摘要
滆湖组黏性土是苏锡常地区工业与民用建筑常使用的地基持力层,也是地下空间开发常涉及的地层之一。为了对该层的工程地质数据进行分析研究,本文挑选出126个该地区的地质钻孔,包含446组滆湖组黏性土样品数据,并将其分为400组训练集数据和46组预测集数据。通过神经网络算法,建立了该层物理性质参数与力学性质参数之间的关联模型,然后将预测集数据输入模型对模型可靠性进行验证。结果表明:(1)模型预测结果与实测数据之间的相关系数基本大于0.8,为极强相关,优于多元线性回归法拟合结果,说明该模型具有一定的可靠度;(2)土体物理性质参数与力学性质参数之间存在关联性,使用神经网络算法能够较好地表达多参数之间的关联性。神经网络算法实现了工程地质大数据使用价值更深层次挖掘,建模方法简单,模型可靠度较高,能够很好地应用于工程地质数据的分析研究当中。
Cohesive soil in Gehu formation is the foundation bearing layer most used in the Suzhou–Wuxi–Changzhou area,and it’s also one of the most commonly used stratum in underground space development.We selected 126 geological boreholes to research this stratum using 446 groups of sample data(400 groups of training data and 46 groups of prediction data).We used a neural network algorithm to establish a relationship model between physical parameters and mechanical parameters.We then input 46 groups of prediction data into the model to verify its reliability and also used specific data to research the relationship between physical and mechanical parameters.The results show that:(1)The Pearson correlation coefficient between the prediction results and measured data is more than 0.8,better than the fitting results of multiple linear regression.The model is verified as reliable.(2)There is a certain correlation between physical and mechanical parameters,and the neural network algorithm can express correlation models between multiple parameters.The neural network algorithm allows the understanding of the high value in the utilization of engineering geological big data.The method is simple and reliable,and it can be applied in the research and analysis of data.
作者
张其琪
顾春生
许书刚
瞿婧晶
唐鑫
ZHANG Qiqi;GU Chunsheng;XU Shugang;QU Jingjing;TANG Xin(Geological Survey of Jiangsu Province,Jiangsu Nanjing 210018,China;Key Laboratory of Earth Fissures Geological Disaster,Ministry of Natural and Resources of China,Jiangsu Nanjing 210018,China)
出处
《上海国土资源》
2025年第2期80-86,共7页
Shanghai Land & Resources
基金
中国地质调查局与江苏省自然资源厅合作项目“苏南现代化建设示范区综合地质调查”(苏财建[2016]140号)
江苏省矿地融合试点项目“基于多源数据融合的苏州地下空间资源综合开发利用评价试点”(苏财资环[2020]53号)
江苏省地质勘查项目“扬子江城市群典型城镇城市地质安全风险调查评价”(苏财资环[2022]27号)。
关键词
工程地质
黏性土
滆湖组
神经网络
数据分析
engineering geological
cohesive soil
Gehu formation
neural network
data analysis
