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Jebur, AAJ, Atherton, W and Al Khaddar, RM (2018) Feasibility of an evolutionary artificial intelligence (AI) scheme for modelling of load settlement response of concrete piles embedded in cohesionless soil. Ships and Offshore Structures. ISSN 1744-5302
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Feasibility of an evolutionary artificial intelligence (AI) scheme for modelling of load settlement response of concrete piles embedded in cohesionless soil.pdf - Accepted Version Download (1MB) | Preview |
Abstract
This investigation aimed to examine the load carrying capacity of piles embedded in sandy soil of various densities, and to develop a predictive model to determine pile settlement using a novel artificial intelligence (AI) method. Experimental pile load tests were conducted using three concrete piles, with aspect ratios of 12, 17 and 25. Evolutionary Levenberg–Marquardt MATLAB algorithms, enhanced by T-tests and F-tests, were used in this process. According to the statistical analysis and the relative importance study, pile length, applied load, pile flexural rigidity, pile aspect ratio and sand–pile friction angle were found to play a key role in pile settlement. Results revealed that the proposed optimum model algorithm precisely characterized pile settlement. There was close agreement between the experimental and predicted data (Pearson's R = 0.988, P = 6.28 × 10 -31 ) with a relatively insignificant root mean square error of 0.002. © 2018 Informa UK Limited, trading as Taylor & Francis Group
| Item Type: | Article |
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| Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis in Ships and Offshore Structures on 23/03/2018, available online: http://www.tandfonline.com/10.1080/17445302.2018.1447746 |
| Uncontrolled Keywords: | 0911 Maritime Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TC Hydraulic engineering. Ocean engineering T Technology > TH Building construction |
| Divisions: | Civil Engineering (merged with Built Env 10 Aug 20) |
| Publisher: | Taylor & Francis |
| Date Deposited: | 27 Apr 2018 11:21 |
| Last Modified: | 04 Sep 2021 02:43 |
| DOI or ID number: | 10.1080/17445302.2018.1447746 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/8594 |
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