{"title":"Inverse analysis for estimating geotechnical parameters using physics-informed neural networks","authors":"Shinichi Ito , Ryusei Fukunaga , Kazunari Sako","doi":"10.1016/j.sandf.2024.101533","DOIUrl":null,"url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) have been proposed for incorporating physical laws into deep learning. PINNs can output solutions that satisfy physical laws by introducing information, such as partial differential equations (PDEs), boundary conditions, and initial conditions, into the loss functions used during the construction of the neural network model. This study presents two cases in which geotechnical parameters were estimated through an inverse analysis of PINNs. PINNs were applied to simulate consolidation and unsaturated seepage processes. The inverse analysis of the PINNs helped estimate the coefficient of consolidation and the parameters related to the unsaturated soil hydraulic properties with sufficient accuracy. The inverse analysis of PINNs for geotechnical parameter estimation was found to be an effective approach that utilizes measurement data.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"64 6","pages":"Article 101533"},"PeriodicalIF":3.3000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soils and Foundations","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038080624001112","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Physics-informed neural networks (PINNs) have been proposed for incorporating physical laws into deep learning. PINNs can output solutions that satisfy physical laws by introducing information, such as partial differential equations (PDEs), boundary conditions, and initial conditions, into the loss functions used during the construction of the neural network model. This study presents two cases in which geotechnical parameters were estimated through an inverse analysis of PINNs. PINNs were applied to simulate consolidation and unsaturated seepage processes. The inverse analysis of the PINNs helped estimate the coefficient of consolidation and the parameters related to the unsaturated soil hydraulic properties with sufficient accuracy. The inverse analysis of PINNs for geotechnical parameter estimation was found to be an effective approach that utilizes measurement data.
期刊介绍:
Soils and Foundations is one of the leading journals in the field of soil mechanics and geotechnical engineering. It is the official journal of the Japanese Geotechnical Society (JGS)., The journal publishes a variety of original research paper, technical reports, technical notes, as well as the state-of-the-art reports upon invitation by the Editor, in the fields of soil and rock mechanics, geotechnical engineering, and environmental geotechnics. Since the publication of Volume 1, No.1 issue in June 1960, Soils and Foundations will celebrate the 60th anniversary in the year of 2020.
Soils and Foundations welcomes theoretical as well as practical work associated with the aforementioned field(s). Case studies that describe the original and interdisciplinary work applicable to geotechnical engineering are particularly encouraged. Discussions to each of the published articles are also welcomed in order to provide an avenue in which opinions of peers may be fed back or exchanged. In providing latest expertise on a specific topic, one issue out of six per year on average was allocated to include selected papers from the International Symposia which were held in Japan as well as overseas.