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

doi:10.1007/s11709-018-0505-3

Front. Struct. Civ. Eng.

2019, Vol. 13

Issue (3) : 674-685 https://doi.org/10.1007/s11709-018-0505-3

RESEARCH ARTICLE

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

Tanvi SINGH(

), Mahesh PAL, V. K. ARORA

Department of Civil Engineering, National Institute of Technology, Kurukshetra, Haryana 136119, India

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Abstract

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 (α), 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 (α) and number of batter pile (B) affect the oblique load capacity for both smooth and rough pile groups.

Keywords batter piles oblique load test neural network M5 model tree random forest regression ANOVA

Corresponding Author(s): Tanvi SINGH

Just Accepted Date: 09 July 2018 Online First Date: 23 August 2018 Issue Date: 05 June 2019

Cite this article:

Tanvi SINGH,Mahesh PAL,V. K. ARORA. Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree[J]. Front. Struct. Civ. Eng., 2019, 13(3): 674-685.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-018-0505-3
https://academic.hep.com.cn/fsce/EN/Y2019/V13/I3/674

Fig.1 Model setup

Fig.2 Particle size distribution curve for sand

Tab.1 Properties of sand

Fig.3 Two pile surfaces

Fig.4 Plan of pile caps. (-ve): Negative batter pile

r (kN/m³)	constant parameter			variable parameter
r (kN/m³)	a (° )	L (m)	pile surface	pile groups
16.28	0	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	0	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	0	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	10	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	10	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	10	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	20	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	20	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	20	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	30	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	30	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	30	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	45	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	45	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	45	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	0	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	0	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	0	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	10	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	10	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	10	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	20	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	20	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	20	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	30	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	30	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	30	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	45	0.40	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	45	0.60	smooth	1B, 1V1B, 2B, 2V2B, 4B
15.79	45	0.90	smooth	1B, 1V1B, 2B, 2V2B, 4B
16.28	0	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	0	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	10	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	10	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	20	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	20	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	30	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	30	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	45	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
16.28	45	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	0	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	0	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	10	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	10	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	20	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	20	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	30	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	30	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	45	0.40	rough	1B, 1V1B, 2B, 2V2B, 4B
15.79	45	0.60	rough	1B, 1V1B, 2B, 2V2B, 4B

Tab.2 Summary of test performed

Tab.3 Summary of training and testing data set for smooth piles

Tab.4 Summary of training and testing data set for rough piles

Tab.5 Optimal values of user defined parameter

Fig.5 Plot between actual vs. predicted load using modelling approaches using smooth piles (Set 1). (a) M5; (b) RF; (c) NN

Tab.6 Detail of performance evaluation parameters using M5, RF, and NN for testing data on all three set of results

Tab.7 Results of ANOVA: Single factor test

Fig.6 Plot between actual vs. predicted load using modelling approaches using rough piles (Set 1). (a) M5; (b) RF; (c) NN

Fig.7 Plot between actual vs. predicted load using modelling approaches (Set 3). (a) M5; (b) RF; (c) NN

Tab.8 Sensitivity analysis using RF, NN and M5 modelling approach

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