岩土力学2025，Vol.46Issue(2)：563-572,10.DOI:10.16285/j.rsm.2024.0500
深圳土工参数数据库及基于生成对抗网络的多元参数分布预测模型研究
Shenzhen geotechnical parameter database and multivariate parameter distribution prediction model based on generative adversarial network
摘要
Abstract
Inspired by big data,fully utilizing geotechnical data for precise characterization and modeling of geotechnical parameters is critical for the digitalization of geotechnical engineering.This study collected geotechnical investigation reports from 75 engineering projects in Shenzhen,established a database containing 8 geotechnical parameters of clay and weathered residual soil(SZ-SOIL/8/11369),and thoroughly analyzed the distribution characteristics of geotechnical parameter data in Shenzhen.Subsequently,a model for predicting geotechnical parameters was developed using this database and a generative adversarial network(GAN).The proposed method was applied to a project in Shenzhen,successfully predicting mechanical parameters from known physical parameters and accurately forecasting the geotechnical parameter distribution of the project site using small samples.The results indicate that the proposed method can make reasonable predictions for samples with missing parameters,achieving the goal of reducing the uncertainty in geotechnical parameters at local engineering sites through extensive regional survey data.This provides parameter assurance for the resilience design and risk assessment of geotechnical and underground engineering structures in Shenzhen.关键词
土工参数分布/数据库/预测/生成对抗网络Key words
distribution of geotechnical parameters/database/prediction/generative adversarial network分类
建筑与水利引用本文复制引用
潘秋景,孙广灿,蔡永敏,苏栋,李凤伟..深圳土工参数数据库及基于生成对抗网络的多元参数分布预测模型研究[J].岩土力学,2025,46(2):563-572,10.基金项目
国家自然科学基金(No.52108388,No.52378424) （No.52108388,No.52378424）
国家重点研发计划(No.2023YFC3009300) （No.2023YFC3009300）
湖南省科技创新计划(No.2021RC3015) （No.2021RC3015）
深圳大学2035 追求卓越研究计划(No.2022B007). This work was supported by the National Natural Science Foundation of China(52108388,52378424),the National Key Research and Development Program of China(2023YFC3009300),the Science and Technology Innovation Program of Hunan Province(2021RC3015)and the Shenzhen University 2035 Pursuit of Excellence Research Program(2022B007). （No.2022B007）
