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Pre-Bored PC Pile Capacity Prediction in Drained Soils Using Artificial Neural Network

dc.contributor.authorChen, Yit-Jin
dc.contributor.authorWang, Kai
dc.contributor.authorPhoon, Kok-Kwang
dc.contributor.authorGuo, Matthew
dc.contributor.authorJos, Mary Abigail
dc.date.accessioned2026-08-20T21:23:30Z
dc.date.available2026-08-20T21:23:30Z
dc.date.copyright2026/02/11
dc.date.issued2026
dc.identifier.otherAJRUA6.RUENG-1737.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314375
dc.description.abstractAbstractThis study evaluated the performance of artificial neural network (ANN) in determining pre-bored precast concrete (PC) pile capacities, utilizing a database of 39 drained pile load tests under axial compression. The ANN model developed in this ...Practical ApplicationsPre-bored precast concrete (PC) piles are increasingly used in design and construction, especially in urban areas where minimizing noise and vibration during construction is important. A major challenge for engineers is determining ...
dc.publisherAmerican Society of Civil Engineers
dc.titlePre-Bored PC Pile Capacity Prediction in Drained Soils Using Artificial Neural Network
dc.typeJournal Article
dc.journal.volume12
dc.journal.issue2
dc.journal.titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi10.1061/AJRUA6.RUENG-1737
dc.journal.fristpage04026007-1
dc.journal.lastpage04026007-26
dc.page26
dc.treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 002
dc.contenttypeFulltext


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