Three-Stage Analysis Method for Calculating the Settlement of Large-Diameter Extralong Piles

Xiao, Kevin; Guo, Shihong; Wen, Jianghai; Han, Jingbo; Yang, Xiong

doi:10.1061/ijgnai.gmeng-8110

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…For very long piles, such as those commonly used in Ho Chi Minh City (50-100 m long), the base resistance only becomes significant when the pile tip presses the soil underneath to a certain degree under vertical compression load. This is because the shaft friction usually takes the majority of the applied load at the initial stage before the soil-pile interaction propagates downward with larger contribution from the base resistance [14,15]. In soft soil regions, such as the Mekong delta where the layer of soft clay (SPT N ≈ 0-5) can exceed 25-35 m [10], the pile toe is normally required to sit on a hard soil layer at extremely great depths (e.g., coarse to medium dense sand layers at a depth of 70-100 m), resulting in a significant but complex contribution of base resistance to the entire bearing capacity of piles.…”

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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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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show abstract

“…As an illustration, it could generate the necessary code from a drawing of a website design. There are additional platforms available, such as the Bard platform from Google, and additional LLMs, such as the open-access, multilingual BLOOM LLM with 176 B parameters [19], which can be used to power chat-like interfaces and to build other applications. The capacity to properly induce desired outputs is not always straightforward because of the iterative nature of GPT-3.5, GPT-4, BARD, Claude, and cohere and the sensitivity to the exact choice of the text prompt.…”

Section: Introductionmentioning

confidence: 99%

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Assiri,

Gumaei,

Mehmood

et al. 2024

CMC

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Modern technological advancements have made social media an essential component of daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive, negative, neutral, or any other personal emotion to understand the sentiment context of the text. Sentiment analysis is essential in business and society because it impacts strategic decision-making. Sentiment analysis involves challenges due to lexical variation, an unlabeled dataset, and text distance correlations. The execution time increases due to the sequential processing of the sequence models. However, the calculation times for the Transformer models are reduced because of the parallel processing. This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations. In particular, the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers (BERT) attention (DeBERTa) and the Gated Recurrent Unit (GRU) for sentiment analysis. Using the Decoding-enhanced BERT technique, the words are mapped into a compact, semantic word embedding space, and the Gated Recurrent Unit model can capture the distance contextual semantics correctly. The proposed hybrid model achieves F1-scores of 97% on the Twitter Large Language Model (LLM) dataset, which is much higher than the performance of new techniques.

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Soft computing for determining base resistance of super-long piles in soft soil: A coupled SPBO-XGBoost approach

Nguyen

Huynh

et al. 2023

Computers and Geotechnics

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Three-Stage Analysis Method for Calculating the Settlement of Large-Diameter Extralong Piles

Cited by 3 publications

References 34 publications

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

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

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Soft computing for determining base resistance of super-long piles in soft soil: A coupled SPBO-XGBoost approach

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