Next Article in Journal
Lightweight Anonymous Authentication and Key Agreement Protocol Based on CoAP of Internet of Things
Previous Article in Journal
Super-Resolution Procedure for Target Responses in KOMPSAT-5 Images
 
 
Article

Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures

Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Raffaella Di Sante
Sensors 2022, 22(19), 7190; https://doi.org/10.3390/s22197190
Received: 18 August 2022 / Revised: 7 September 2022 / Accepted: 8 September 2022 / Published: 22 September 2022
Concrete exhibits time-dependent long-term behavior driven by creep and shrinkage. These rheological effects are difficult to predict due to their stochastic nature and dependence on loading history. Existing empirical models used to predict rheological effects are fitted to databases composed largely of laboratory tests of limited time span and that do not capture differential rheological effects. A numerical model is typically required for application of empirical constitutive models to real structures. Notwithstanding this, the optimal parameters for the laboratory databases are not necessarily ideal for a specific structure. Data-driven approaches using structural health monitoring data have shown promise towards accurate prediction of long-term time-dependent behavior in concrete structures, but current approaches require different model parameters for each sensor and do not leverage geometry and loading. In this work, a physics-informed data-driven approach for long-term prediction of 2D normal strain field in prestressed concrete structures is introduced. The method employs a simplified analytical model of the structure, a data-driven model for prediction of the temperature field, and embedding of neural networks into rheological time-functions. In contrast to previous approaches, the model is trained on multiple sensors at once and enables the estimation of the strain evolution at any point of interest in the longitudinal section of the structure, capturing differential rheological effects. View Full-Text
Keywords: predictive modeling; creep and shrinkage; structural health monitoring; long-term structural behavior; physics-informed machine learning; optical fibers; fiber bragg grating predictive modeling; creep and shrinkage; structural health monitoring; long-term structural behavior; physics-informed machine learning; optical fibers; fiber bragg grating
Show Figures

Figure 1

MDPI and ACS Style

Pereira, M.; Glisic, B. Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures. Sensors 2022, 22, 7190. https://doi.org/10.3390/s22197190

AMA Style

Pereira M, Glisic B. Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures. Sensors. 2022; 22(19):7190. https://doi.org/10.3390/s22197190

Chicago/Turabian Style

Pereira, Mauricio, and Branko Glisic. 2022. "Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures" Sensors 22, no. 19: 7190. https://doi.org/10.3390/s22197190

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop