌
• Title: Méthodes et outils‌ pour la collaboration sectorielle‌​‌ et la continuité numérique​​ sur le cycle de​​​‌ vie (MINERVE)

• Abstract: The‌ six main objectives of‌​‌ the MINERVE project are:​​ - Develop design and​​​‌ construction methods and tools‌ using effective BIM approaches‌​‌ for each business -​​ Anticipate and optimize the​​​‌ construction phase, based on‌ sustainable BIM (digital continuity,‌​‌ frugality of models) -​​ Developing digital twins (exploring​​​‌ the potential of AI‌ for decision support), using‌​‌ opportunities with regard to​​ biodiversity and the environment​​​‌ - Use the digital‌ twin to improve resilience‌​‌ to climate change -​​ Develop an industrializable, standardized​​​‌ and shared vision of‌ interfaces ensuring digital continuity‌​‌ via the BIM model​​ on all phases -​​​‌ Build a collaborative ecosystem‌ around the modeling of‌​‌ linear and particularly railway​​ infrastructure

  The team participates​​​‌ with BIM and monitoring‌ of railway structures by‌​‌ modeling vibrations, defining original​​ ways of operational monitoring​​​‌ including fiber optic sensors.‌

PIA4: DIAM

Participants: Vincent‌​‌ Le Cam.

• Duration:​​ 2022–2026
• Partners: STIMIO (coordinator),​​​‌ SNIC, UGE. Budget: 3‌ M€, 693 k€ for‌​‌ the team.
• Abstract: In​​ this project, new ways​​​‌ to diagnose infrastructure deterioration‌ are identified through the‌​‌ use of innovative instrumentation​​ and by merging different​​​‌ data sources. With focus‌ on railway monitoring, the‌​‌ goal is online diagnosis​​ communication of critical trackside​​​‌ elements, and to enrich‌ trackside elements with augmented‌​‌ infrastructure monitoring systems. New​​ algorithms and models for​​​‌ predictive maintenance are developed.‌

ANR France2030 Sci-ty Geronimo‌​‌

Participants: Vincent Le Cam​​​‌, Arthur Bouche,​ Adji Toure.

• Duration:​‌ 10/2025-09/2027
• Abstract: This project​​ is a maturation project​​​‌ aimed at advancing Acoustic​ Emission technologies and associated​‌ know-how to a higher​​ TRL. The objective is​​​‌ to develop off-the-shelf, fully​ synchronized wireless sensor nodes​‌ capable of running Structural​​ Health Monitoring (SHM) algorithms​​​‌ in situ and in​ real time, such as​‌ Time Difference of Arrival​​ (TDOA) algorithms.

ANR France2030​​​‌ Sci-ty OBLiX

Participants: Romain​ Noel.

• Duration: 02/2025-01/2026​‌
• Abstract: The OBLiX pre-maturation​​ project aims at its​​​‌ extension and development. The​ main objective is to​‌ address the real need​​ for tools enabling structural​​​‌ health monitoring (SHM) for​ sustainable, resilient, and safe​‌ cities. Currently, the field​​ lacks generic, flexible, and​​​‌ efficient solutions for connecting​ sensors to physical and​‌ mathematical models. OBLiX, stands​​ out for its ability​​​‌ to combine genericity, flexibility,​ lightweight design, and efficiency.​‌ It offers an approach​​ better suited to SHM​​​‌ and embedded electronics. OBLiX's​ innovative nature lies in​‌ its combination of functionalities​​ and its ability to​​​‌ combine highly heterogeneous models.​ Its development is part​‌ of the National Acceleration​​ Strategy for Sustainable Cities​​​‌ and Innovative Buildings, thus​ contributing to the emergence​‌ of a sustainable construction​​ culture and the creation​​​‌ of a community of​ SHM experts.

ANR France2030​‌ Sci-ty TAPAS

Participants: Thibaud​​ Toullier, Arthur Bouche​​​‌.

• Duration: 05/2025-04/2026

• Abstract:​ The TAPAS software (Tracking,​‌ Acquisition, Processing, Archiving &​​ Storage) addresses the growing​​​‌ needs of instrumentation projects,​ which play a key​‌ role in the development​​ of tomorrow’s smart and​​​‌ connected cities. Designed to​ manage complex projects and​‌ supervise large-scale sensor networks,​​ the development of TAPAS​​​‌ ensures the collection, storage,​ visualization, archiving, and control​‌ of the generated data.​​

  A dedicated programming interface​​​‌ enables seamless integration into​ domain-specific applications, while an​‌ intuitive user interface allows​​ users to visualize and​​​‌ manage projects as well​ as control data sharing.​‌ TAPAS therefore supports structural​​ health monitoring and energy​​​‌ optimization of infrastructures, contributing​ to more sustainable, resilient,​‌ and innovative cities.

  Finally,​​ through the integration of​​​‌ computational models, TAPAS paves​ the way for the​‌ deployment and operation of​​ digital twins dedicated to​​​‌ the real-time monitoring, control,​ and optimization of infrastructures.​‌

CETIM

Participants: Michael Doehler​​, Xavier Chapeleau.​​​‌

• Duration: 09/2024–08/2025
• Partners: CETIM,​ I4S, UGE/MAST-SMC. Budget: 100​‌ k€
• Abstract: This research​​ collaboration funded by CETIM​​​‌ aims at developing a​ thesis project focused on​‌ data fusion and AI​​ using data from SHM​​​‌ sensors to create predictive​ models for fatigue damage​‌ (initiation and propagation of​​ cracks) in welded structures.​​​‌ This preliminary study focuses​ on evaluating SHM sensors​‌ and the processing and​​ fusion of data related​​​‌ to the considered use​ case.

CETIM

Participants: Michael​‌ Doehler, Christophe Droz​​.

• Duration: 10/2025–09/2028
• Partners:​​​‌ CETIM, I4S. Budget: 240​ k€
• Abstract: Following the​‌ previous research collaboration with​​ CETIM, a PhD project​​​‌ has been launched within​ the strategic partnership between​‌ Inria and CETIM. The​​ PhD of Hovanes Boksyan​​​‌ on Digital twins for​ monitoring welded mechanical components​‌ is funded within CETIM's​​ Digital Twin program.

AID​​

Participants: Christophe Droz,​​​‌ Michael Doehler.

• Duration:‌ 10/2025–09/2028
• Partners: DGA TT,‌​‌ Université Angers, I4S.
• Abstract:​​ This project aims to​​​‌ improve vibration analysis methods‌ used to assess the‌​‌ reliability and durability of​​ complex systems - particularly​​​‌ military vehicles and their‌ onboard equipment - under‌​‌ realistic operating conditions. Building​​ on the NF X50-144​​​‌ standard, the work seeks‌ to replace generic test‌​‌ procedures with more customized,​​ data-driven approaches using AI​​​‌ techniques to analyse vibration‌ profiles for fatigue prediction.‌​‌

CEA

Participants: Romain Noel​​.

• Partners: CEA/DM2S/STMF.
• Abstract:​​​‌ Within the Inria/CEA collaborative‌ framework, I4S and the‌​‌ LMSF started to work​​ together on CFD methods.​​​‌ This collaboration led to‌ a first M2 internship‌​‌ and the collaboration continues​​ through the PhD project​​​‌ of Clément Bardet (2024–2027)‌ on the use of‌​‌ thermo-chemical potential in LBM.​​

IFPEN

Participants: Laurent Mevel​​​‌.

Collaboration with IFPEN‌ leading to the thesis‌​‌ of A. Cadoret on​​ applying OMA techniques on​​​‌ wind turbines, and a‌ new PhD project has‌​‌ started with PhD candidate​​ N. Delette (2023–2026).

AEx​​​‌ - Fluidonics

Participants: Christophe‌ Droz, Romain Noel‌​‌.

• Duration: 05/2025–10/2028
• Abstract:​​ This project explores seismic​​​‌ barrier concepts relying on‌ fluid sloshing. The objective‌​‌ is to optimize the​​ internal flow turbulence as​​​‌ well as the tank's‌ structural design to combine‌​‌ dissipation phenomena with interference​​ of seismic waves. The​​​‌ M2 internship of Methmika‌ Athulkotte (05-10/2025) was dedicated‌​‌ to the project, and​​ PhD student Louis Ramseyer​​​‌ started in 11/2025.

ANR‌ ASTRID HYDRAVIB

Participants: Boris‌​‌ Lossouarn.

• Duration: 09/2023–08/2026​​
• Partners: Cnam (coordinator), Arts​​​‌ et Métiers Lille, École‌ navale
• Title: Piezoelectric damping‌​‌ for mitigation of flow-induced​​ vibrations
• Abstract: Unlike electromagnetic​​​‌ waves, underwater acoustic waves‌ propagate with very low‌​‌ attenuation and can be​​ measured more than a​​​‌ hundred kilometers away. Acoustic‌ noise comes from various‌​‌ sources including propeller cavitation,​​ hydroacoustic phenomena and vibration​​​‌ of structural components. The‌ HYDRAVIB project aims to‌​‌ reduce the vibration-induced noise​​ under hydrodynamic flow by​​​‌ proposing piezoelectric damping techniques‌ intended for the mitigation‌​‌ of structural resonances. These​​ techniques will also contribute​​​‌ to the increase in‌ the lifespan of the‌​‌ blades, hydrofoils or fins,​​ by reducing fatigue in​​​‌ a vibrating environment.

10.1.1 Scientific events:​​ organisation

10.1.2 Scientific events:​ selection

10.1.3 Journal

10.1.4 Invited talks

• Christophe​​​‌ Droz
  • "Modélisation des structures‌ périodiques: Applications à la‌​‌ non-localité, non-linéarité, l'identification et​​ la réduction de modèles".​​​‌ Institut Polytechnique de Paris,‌ UMA, POEMS, France, 11/12/2025.‌​‌
  • "Reduced frequency-driven Bloch Wave​​ Decomposition for Harmonic Analysis​​​‌ of Finite Periodic Structures".‌ 7th International Conference on‌​‌ Phononic Crystals/Metamaterials, Phonon Transport,​​ Topological Phononics, Seoul, Korea,​​​‌ 09/06/2025.
  • "Dynamics of Periodic‌ Structures: modeling, identification and‌​‌ applications". ISAE-Supmeca, VAST-FM, France,​​ 06/02/2025.
• Vincent Le Cam​​​‌
  • introduction keynote on SHM‌ development in France at‌​‌ the 8th SHM@COFREND day​​ in Grenoble, 19/03/2025
• Michael​​​‌ Doehler
  • “Statistical uncertainties and‌ change detectability in vibration-based‌​‌ structural monitoring”, invited seminar,​​ Aalborg University, Denmark, 11/02/2025​​​‌
• Jean Dumoulin
  • “Ultra Time‌ Domain Infrared Thermography in‌​‌ ground based outdoor monitoring:​​ outcomes and perspectives”, QIRT​​​‌ ASIA 2025, HARBIN, China,‌ 14-18 July 2025.
• Boualem‌​‌ Merainani
  • “Détection et suivi​​ de boites d’essieux par​​​‌ thermographie infrarouge ”, Journée‌ d’étude de la Société‌​‌ Française de Thermique (SFT)​​ sur la Thermographie Infrarouge,​​​‌ 16 octobre 2025, FIAP,‌ Paris.
• Thibaud Toullier
  • “DAM2‌​‌ - A Scalable and​​ Compliant Solution for Managing​​​‌ enriched Infrared images as‌ FAIR Research Data ”,‌​‌ Journée d’étude de la​​ Société Française de Thermique​​​‌ (SFT) sur la Thermographie‌ Infrarouge, 16 octobre 2025,‌​‌ FIAP, Paris.
• Adrien Mélot​​
  • "An Overview of Deep​​​‌ Learning Paradigms for Mechanical‌ Engineering", Lyon, France, 06/11/2025.‌​‌
• Romain Noel
  • "The LBM​​ use for fluid dynamics​​​‌ in granular media", invited‌ seminar at Université Jan‌​‌ Evangelista Purkyně, Czech Republic,​​ 10/2025
  • "Phase change for​​​‌ thermoregulation of porous media",‌ invited talk at the‌​‌ compagny Dotblock, 11/2025, France.​​

10.1.5 Leadership within the​​​‌ scientific community

• Vincent Le‌ Cam
  • co-chair of SHM@COFREND:‌​‌ this activity branch of​​ the COFREND (French Confederation​​​‌ for Non-destructive Testing) aims‌ at uniting the national‌​‌ SHM community from academia​​ and industry, and to​​​‌ promote and standardize the‌ SHM sector in France.‌​‌
  • member of the scientific​​ council of WEN (West​​​‌ Electronic Network) since 2014,‌ which is a cluster‌​‌ of about 200 companies,​​ academics and research laboratories​​​‌ active in electronics
• Michael‌ Doehler
  • co-leader of the‌​‌ working group “GT SHM​​ Data” within SHM@COFREND. The​​​‌ GT focuses on scientific‌ issues, technical challenges and‌​‌ standards of the SHM​​ sector related to handling​​​‌ and processing of structural‌ monitoring data. The GT‌​‌ involves around 50 people​​ from academia and industry​​​‌ in France.

10.1.6 Scientific‌ expertise

• Christophe Droz was‌​‌ expert evaluator for the​​ French National Research Agency​​​‌ (ANR) and for the‌ Campus Polytechnique Grants of‌​‌ Université Le Havre Normandie.​​​‌
• Adrien Mélot was scientific​ expert for the French​‌ National Research Agency (ANR)​​

10.1.7 Research administration

• Laurent​​​‌ Mevel
  • deputy head of​ science of Inria Rennes​‌
  • member of Commisision d'Evaluation​​ at Inria
  • member of​​​‌ the jury CESAAR at​ Ministère de la Transition​‌ Écologique et Solidaire
• Vincent​​ Le Cam
  • deputy co-head​​​‌ of COSYS department at​ Université Gustave Eiffel
• Jean​‌ Dumoulin
  • member of Commisision​​ d'Evaluation des chercheurs du​​​‌ Ministère de la Transition​ Ecologique (MTE)
• Xavier Chapeleau​‌
  • member of Commisision d'Evaluation​​ des chercheurs du Ministère​​​‌ de la Transition Ecologique​ (MTE)

10.2.1 Teaching

• Jean Dumoulin​
  • Licence Professionnelle TAM (Techniques​‌ Avancées en Maintenance): thermographie​​ infrarouge active, 30h, Université​​​‌ Paris-Est Créteil (UPEC), France​
  • Master 2 ITII, BTP,​‌ module Maintenance et réhabilitation​​ des ouvrages, Transferts thermiques​​​‌ dans les Structures :​ Des principes physiques à​‌ l'application sur site réel,​​ 12 h, Ecole Centrale​​​‌ de Nantes (ECN), France.​
• Vincent Le Cam
  • Master​‌ Electrical Engineering (GEII), 3h​​ CM in M1, 4h​​​‌ CM in M2 on​ electronic systems and Structural​‌ Monitoring, Université Bretagne Sud,​​ Lorient, France
  • M2 ENSIM​​​‌ Le Mans, 5h CM​ (monitoring des structures par​‌ capteurs sans fils)
  • EC​​ Nantes, 4h CM +​​​‌ 32h TP (electronique embarquée,​ Linux et drivers)
  • Ecole​‌ d'Ingénieur Builders, 10h CM,​​ Caen
• Xavier Chapeleau
  • M1​​​‌ ITI, fibre-optique, 8h, IUT​ Nantes
  • "Monitoring and auscultation​‌ : Optical fiber sensors",​​ Infrastar Training School, November​​​‌ 2025
• Romain Noel
  • École​ des Mines de Saint-Étienne,​‌ Master 2, Fluid Mechanics​​ Applications: Plenary conference (2h)​​​‌
  • École des Mines de​ Saint-Étienne, Master 1, Advanced​‌ Fluid Mecanics: Lectures (6h)​​ + practical lessons (6h)​​​‌
  • École Centrale de Nantes,​ Master 2, Data Signal​‌ and Image: Lectures (4h)​​ + practical lessons (4h)​​​‌
  • École Polytech' Nantes, Master​ 1, Probabilty and Statistics:​‌ Lectures (12h) + practical​​ lessons (3h)
• Christophe Droz​​​‌
  • MSc 1, Modélisation en​ Action, Dpt. Mathématiques, Université​‌ de Rennes (50h)
  • MSc​​ 2, Contrôle non destructif​​​‌ et identification, Dpt. Sciences​ pour la matière, Université​‌ de Rennes (20h)
• Alvaro​​ Gavilan Rojas
  • Master 2,​​​‌ Mécanique et Matériaux, Contrôle​ non destructif et identification,​‌ 1.5h CM + 5h​​ TD, Université de Rennes​​​‌
• Lucas Rouhi
  • L1 Biologie,​ Environnement et Chimie du​‌ Vivant (BECV), 57h TD​​ en mathématiques, 6h TP​​​‌ initiation Python, Université de​ Rennes
• Nicolas Madinier
  • STP03​‌ MECA, mécanique du solide,​​ 22h TD, INSA Rennes​​​‌
• Boris Lossouarn
  • From september​ 2025 : International Master​‌ (M2), Smart structures -​​ Piezoelectric shunt damping, Cnam,​​​‌ Paris (15h)

10.2.2 Supervision​

10.2.3 Juries‌

• Michael Doehler
  • External reviewer‌​‌ PhD Brandon O'Connell, "Novel​​ Probabilistic and Bayesian Approaches​​​‌ to Uncertainty Quantification for‌ Operational Modal Analysis". Sheffield‌​‌ University, UK, 02/2025.
  • External​​ reviewer PhD Armin Hermes,​​​‌ "High fidelity aeroelastic stability‌ analysis of complex blades‌​‌ in 3D flow". Technical​​ University of Denmark, Denmark,​​​‌ 03/2025.
• Christophe Droz
  • PhD‌ external reviewer F. Qu‌​‌ "Frequency dependent and parametric​​ reduced order models for​​​‌ NVH simulation of metamaterial‌ structures", KU Leuven, Belgium,‌​‌ 12/2025.
  • CSI member of​​ PhD candidate K. Mouffok​​​‌ "Optimization and Management of‌ Event Logging in Distributed‌​‌ Embedded Networks", INSA Rennes.​​
  • CSI member of PhD​​​‌ candidate N. Klaimi "Sparse‌ model-based deep learning for‌​‌ massive MIMO". IETR Rennes.​​
• Laurent Mevel
  • PhD Reviewer​​​‌ - Université de Lille.‌ ELIE ROUPHAEL, "Towards Stochastic‌​‌ Realization Theory for Linear​​ Switched Models". Defense October​​​‌ 24 2025.
• Chapeleau Xavier‌
  • CSI member of PhD‌​‌ candidate: Bzeih Rayan, Development​​ of methods for analyzing​​​‌ fiber optic measurement data‌ to determine deformation and‌​‌ damage to underground concrete​​ structures., Nantes Université.
• Romain​​​‌ Noel
  • CSI member of‌ PhD candidate: Khac Minh‌​‌ Tam Truong, Optimization of​​ Phylogenetic Compression Algorithms, Inria​​​‌ Rennes.
  • CSI member of‌ PhD candidate: Mahmoud Assaf,‌​‌ Numerical modeling and optimization​​ of two-phase flows with​​​‌ change of state, Polytech'Nantes.‌
  • CSI member of PhD‌​‌ candidate Badr Eddine Hamaid,​​ Decarbonizing cities on the​​​‌ climate change context: application‌ to the tertiary building,‌​‌ Univ. Eiffel.

10.2.4 Educational​​ and pedagogical outreach

• Christophe​​​‌ Droz was involved in‌ Chiche!: a scientific mediation‌​‌ program promoting scientific careers​​ to high-school students (Lycée​​​‌ Sévigné).

10.3.1‌ Specific official responsibilities in‌​‌ science outreach structures

• Christophe​​ Droz is co-organizator of​​​‌ the Sci-Rennes seminar series‌ at the Inria center‌​‌ of the University of​​ Rennes (since Sep. 2022).​​​‌

10.3.2 Participation in Live‌ events

• Boris Lossouarn actively‌​‌ contributed to the first​​ Carnot ARTS "Tech Show"​​​‌ on September 30, 2025‌ by presenting a vibration‌​‌ damping demonstrator for hydrofoils,​​ developed within the SMARTFOIL​​​‌ project (2019–2022, co-funded by‌ Carnot ARTS). The proposed‌​‌ experimental setup integrates piezoelectric​​ material to mitigate flow-induced​​​‌ vibrations. This live presentation‌ illustrated the scientific foundations‌​‌ and technological bricks enabling​​ next-generation smart structures for​​​‌ vibration analysis and control.‌

10.3.3 Others science outreach‌​‌ relevant activities

• Vincent Le​​ Cam , Jean Dumoulin​​​‌ , Vincent Baltazart ,‌ Romain Noel , Xavier‌​‌ Chapeleau , Thibaud Toullier​​ , Arthur Bouché have​​​‌ participated in the Journées‌ Portes Ouvertes on the‌​‌ UGE campus in Nantes,​​​‌ with several demonstrators.
• Vincent​ Le Cam organizes the​‌ stay of high school​​ students for internships on​​​‌ the UGE campus in​ Nantes.

International journals

• 18 article​​M.Marina Al Bacha​​​‌, M.-L.Mai-Lan Nguyen‌, P.Pierre Hornych‌​‌, O.Olivier Chupin​​, J.Juliette Blanc​​​‌ and X.Xavier Chapeleau‌. Full-scale experimental and‌​‌ numerical analysis of pavement​​ surface layers responses to​​​‌ different loading conditions.‌Road Materials and Pavement‌​‌ DesignNovember 2025HAL​​DOIback to text​​​‌
• 19 articleN.Neha‌ Aswal, A.Adrien‌​‌ Mélot, L.Laurent​​ Mevel and Q.Qinghua​​​‌ Zhang. Identification and‌ monitoring of stochastic linear‌​‌ subsystems with unknown local​​ nonlinearities via output injection​​​‌.Mechanical Systems and‌ Signal Processing238September‌​‌ 2025, 113239HAL​​DOIback to text​​​‌
• 20 articleA.Ambroise‌ Cadoret, E.Enora‌​‌ Denimal-Goy, J.-M.Jean-Marc​​ Leroy, J.-L.Jean-Lou​​​‌ Pfister and L.Laurent‌ Mevel. Damage detection‌​‌ and localization method for​​ wind turbine rotor based​​​‌ on Operational Modal Analysis‌ and anisotropy tracking.‌​‌Mechanical Systems and Signal​​ Processing224February 2025​​​‌, 111982HALDOI‌back to text
• 21‌​‌ articleN.Nicolas Derrien​​, M.Maximilien Lehujeur​​​‌, X.Xavier Chapeleau‌, O.Olivier Durand‌​‌, A.Antoine Gallet​​, N.Nicolas Roussel​​​‌, B.Béatrice Yven‌ and O.Odile Abraham‌​‌. Challenges of Embedding​​ Fiber Bragg Grating Sensors​​​‌ in Castable Material :‌ Influence of Material Shrinkage‌​‌ and Fiber Coatings on​​ Ultrasonic Measurements.Sensors​​​‌259April 2025‌, 2657HALDOI‌​‌back to text
• 22​​​‌ articleS.Szymon Greś​, M.Michael Döhler​‌, V.Vasilis Dertimanis​​ and E.Eleni Chatzi​​​‌. Subspace-based noise covariance​ estimation for Kalman filter​‌ in virtual sensing applications​​.Mechanical Systems and​​​‌ Signal Processing222January​ 2025, 111772HAL​‌DOIback to text​​
• 23 articleA.Andreas​​​‌ Ioakim, S.Szymon​ Greś, M.Michael​‌ Döhler, L. J.​​Luke J Prendergast and​​​‌ E.Eleni Chatzi.​ An indirect data-driven model-updating​‌ framework to estimate soil–pile​​ interaction parameters using output-only​​​‌ data.Engineering Structures​328April 2025,​‌ 119699HALDOIback​​ to text
• 24 article​​​‌M.Mira Kabbara,​ X.Xavier Chapeleau,​‌ Q.Qinghua Zhang and​​ F.Frédéric Bourquin.​​​‌ Strain Transfer Model Assessment​ for Steel-Reinforced and Polymer-Coated​‌ Optical Fiber Cables Used​​ as Distributed Strain Sensors​​​‌.IEEE Sensors Letters​98August 2025​‌, 1-4HALDOI​​back to text
• 25​​​‌ articleZ.Zhilei Luo​, B.Boualem Merainani​‌, V.Vincent Baltazart​​, Q.Qinghua Zhang​​​‌ and M.Michael Döhler​. Subpixel motion estimation​‌ for video-based target-free vibration​​ monitoring under complex environmental​​​‌ conditions.Mechanical Systems​ and Signal Processing226​‌March 2025, 112342​​HALDOIback to​​​‌ text
• 26 articleN.​Nikhil Mahar, S.​‌Subhamoy Sen and L.​​Laurent Mevel. A​​​‌ parallel framework of physics-informed​ neural networks for model​‌ identification of linear and​​ nonlinear systems.Structures​​​‌79September 2025,​ 109454HALDOIback​‌ to text
• 27 article​​N.Nikhil Mahar,​​​‌ S.Subhamoy Sen and​ L.Laurent Mevel.​‌ An Attention-Augmented Long Short-Term​​ Memory Network for PINN-based​​​‌ Structural Health Monitoring.​Engineering Structures340October​‌ 2025, 120673HAL​​DOIback to text​​​‌
• 28 articleN.Nikhil​ Mahar, S.Subhamoy​‌ Sen and L.Laurent​​ Mevel. Physics informed​​​‌ Long Short-Term Memory neural​ network for dual state-parameter​‌ estimation of linear dynamical​​ systems robust to input​​​‌ forces.Mechanical Systems​ and Signal Processing236​‌August 2025, 113012​​HALDOIback to​​​‌ text
• 29 articleV.​Vincent Mahé, A.​‌Adrien Mélot, B.​​Benjamin Chouvion and C.​​​‌Christophe Droz. Computing​ the dynamic response of​‌ periodic waveguides with nonlinear​​ boundaries using the Wave​​​‌ Finite Element Method.​Computers & StructuresApril​‌ 2025HALDOIback​​ to text
• 30 article​​​‌A.Adrien Mélot.​ Optimizing bifurcations and singularities​‌ for performance enhancement and​​ mitigation of the adverse​​​‌ dynamics of nonlinear energy​ sinks.Nonlinear Dynamics​‌August 2025HALDOI​​back to text
• 31​​​‌ articleR.Romain Noël​, F.Feifei Qin​‌, L.Linlin Fei​​ and J.Jan Carmeliet​​​‌. Adjustable surface tension​ independent of the collision​‌ operator for pseudo-potential lattice​​ Boltzmann methods.Physical​​​‌ Review Fluids1010​October 2025, 104901​‌HALDOIback to​​ text
• 32 articleO.​​​‌O.A. Shereena, E.​Eshwar Kuncham, P.​‌ C.Prakash Chand Jain​​, L.Laurent Mevel​​​‌ and S.Subhamoy Sen​. Mitigating high dimensionality​‌ in damage identification for​​ plate-like structures through substructuring​​ with interacting filtering-based approaches​​​‌.Engineering Structures323‌AJanuary 2025,‌​‌ 119190HALDOIback​​ to text
• 33 article​​​‌O.O.A. Shereena,‌ S.Subhamoy Sen and‌​‌ L.Laurent Mevel.​​ Monitoring of sparsely observed​​​‌ structures by time-lagged Bayesian‌ smoothing.Mechanical Systems‌​‌ and Signal Processing241​​December 2025, 113493​​​‌HALDOIback to‌ text
• 34 articleM.‌​‌ T.Mikkel Tandrup Steffensen​​, M.Michael Döhler​​​‌, D.Dmitri Tcherniak‌ and J. J.Jon‌​‌ Juel Thomsen. Variance​​ estimation of modal parameters​​​‌ from the poly-reference least-squares‌ complex frequency-domain algorithm.‌​‌Mechanical Systems and Signal​​ Processing223January 2025​​​‌, 111905HALDOI‌back to text
• 35‌​‌ articleT.Thibaud Toullier​​, J.Jean Dumoulin​​​‌ and L.Laurent Mevel‌. New joint estimation‌​‌ method for emissivity and​​ temperature distribution based on​​​‌ a Kriged Marginalized Particle‌ Filter : application to‌​‌ simulated infrared thermal image​​ sequences.Science of​​​‌ Remote Sensing11June‌ 2025, 100209HAL‌​‌DOIback to text​​

Invited conferences

• 36 inproceedings​​​‌C.Christophe Droz and‌ A. C.Alvaro C.‌​‌ Gavilan-Rojas. Reduced Frequency-driven​​ Bloch Wave Decomposition for​​​‌ Harmonic Analysis of Finite‌ Periodic Structures.PHONONICS‌​‌ 2025: 7th International Conference​​ on Phononic Crystals/Metamaterials/Metasurfaces, Phonon​​​‌ Transport, and Topological Phononics‌Phononics 2025 - 7th‌​‌ International Conference on Phononic​​ Crystals/Metamaterials/Metasurfaces, Phonon Transport, and​​​‌ Topological PhononicsSeoul, South‌ KoreaJune 2025,‌​‌ 58-59HALback to​​ text

International peer-reviewed conferences​​​‌

• 37 inproceedingsN.Neha‌ Aswal, S.Szymon‌​‌ Greś, L.Laurent​​ Mevel and Q.Qinghua​​​‌ Zhang. Improving Bayesian‌ filters for structural health‌​‌ monitoring applications with subspace-based​​ noise covariance estimates.​​​‌IOMAC 2025 proceedingsIOMAC‌ 2025 - 11th International‌​‌ Operational Modal Analysis Conference​​Rennes, France2025,​​​‌ 1-8HALback to‌ text
• 38 inproceedingsR.‌​‌Rune Brincker, C.​​Carmelo Gentile, M.​​​‌Manuel Aenlle, J.‌Jörg Bienert, C.‌​‌Carlo Rainieri and M.​​Michael Döhler. 20​​​‌ Years of IOMAC.‌IOMACS 2025 - 11th‌​‌ International Operational Modal Analysis​​ ConferenceRennes, FranceMay​​​‌ 2025HAL
• 39 inproceedings‌N.Nina Delette,‌​‌ E.Enora Denimal Goy​​, J.-L.Jean-Lou Pfister​​​‌, R.Reda El‌ Amri and L.Laurent‌​‌ Mevel. Model Updating​​ of Rotating Wind Turbines​​​‌ Using Operational Modal Analysis‌ and Floquet Mode Decomposition‌​‌.IOMAC 2025 -​​ 11th International Operational Modal​​​‌ Analysis ConferenceRennes, France‌May 2025, 1-7‌​‌HALback to text​​
• 40 inproceedingsN.Nina​​​‌ Delette, J.-L.Jean-Lou‌ Pfister, E.Enora‌​‌ Denimal Goy, M.​​ R.Mohamed Reda El​​​‌ Amri and L.Laurent‌ Mevel. Bayesian model‌​‌ updating of rotating wind​​ turbines.WESC 2025​​​‌ - Wind Energy Science‌ ConferenceNantes, FranceJune‌​‌ 2025, 1-3HAL​​back to text
• 41​​​‌ inproceedingsE.Enora Denimal‌ Goy, Y.Yichang‌​‌ Shen, S.Samuel​​ Fruchard, A.Adrien​​​‌ Mélot and L.Ludovic‌ Renson. Topology Optimization‌​‌ of Isolated Response Curves​​ in 3D Geometrically-nonlinear Beam​​​‌.Nonlinear Structures &‌ Systems, Vol. 1 -‌​‌ Proceedings of the 43rd​​​‌ IMAC, A Conference and​ Exposition on Structural Dynamics​‌ 2025IMAC 2025 -​​ 43rd Conference and Exposition​​​‌ on Structural DynamicsOrlando,​ United StatesRiver Publishers​‌August 2025, 81​​ - 92HALDOI​​​‌back to text
• 42​ inproceedingsA.Alexandra Duminil​‌, J.-P.Jean-Philippe Tarel​​ and J.Jean Dumoulin​​​‌. Ozone Concentration Estimation​ from Infrared Images Using​‌ Extinction Coefficient.Lecture​​ Notes in Computer Science​​​‌CAIP 2025 - 21st​ International Conference on Computer​‌ Analysis of Images and​​ PatternsLNCS-15622Lecture Notes​​​‌ in Computer ScienceLas​ Palmas de Gran Canaria,​‌ SpainSpringer Nature Switzerland​​September 2026, 292-303​​​‌HALDOIback to​ text
• 43 inproceedingsJ.​‌Jean Dumoulin, D.​​Domenico Vizzari, L.​​​‌ C.Lucas Czamanski Meireles​, T.Thibaud Toullier​‌, J.-L.Jean-Luc Manceau​​, E.Eric Gennesseaux​​​‌, S.Stephane Lavaud​, E.Emmanuel Chailleux​‌ and T.Thierry Sedran​​. Outdoor Hybrid Solar​​​‌ Road Demonstrator Monitoring Using​ Infrared Thermography with Embedded​‌ Local Probes for Energy​​ Harvesting Performance Evaluation.​​​‌AITA 2025AITA 2025​ - 18th International Workshop​‌ on Advanced Infrared Technology​​ and ApplicationsProceedingsKobe,​​​‌ JapanMDPISeptember 2025​, 37HALDOI​‌back to text
• 44​​ inproceedingsA.Arij Fawaz​​​‌, E.Emilie Lepretre​, X.Xavier Chapeleau​‌ and S.Sylvain Chataigner​​. Evaluation of a​​​‌ new adhesively bonded sample​ configuration adapted for ELS​‌ test and creep frame​​ setups.CICE 2025​​​‌ - 12th International Conference​ on FRP Composites in​‌ Civil EngineeringLisbon, Portugal​​2025HALback to​​​‌ text
• 45 inproceedingsM.​Maha Fodda, S.​‌Sylvain Chataigner, L.​​Ludwig Battais, X.​​​‌Xavier Chapeleau, M.​Marc Quiertant, A.​‌Arnaud Rolland and K.​​Karim Benzarti. Experimental​​​‌ study on the bond​ behaviour between thermoplastic GFRP​‌ rebars and concrete.​​JNC 2025 - Journées​​​‌ Nationales sur les Composites​Gif-sur-Yvette, France2025,​‌ 1-6HALback to​​ text
• 46 inproceedingsA.​​​‌Alvaro Gavilán Rojas,​ Q.Qinghua Zhang,​‌ O.Olivier Robin and​​ C.Christophe Droz.​​​‌ Subspace-based wavenumber identification in​ periodic waveguides adapted to​‌ full-field vibration measurements.​​IOMAC 2025 - 11th​​​‌ International Operational Modal Analysis​ ConferenceRennes, France2025​‌, 1-7HALback​​ to text
• 47 inproceedings​​​‌M.Mira Kabbara,​ X.Xavier Chapeleau,​‌ Q.Qinghua Zhang and​​ F.Frédéric Bourquin.​​​‌ Post-processing of fiber optic​ sensors data with consideration​‌ of strain transfer for​​ distributed strain measurements.​​​‌IOMAC 2025 proceedingsIOMAC​ 2025 - 11th International​‌ Operational Modal Analysis Conference​​Rennes, France2025,​​​‌ 1-8HALback to​ text
• 48 inproceedingsV.​‌Vincent Le Cam,​​ A.Arthur Bouché,​​​‌ L.Laurent Lemarchand,​ A.Adji Toure,​‌ A.Antoine Barre and​​ L.Lucas Doula.​​​‌ PEGASE4 : a generic​ board for embedded SHM​‌ applications.IWSHM Proceedings​​ 2025,IWSHM 2025 -​​​‌ International Workshop on Structural​ Health MonitoringIWSHM 2025​‌ ProceedingsPalo Alto (Stanford),​​ United StatesDestech Publications,​​​‌ Inc.September 2025,​ 534-542HALback to​‌ text
• 49 inproceedingsN.​​Nikhil Mahar, N.​​Nikesh Yadav, G.​​​‌Gajendra Yadav, S.‌Subhamoy Sen and L.‌​‌Laurent Mevel. Physics-aware​​ neural network integrated with​​​‌ stochastic subspace identification for‌ quantification of physical changes‌​‌.IOMAC 2025 proceedings​​IOMAC 2025 - 11th​​​‌ International Operational Modal Analysis‌ ConferenceRennes, France2025‌​‌, 1-8HALback​​ to text
• 50 inproceedings​​​‌V.Vincent Mahé,‌ A.Adrien Mélot,‌​‌ B.Benjamin Chouvion and​​ C.Christophe Droz.​​​‌ Wave-based modelling of arbitrarily‌ complex periodic waveguides with‌​‌ nonlinear boundaries.IOMAC​​ 2025 proceedingsIOMAC 2025​​​‌ - 11th International Operational‌ Modal Analysis ConferenceRennes,‌​‌ France2025, 1-8​​HALback to text​​​‌
• 51 inproceedingsB.Boualem‌ Merainani, T.Thibaud‌​‌ Toullier and J.Jean​​ Dumoulin. Comparative study​​​‌ of image quality acquired‌ on a new portable‌​‌ dynamic laboratory test bench​​ using two thermal time​​​‌ constant uncooled IRFPA microbolometric‌ camera.QIRT Asia‌​‌ 2025 - 5th Quantitative​​ InfraRed Thermography Conference Asia​​​‌Harbin, China2025HAL‌back to text
• 52‌​‌ inproceedingsV.Valentin Mouton​​, A.Adrien Mélot​​​‌, E.Emmanuel Rigaud‌ and J.Joel Perret-Liaudet‌​‌. An efficient neural​​ network-based surrogate model for​​​‌ predicting static gear contact‌ conditions.IOMAC 2025‌​‌ proceedingsIOMAC 2025 -​​ 11th International Operational Modal​​​‌ Analysis ConferenceRennes, France‌2025HALback to‌​‌ text
• 53 inproceedingsS.​​Shereena O A,​​​‌ S.Subhamoy Sen and‌ L.Laurent Mevel.‌​‌ A novel lagged estimation​​ framework for sparsely observed​​​‌ systems supplemented with virtual‌ measurements sampled in delayed‌​‌ time.IOMAC 2025​​ - 11th International Operational​​​‌ Modal Analysis ConferenceRennes,‌ France2025, 1-8‌​‌HALback to text​​
• 54 inproceedingsL.Lucas​​​‌ Rouhi and C.Christophe‌ Droz. Controlling dispersion‌​‌ in lattice waveguides using​​ positive-stiffness-only non-local interactions.​​​‌International Conference on Wave‌ Mechanics and VibrationsLisbonne,‌​‌ PortugalSeptember 2025HAL​​back to textback​​​‌ to text
• 55 inproceedings‌F.Francesco Soldovieri,‌​‌ V.Vincenzo Cuomo and​​ J.Jean Dumoulin.​​​‌ Monitoring for  sustainable and‌ inclusive urban areas.‌​‌EGU 2025 - European​​ Geosciences UnionVienne, Austria​​​‌November 2025HALDOI‌back to text
• 56‌​‌ inproceedingsA.Ananay Thakur​​, S.Shereena O​​​‌ A, S.Subhamoy‌ Sen and L.Laurent‌​‌ Mevel. Compensating Low​​ Instrumentation for Mode Shape​​​‌ Estimation in Bayesian Filtering‌ with Time-Delayed Data Embedding‌​‌.IOMAC 2025 proceedings​​IOMAC 2025 - 11th​​​‌ International Operational Modal Analysis‌ ConferenceRennes, France2025‌​‌, 1-8HALback​​ to text
• 57 inproceedings​​​‌Q.Qinghua Zhang.‌ Subspace System Identification with‌​‌ Unknown Disturbance Rejection.​​IOMAC 2025 proceedingsIOMAC​​​‌ 2025 - 11th International‌ Operational Modal Analysis Conference‌​‌Rennes, France2025,​​ 1-8HALback to​​​‌ text

Conferences without proceedings‌

• 58 inproceedingsA.Alvaro‌​‌ Gavilán-Rojas, Q.Qinghua​​ Zhang, O.Olivier​​​‌ Robin and C.C‌ Droz. Déflectométrie et‌​‌ théorie des structures périodiques​​ pour l'identification de nombres​​​‌ d'onde.CFM 2025‌ - 26ème Congrès Français‌​‌ de MécaniqueMetz, France​​August 2025HALback​​​‌ to text
• 59 inproceedings‌V.Vincent Mahé,‌​‌ A.Adrien Mélot,​​​‌ B.Benjamin Chouvion and​ C.Christophe Droz.​‌ Wave-based reduction of finite​​ phononic structures with nonlinear​​​‌ boundary conditions.NODYCON​ 2025 - 4th International​‌ Nonlinear Dynamics ConferenceHoboken,​​ NJ, United StatesJune​​​‌ 2025, 1-1HAL​back to text
• 60​‌ inproceedingsA.Adrien Mélot​​. Optimizing Bifurcations and​​​‌ Isolated Response Curves to​ Enhance the Performances of​‌ Nonlinear Energy Sinks.​​NODYCON 2025 - 4th​​​‌ International Nonlinear Dynamics Conference​Hoboken, NJ, United States​‌June 2025, 1-1​​HALback to text​​​‌

Edition (books, proceedings, special​ issue of a journal)​‌

• 61 proceedingsProceedings of​​ the 11th International Operational​​​‌ Modal Analysis Conference (IOMAC​ 2025).IOMAC 2025​‌ - 11th International Operational​​ Modal Analysis ConferenceRennes,​​​‌ France2025HAL

Doctoral​ dissertations and habilitation theses​‌

• 62 thesisC.Clément​​ Rigal. Multi-scale numerical​​​‌ study of a microencapsulated​ phase change materials laden​‌ flow in a porous​​ medium.Université de​​​‌ StrasbourgJuly 2025HAL​

Other scientific publications

• 63​‌ inproceedingsJ.Jean Dumoulin​​, T.Thibaud Toullier​​​‌, N.Nathanael Gey​ and M.Mathias Malandain​‌. DAM2 -Data, Model​​ and Monitoring A Scalable​​​‌ and Compliant Solution for​ Managing enriched Infrared images​‌ as FAIR Research Data​​.EGU 2025 -​​​‌ European Geosciences UnionVienna,​ AustriaMarch 2025HAL​‌DOI