Material Engineering

QiLi Zuo This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Yunnan Technology and Business University, KunMing, Yunnan, 650228, China

 

Received: September 13, 2021
Accepted: January 21, 2022
Publication Date: April 11, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202301_26(1).0012  


ABSTRACT

The settlement of piles socketed into rock has captured remarkable consideration. In spite of very design techniques of settlement of pile are introduced, developing new as well statistical method with higher performance prediction may be of advantage. At the present study, multivariate adaptive regression splines (MARS) method with different degree of interactions were proposed to predict the settlement of piles in the Klang Valley Mass Rapid Transit (KVMRT) project that constructed in Kuala Lumpur, Malaysia. In this research the results of pile driving analyzer tests along with piles and earth’s properties were considered. Five parameters considered as inputs and piles settlement as output variable. Comparing the measured settlements from experimental efforts with those predicted by MARS method are supplied that the developed models have coefficient of determination (R2) in acceptance rate. By apprising the performance index (PI) index as the whole model evaluator, which considers other indexes altogether, the order 3 MARS model outperforms the other two models, with lower PI values equal to 0.0541 and 0.0951 in training and testing phase, respectively. Therefore, the third interaction equation of MARS for predicting settlement of rock-socketed piles can be recognized as the proposed regression model.


Keywords: Pile socketed into rock; Settlement; Prediction; Multivariate Adaptive Regression Splines


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