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

Nguyen, Thang Trung; Ly, Duy-Khuong; Huynh, Quoc Thien; Nguyên, Thanh Trung

doi:10.1016/j.compgeo.2023.105707

Cited by 14 publications

(7 citation statements)

References 48 publications

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“…Researchers can determine which factors have the strongest influence on the predictions by systematically adjusting the supplied variables and observing the associated changes in the output of the model. 185,186 For example, a sensitivity analysis may reveal that the moisture content and fixed carbon content have a significant impact on biochar yield, whereas the ash content and pH level mainly impact features such as the surface area and pore size distribution of the biochar. 187 It has also been observed that although some components, such as alkaline-earth metals, may have a modest content and sensitivity to biochar, their impact on the characteristics of the biochar can be significant.…”

Section: Sensitivity Of Machine Learning Models To Input Variablesmentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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Section: Sensitivity Of Machine Learning Models To Input Variablesmentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

View full text Add to dashboard Cite

show abstract

“…There are various Machine Learning (ML) algorithms that have been developed and successfully applied to pile foundation in recent years, among which XGBoost and Random Forest (RF) have shown the best performance [21,25]. Therefore, these two algorithms were selected to predict base resistance, as well as in assessing the role of pile settlement.…”

Section: Selection and Algorithms For Machine Learning Modelsmentioning

confidence: 99%

“…The differences in these two algorithms and their implementations in the current database are represented in Figure 7. Further details of these techniques, including their mathematical formula, have been given in various past studies [21,34], so they are not further repeated in this paper for brevity.…”

Section: Selection and Algorithms For Machine Learning Modelsmentioning

confidence: 99%

“…Geotechnics 2024, 4, FOR PEER REVIEW Random Forest (RF) have shown the best performance [21,25]. Therefore, these two al rithms were selected to predict base resistance, as well as in assessing the role of pile s tlement.…”

Section: Selection and Algorithms For Machine Learning Modelsmentioning

confidence: 99%

“…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%

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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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Axial Load-Bearing Capacity Solutions of PHC Nodular Pile: A New Advancement in Vietnam Engineering Practice

Aphisith,

Ly,

Nguyen

et al. 2024

Lecture Notes in Civil Engineering

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

Cited by 14 publications

References 48 publications

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

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

Axial Load-Bearing Capacity Solutions of PHC Nodular Pile: A New Advancement in Vietnam Engineering Practice

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