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contributor authorChen, Yit-Jin
contributor authorWang, Kai
contributor authorPhoon, Kok-Kwang
contributor authorGuo, Matthew
contributor authorJos, Mary Abigail
date accessioned2026-08-20T21:23:30Z
date available2026-08-20T21:23:30Z
date copyright2026/02/11
date issued2026
identifier otherAJRUA6.RUENG-1737.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314375
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 ...
publisherAmerican Society of Civil Engineers
titlePre-Bored PC Pile Capacity Prediction in Drained Soils Using Artificial Neural Network
typeJournal Article
journal volume12
journal issue2
journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
identifier doi10.1061/AJRUA6.RUENG-1737
journal fristpage04026007-1
journal lastpage04026007-26
page26
treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 002
contenttypeFulltext


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