Abstract
Predicting stratified ground consolidation effectively remains a challenge in geotechnical engineering, especially when it comes to quickly and dependably determining the coefficient of consolidation ((Formula presented.)) for each soil layer. This difficulty primarily stems from the time-intensive nature of the consolidation process and the challenges in efficiently simulating this process in laboratory settings and using numerical methods. Nevertheless, the consolidation of stratified ground is crucial because it governs ground settlement, affecting the safety and serviceability of structures situated on or in such ground. In this study, an innovative method utilizing a physics-informed neural network (PINN) is introduced to predict stratified ground consolidation, relying solely on short-term excess pore water pressure (PWP) data collected by monitoring sensors. The proposed PINN framework identifies (Formula presented.) from the limited PWP data set and subsequently utilizes the identified (Formula presented.) to predict the long-term consolidation process of stratified ground. The efficacy of the method is demonstrated through its application to a case study involving two-layer ground consolidation, with comparisons made to an existing PINN method and a laboratory consolidation test. The results of the case study demonstrate the applicability of the proposed PINN method to both forward and inverse consolidation problems. Specifically, the method accurately predicts the long-term dissipation of excess PWP when (Formula presented.) is known (i.e., the forward problem). It successfully identifies the unknown (Formula presented.) with only 0.05-year monitoring data comprising 10 data points and predicts the dissipation of excess PWP at 1-year, 10-year, 15-year, and even up to 30-year intervals using the identified (Formula presented.) (i.e., the inverse problem). Moreover, the investigation into optimal PWP monitoring sensor layouts reveals that installing sensors in areas with significant variations in excess PWP enhances the prediction accuracy of the proposed PINN method. The results underscore the potential of leveraging PINNs in conjunction with PWP monitoring sensors to effectively predict stratified ground consolidation.
Recommended Citation
W. Gong et al., "Prediction of Stratified Ground Consolidation Via a Physics-informed Neural Network Utilizing Short-term Excess Pore Water Pressure Monitoring Data," Computer-Aided Civil and Infrastructure Engineering, Wiley, Jan 2024.
The definitive version is available at https://doi.org/10.1111/mice.13326
Department(s)
Geosciences and Geological and Petroleum Engineering
Publication Status
Open Access
International Standard Serial Number (ISSN)
1467-8667; 1093-9687
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Wiley, All rights reserved.
Publication Date
01 Jan 2024
Comments
Fundamental Research Funds for the Central Universities, Grant 52108297