Universiti Teknologi Malaysia Institutional Repository

Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

Armaghani, Danial Jahed and Faradonbeh, Roohollah Shirani and Rezaei, Hossein and A. Rashid, Ahmad Safuan and Amnieh, Hassan Bakhshandeh (2016) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Computing and Applications . pp. 1-11. ISSN 0941-0643 (In Press)

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Abstract

The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively.

Item Type:Article
Uncontrolled Keywords:Gene expression programming, Linear multiple regression, Rock-socketed pile, Settlement
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:72804
Deposited By: Fahmi Moksen
Deposited On:20 Nov 2017 08:04
Last Modified:20 Nov 2017 08:04

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