Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment

Zamri Chik, Qasim A. Aljanabi, Anuar Kasa, Mohd. Raihan Taha

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Construction of embankments in engineering structures on soft clay soils normally encounters problems related to excessive settlement issues. The conventional methods are inadequate to analyze and predict the surface settlement when the necessary parameters are difficult to determine in the field and in the laboratory. In this study, artificial neural network systems (ANNs) were used to predict settlement under embankment load using soft soil properties together with various geometric parameters as input for each stone column (SC) arrangement and embankment condition. Data from a highway project called Lebuhraya Pantai Timur2 in Terengganu, Malaysia, were investigated. The FEM package of Plaxis v8 program analysis was utilized. The actual angle of internal friction, spacing between SC, diameter of SC, length of SC, and height of embankment were used as the input parameters, and the settlement was used as the main output. Non cross validation (NCV) and tenfold cross validation (TFCV) were used to build the ANN model. The results of the TFCV model were more accurate than those of the NCV model. Comparisons made with the predictions of the Priebe model showed that the proposed TFCV model could provide better predictions than conventional methods.

Original languageEnglish
Pages (from-to)4877-4887
Number of pages11
JournalArabian Journal of Geosciences
Volume7
Issue number11
DOIs
Publication statusPublished - 2013

Fingerprint

embankment
artificial neural network
model validation
road
soft soil
modeling
soft clay
prediction
clay soil
soil property
spacing
friction
engineering
stone
parameter
method

Keywords

  • Artificial neural network
  • Soil improvement
  • Stone column behavior
  • Tenfold cross neural network

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Environmental Science(all)

Cite this

@article{484b685b26d343bf8455c42037f9c650,
title = "Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment",
abstract = "Construction of embankments in engineering structures on soft clay soils normally encounters problems related to excessive settlement issues. The conventional methods are inadequate to analyze and predict the surface settlement when the necessary parameters are difficult to determine in the field and in the laboratory. In this study, artificial neural network systems (ANNs) were used to predict settlement under embankment load using soft soil properties together with various geometric parameters as input for each stone column (SC) arrangement and embankment condition. Data from a highway project called Lebuhraya Pantai Timur2 in Terengganu, Malaysia, were investigated. The FEM package of Plaxis v8 program analysis was utilized. The actual angle of internal friction, spacing between SC, diameter of SC, length of SC, and height of embankment were used as the input parameters, and the settlement was used as the main output. Non cross validation (NCV) and tenfold cross validation (TFCV) were used to build the ANN model. The results of the TFCV model were more accurate than those of the NCV model. Comparisons made with the predictions of the Priebe model showed that the proposed TFCV model could provide better predictions than conventional methods.",
keywords = "Artificial neural network, Soil improvement, Stone column behavior, Tenfold cross neural network",
author = "Zamri Chik and Aljanabi, {Qasim A.} and Anuar Kasa and Taha, {Mohd. Raihan}",
year = "2013",
doi = "10.1007/s12517-013-1128-6",
language = "English",
volume = "7",
pages = "4877--4887",
journal = "Arabian Journal of Geosciences",
issn = "1866-7511",
publisher = "Springer Verlag",
number = "11",

}

TY - JOUR

T1 - Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment

AU - Chik, Zamri

AU - Aljanabi, Qasim A.

AU - Kasa, Anuar

AU - Taha, Mohd. Raihan

PY - 2013

Y1 - 2013

N2 - Construction of embankments in engineering structures on soft clay soils normally encounters problems related to excessive settlement issues. The conventional methods are inadequate to analyze and predict the surface settlement when the necessary parameters are difficult to determine in the field and in the laboratory. In this study, artificial neural network systems (ANNs) were used to predict settlement under embankment load using soft soil properties together with various geometric parameters as input for each stone column (SC) arrangement and embankment condition. Data from a highway project called Lebuhraya Pantai Timur2 in Terengganu, Malaysia, were investigated. The FEM package of Plaxis v8 program analysis was utilized. The actual angle of internal friction, spacing between SC, diameter of SC, length of SC, and height of embankment were used as the input parameters, and the settlement was used as the main output. Non cross validation (NCV) and tenfold cross validation (TFCV) were used to build the ANN model. The results of the TFCV model were more accurate than those of the NCV model. Comparisons made with the predictions of the Priebe model showed that the proposed TFCV model could provide better predictions than conventional methods.

AB - Construction of embankments in engineering structures on soft clay soils normally encounters problems related to excessive settlement issues. The conventional methods are inadequate to analyze and predict the surface settlement when the necessary parameters are difficult to determine in the field and in the laboratory. In this study, artificial neural network systems (ANNs) were used to predict settlement under embankment load using soft soil properties together with various geometric parameters as input for each stone column (SC) arrangement and embankment condition. Data from a highway project called Lebuhraya Pantai Timur2 in Terengganu, Malaysia, were investigated. The FEM package of Plaxis v8 program analysis was utilized. The actual angle of internal friction, spacing between SC, diameter of SC, length of SC, and height of embankment were used as the input parameters, and the settlement was used as the main output. Non cross validation (NCV) and tenfold cross validation (TFCV) were used to build the ANN model. The results of the TFCV model were more accurate than those of the NCV model. Comparisons made with the predictions of the Priebe model showed that the proposed TFCV model could provide better predictions than conventional methods.

KW - Artificial neural network

KW - Soil improvement

KW - Stone column behavior

KW - Tenfold cross neural network

UR - http://www.scopus.com/inward/record.url?scp=84919936321&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84919936321&partnerID=8YFLogxK

U2 - 10.1007/s12517-013-1128-6

DO - 10.1007/s12517-013-1128-6

M3 - Article

AN - SCOPUS:84919936321

VL - 7

SP - 4877

EP - 4887

JO - Arabian Journal of Geosciences

JF - Arabian Journal of Geosciences

SN - 1866-7511

IS - 11

ER -