Predicting permanent strain accumulation of unbound aggregates using machine learning algorithms

Won, Jongmuk; Tutumluer, Erol; Byun, Yong-Hoon

doi:10.1016/j.trgeo.2023.101060

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…In recent years, machine learning (ML) has emerged as a preferrable and effective approach, not only to predict various geotechnical issues, but also to assess the interaction between different features [20][21][22]. Past ML applications, including the artificial neural network (ANN) and other advanced algorithms for pile foundation, have mainly focused on prediction of pile bearing capacity and settlement as highlighted in recent review studies, e.g., Baghbani et al (2022) and Nguyen et al (2023b) [23,24].…”

Section: Introductionmentioning

confidence: 99%

Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments

Nguyen,

Le,

Huynh

et al. 2024

Geotechnics

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Understanding the role that settlement can have on the base resistance of piles is a crucial matter in the design and safety control of deep foundations under various buildings and infrastructure, especially for long to super-long piles (60–90 m length) in soft soil. This paper presents a novel assessment of this issue by applying explainable machine learning (ML) techniques to a robust database (1131 datapoints) of fully instrumented pile tests across 37 real-life projects in the Mekong Delta. The analysis of data based on conventional methods shows distinct responses of long piles to rising settlement, as compared to short piles. The base resistance can rapidly develop at a small settlement threshold (0.015–0.03% of pile’s length) and contribute up to 50–55% of the total bearing capacity in short piles, but it slowly rises over a wide range of settlement to only 20–25% in long piles due to considerable loss of settlement impact over the depth. Furthermore, by leveraging the advantages of ML methods, the results significantly enhance our understanding of the settlement–base resistance relationship through explainable computations. The ML-based prediction method is compared with popular practice codes for pile foundations, further attesting to the high accuracy and reliability of the newly established model.

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