Mr. Emmanuel Ramasso Profile

Emmanuel Ramasso

at FEMTO-ST

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 9 May 2024 Presentation + Paper

Physics-informed neural networks for inverse problems in structural dynamics

Rafael de O. Teloli, Mael Bigot, Lucas Coelho, Emmanuel Ramasso, Roberta Tittarelli, Patrice Le Moal, Morvan Ouisse

Proceedings Volume 12950, 129500E (2024) https://doi.org/10.1117/12.3010918

KEYWORDS: Neural networks, Data modeling, Inverse problems, Structural analysis, Elastic modulus, Modeling, Elasticity

Read Abstract +

This study introduces an innovative approach that employs Physics-Informed Neural Networks (PINNs) to address inverse problems in structural analysis. Specifically, we apply this technique to the 4-th order PDE of Euler-Bernoulli formulation to approximate beam displacement and identify structural parameters, including damping and elastic modulus. Our methodology incorporates partial differential equations (PDEs) into the neural network’s loss function during training, ensuring it adheres to physics-based constraints. This approach simplifies complex structural analysis, even when specific boundary conditions are unavailable. Importantly, our model reliably captures structural behavior without resorting to synthetic noise in data. This study represents a pioneering effort in utilizing PINNs for inverse problems in structural analysis, offering potential inspiration for other fields. The reliable characterization of damping, a typically challenging task, underscores the versatility of methodology. The strategy was initially assessed through numerical simulations utilizing data from a finite element solver and subsequently applied to experimental datasets. The presented methodology successfully identifies structural parameters using experimental data and validates its accuracy against reference data. This work opens new possibilities in engineering problem-solving, positioning Physics-Informed Neural Networks as valuable tools in addressing practical challenges in structural analysis.

Proceedings Article | 27 March 2019 Presentation + Paper

CMUT sensors based on circular membranes array for SHM applications

P. Butaud, G. Bourbon, P. Le Moal, E. Joseph, B. Verdin, E. Ramasso, V. Placet

Proceedings Volume 10970, 1097027 (2019) https://doi.org/10.1117/12.2515324

KEYWORDS: Sensors, Structural health monitoring, Transducers, Continuous wavelet transforms, Microelectromechanical systems, Acoustic emission

Read Abstract +

A MEMS sensor dedicated to SHM applications is presented. The MEMS is made of a Capacitive Micromachined Ultrasonic Transducer (CMUT) chip composed of circular membranes array. The radius of the membranes varies between 50 μm and 250 μm and hence the associated resonance frequencies between 80 kHz and 2 MHz. A wide frequency bandwidth is then available for acoustic measurements. A testing campaign is conducted in order to characterize the MEMS sensor's behavior when subjected to single-frequency and broadband excitation stimuli. The single-frequency excitations are produced with specific piezoelectric transducers from 300 kHz to 800 kHz. The Fast Fourier Transform (FFT) of the measured signal from the CMUT is centered as expected on the excitation frequency. The broadband excitation is obtained with a pencil lead break. In this case, the FFT of the measured signal is centered on the resonance frequency of the membrane. These characterizations point out the DC bias voltage applied to the CMUT as a major parameter for controlling the sensitivity of the sensor. The CMUT sensor proves to be sufficiently sensitive to monitor these sources. This work highlights the relevant prospective capacities of the CMUT sensor to collect data in structural health monitoring applications. This sensor technology could be externally deployed, or even integrated into a composite structure, in order to monitor the structure by the CMUT detection, either by active ultrasound tests or by passive acoustic emission.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years