Improving Shallow Foundation Settlement Prediction through Intelligent Optimization Techniques  

作　　者：Hadi Fattahi Hossein Ghaedi Danial Jahed Armaghani 

机构地区：[1]Faculty of Earth Sciences Engineering,Arak University of Technology,Arak,38135-1177,Iran [2]School of Civil and Environmental Engineering,University of Technology Sydney,Sydney,NSW 2007,Australia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期747-766,共20页工程与科学中的计算机建模(英文)

摘　　要：In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(such as analytical,numerical,and regression)is challenging and sometimes unattainable.This is primarily due to the inherent nonlinearity of the model,the intricate nature of geotechnical materials,the complex interaction between soil and foundation,and the inherent uncertainty in soil parameters.Therefore,thesemethods often introduce assumptions and simplifications,resulting in relationships that deviate from the actual problem’s reality.In addition,many of these methods demand significant investments of time and resources but neglect to account for the uncertainty inherent in soil/rock parameters.This study explores the application of innovative intelligent techniques to predict S_(m) to address these shortcomings.Specifically,two optimization algorithms,namely teaching-learning-based optimization(TLBO)and harmony search(HS),are harnessed for this purpose.The modeling process involves utilizing input parameters,such as thewidth of the footing(B),the pressure exerted on the footing(q),the count of SPT(Standard Penetration Test)blows(N),the ratio of footing embedment(Df/B),and the footing’s geometry(L/B),during the training phase with a dataset comprising 151 data points.Then,the models’accuracy is assessed during the testing phase using statistical metrics,including the coefficient of determination(R^(2)),mean square error(MSE),and rootmean square error(RMSE),based on a dataset of 38 data points.The findings of this investigation underscore the substantial efficacy of intelligent optimization algorithms as valuable tools for geotechnical engineers when estimating S_(m).In addition,a sensitivity analysis of the input parameters in S_(m) estimation is conducted using@RISK software,revealing that among the various input parameters,the N exerts the most pronounced influence on S_(m).

关 键 词：Shallow foundations optimization algorithms settlement prediction intelligent methods sensitivity analysis 

分 类 号：TU470[建筑科学—结构工程]