A physics-informed neural networks inversion method for in-situ consolidation coefficient based on piezocone penetration test pore pressure data

LI Lin; ZUO Lin-long; HU Tao-tao; SONG Bo-kai

doi:10.16285/j.rsm.2024.0842

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

A physics-informed neural networks inversion method for in-situ consolidation coefficient based on piezocone penetration test pore pressure data

更新时间：2026-06-25

- A physics-informed neural networks inversion method for in-situ consolidation coefficient based on piezocone penetration test pore pressure data
- Vol. 45, Issue 10, Pages: 2889-2899(2024)
- 作者机构：
  
  1. ．长安大学公路学院,陕西,西安,710064
  2. ．长安大学西安市绿色智慧交通岩土工程重点实验室,陕西,西安,710061
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2024.0842
  CLC： TU41
- Online First：09 October 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
LI Lin, ZUO Lin-long, HU Tao-tao, et al. A physics-informed neural networks inversion method for in-situ consolidation coefficient based on piezocone penetration test pore pressure data[J]. 2024, 45(10): 2889-2899.
DOI：

LI Lin, ZUO Lin-long, HU Tao-tao, et al. A physics-informed neural networks inversion method for in-situ consolidation coefficient based on piezocone penetration test pore pressure data[J]. 2024, 45(10): 2889-2899. DOI： 10.16285/j.rsm.2024.0842.

摘要

固结系数是软基沉降计算和稳定性分析的关键参数，现有固结系数原位测试方法存在耗时长且精度低的缺点。根据孔压静力触探试验（piezocone penetration test，简称CPTU）贯入机制与锥肩超孔隙水压力消散模式，采用圆孔扩张理论和轴对称固结方程描述CPTU锥肩超孔隙水压力的形成、发展和消散过程，利用神经网络自动微分功能将轴对称固结方程嵌入深度神经网络，通过物理方程损失函数、边界条件损失函数和初始条件损失函数形成神经网络的物理信息约束，同时将CPTU孔压测试数据作为数据驱动项，以最小化超孔隙水压力损失函数为优化目标，建立了CPTU孔压测试数据反演场地原位固结系数的物理信息神经网络（physics-informed neural networks，简称PINNs）模型。通过已有离心模型试验数据反演验证了PINNs模型反演场地原位固结系数的有效性，并利用CPTU孔压测试数据分析了PINNs模型反演原位固结系数的鲁棒性。结果表明：提出的PINNs模型能够有效利用CPTU孔压测试数据快速准确地反演场地原位固结系数；由于模型融入了物理机制约束，所需训练数据量少，且对有噪声的孔压测试数据具有较强的鲁棒性和泛化性能，为准确、快速可靠测试场地原位固结系数提供了有效途径。

Abstract

The consolidation coefficient is a crucial parameter for settlement calculation and stability analysis of soft foundations. Existing in-situ testing methods for the consolidation coefficient have the disadvantages of time-consuming and low accuracy. Based on the penetration mechanism of piezocone penetration test (CPTU) and the dissipation pattern of excess pore water pressure at the cone shoulder

the formation

development

and dissipation processes of excess pore water pressure at the CPTU cone shoulder are described using the theory of circular cavity expansion and the axisymmetric consolidation equation. By incorporating the automatic differentiation capability of neural networks

the axisymmetric consolidation equation is embedded into a deep neural network. The physical information constraints of the neural network are formed through the loss functions of physical equations

boundary conditions

and initial conditions. At the same time

the CPTU pore pressure test data serve as a data-driven term. Consequently

with the minimization of the excess pore water pressure loss function as the optimization goal

a physics-informed neural networks (PINNs) model is established for inversely analyzing the in-situ consolidation coefficient using CPTU pore pressure test data. The effectiveness of the PINNs model in inversely analyzing in-situ consolidation coefficient is verified through example analysis and inversion validation using existing centrifuge test data. The robustness of the PINNs model is also analyzed using CPTU pore pressure test data. The results indicate that the proposed PINNs model can effectively use CPTU pore pressure test data to rapidly and accurately invert the site in-situ consolidation coefficient. Due to the integration of physical mechanism constraints

the model requires only a small amount of training data and exhibits strong robustness and generalization performance against noisy pore pressure test data

providing an effective approach for accurate

rapid

and reliable testing of the in-situ consolidation coefficient.

关键词

Keywords

references

Views

238

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Coupling mechanism of seepage and heat transfer in rock fracture based on physics-informed neural networks

Research progress and technological prospects for in-situ mechanical characterization of lunar soil

Prediction model for surface subsidence and parameters inversion in valley bottom area

Inversion of rock mass mechanical parameter in underground powerhouse of Baishan pumped storage station

Integrated inversion of instantaneous and rheological parameters and deformation prediction of high rockfill dam

Related Author

LI Shao-Jun2

SHEN Lin-fang1

XIAO Zhi-huan1

WANG Zhi-liang1

CHU Fu-lin

HAN Zong-fang

LI Yu-qiong

LI Na

Related Institution

Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology

State Key Laboratory of Geomechanics and Geotechnical Engineering Safety, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences

Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences

Geographic Information and Tourism College, Chuzhou University

AI问答

⁰