Assessment of Driven Pile Ultimate Capacity through Artificial Neural Network Analysis of Cone Penetration Test Data

Author:

Mojumder Md Ariful1,Abu-Farsakh Murad Y.2ORCID,Rosti Firouz3,Chen Shengli4ORCID

Affiliation:

1. Klohn Crippen Berger Ltd., Vancouver, British Columbia, Canada

2. Louisiana Transportation Research Center, Louisiana State University, Baton Rouge, LA

3. Engineering and Computer Science Department, McNeese State University, Lake Charles, LA

4. Department of Civil & Environmental Engineering, Louisiana State University, Baton Rouge, LA

Abstract

In this research, the application of an artificial neural network (ANN) was employed utilizing cone penetration test (CPT) information to produce an enhanced comprehension of the ultimate load-bearing capacity of piles. The ANN algorithm is independent of correlation assumptions as it uses prior cases/instances to grasp the relationship. A database of eighty pile load tests on squared precast/prestressed concrete (PPC) driven piles and corresponding CPT data was prepared in this regard, in which the ANN models were trained using these data. Feed-forward network techniques such as backpropagation algorithm, Levenberg–Marquardt algorithm were used with trial and error. The cone sleeve friction and corrected cone tip resistance were used to train numerous ANN models. A comparison was made between the prediction of ANN models and three pile-CPT methods, that is, Laboratoire central des pontes et chaussées (LCPC), probabilistic, and University of Florida (UF) methods. The findings of this research exhibited that ANN excels in the evaluation of ultimate capacity of squared PPC piles. A comparison was also made with LCPC, probabilistic, and UF method on the basis of reliability-based load and resistance factor design analysis, which also demonstrates higher resistance factors, ϕ, and superior efficiencies of ANN models over the traditional pile-CPT methods. Consequently, these discoveries reinforce the efficacy of utilizing ANN for assessing the ultimate load-bearing capacity of piles through the interpretation of CPT data.

Publisher

SAGE Publications

Reference43 articles.

1. Bearing Capacity and Settlement of Pile Foundations

2. New Design Correlations for Piles in Sand

3. Schmertmann J. Guidelines for Cone Penetration Test Performance and Design. FHWA TS-78-209. Federal Highway Administration, Washington, D.C., 1978. https://rosap.ntl.bts.gov/view/dot/958.

4. Pile foundations for large North Sea structures

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3