Modeling oblique load carrying capacity of batter pile groups using neural network,random forest regression and M5 model tree

作     者：Tanvi SINGH Mahesh PAL V.K.ARORA 

作者机构：Department of Civil EngineeringNational Institute of TechnologyKurukshetraHaryana 136119India 

出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)

年 卷 期：2019年第13卷第3期

页      面：674-685页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 08[工学] 0813[工学-建筑学] 0814[工学-土木工程] 

主　　题：batter piles oblique load test neural network M5 model tree random forest regression ANOVA 

摘      要：M5 model tree,random forest regression(RF)and neural network(NN)based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups.Pile length(L),angle of oblique load(a),sand density(ρ),number of batter piles(B),and number of vertical piles(V)as input and oblique load(Q)as output was used.Results suggest improved performance by RF regression for both pile groups.M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also.Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data.NN based approach was found performing equally well with both smooth and rough piles.Sensitivity analysis using all three modelling approaches suggest angle of oblique load(a)and number of batter pile(B)affect the oblique load capacity for both smooth and rough pile groups.