Application of extreme gradient boosting method for evaluating the properties of episodic failure of borehole breakout

Geoenergy Science and Engineering, Journal Year: 2023, Volume and Issue: 226, P. 211837 - 211837

Published: April 23, 2023

Language: Английский

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Estimation of Settlement of Pile Group in Clay Using Soft Computing Techniques

Geotechnical and Geological Engineering, Journal Year: 2023, Volume and Issue: 42(3), P. 1729 - 1760

Published: Sept. 18, 2023

Language: Английский

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Smart prediction of liquefaction-induced lateral spreading

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2023, Volume and Issue: 16(6), P. 2310 - 2325

Published: Sept. 5, 2023

The prediction of liquefaction-induced lateral spreading/displacement (Dh) is a challenging task for civil/geotechnical engineers. In this study, new approach proposed to predict Dh using gene expression programming (GEP). Based on statistical reasoning, individual models were developed two topographies: free-face and gently sloping ground. Along with comparison conventional approaches predicting the Dh, four additional regression-based soft computing models, i.e. Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimization (SMOR), M5-tree, compared GEP model. results indicate that less bias, as evidenced by root mean square error (RMSE) absolute (MAE) training (i.e. 1.092 0.815; 0.643 0.526) testing 0.89 0.705; 0.773 0.573) in ground topographies, respectively. overall performance topology was ranked follows: > RVM M5-tree GPR SMOR, total score 40, 32, 24, 15, 10, For condition, SMOR 21, 19, 8, Finally, sensitivity analysis showed both ground, liquefiable layer thickness (T15) major parameter percentage deterioration (%D) value 99.15 90.72,

Language: Английский

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Prediction of joint roughness coefficient via hybrid machine learning model combined with principal components analysis

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

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Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Buildings, Journal Year: 2024, Volume and Issue: 14(4), P. 954 - 954

Published: March 30, 2024

This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) reinforced soil foundations (RSFs) based on settlement criteria, addressing critical gap in sustainable construction practices. Based principles probability and statistics, uncertainties were mapped using first-order second-moment (FOSM) approach. The historical data generated via parametric validated finite element numerical model used to train validate GEP models. Among ten developed frameworks, best-performing model, abbreviated as GEP-M9 (R2 = 0.961 RMSE 0.049), testing phase was perform RA an RSF. model’s effectiveness affirmed through comprehensive evaluation, including sensitivity validation against two independent case studies. index (β) failure (Pf) determined across various coefficient variation (COV) configurations, underscoring potential civil engineering risk analysis. newly has shown considerable analyzing risk, by experimental results varying values.

Language: Английский

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Dimensionality analysis in assessing the unconfined strength of lime-treated soil using machine learning approaches

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

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Optimized ANN-based approach for estimation of shear strength of soil

Asian Journal of Civil Engineering, Journal Year: 2023, Volume and Issue: 24(8), P. 3627 - 3640

Published: June 6, 2023

Language: Английский

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Application of ANN and FELA for Predicting Bearing Capacity of Shell Foundations on Sand

International Journal of Geosynthetics and Ground Engineering, Journal Year: 2023, Volume and Issue: 9(2)

Published: March 30, 2023

Language: Английский

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Slope stability analysis of heavy-haul freight corridor using novel machine learning approach

Modeling Earth Systems and Environment, Journal Year: 2023, Volume and Issue: 10(1), P. 201 - 219

Published: April 24, 2023

Language: Английский

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Data-driven intelligent modeling of unconfined compressive strength of heavy metal-contaminated soil

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 1, 2024

This study focuses on empirical modeling of the strength characteristics urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization ordinary Portland cement (OPC). For dataset collection, an extensive experimental program was designed to estimate unconfined compressive (Qu) metal-contaminated collected from a wide range land use pattern, i.e. residential, industrial roadside soils. Accordingly, robust comparison predictive performances four data-driven models including extreme machines (ELMs), gene expression programming (GEP), random forests (RFs), multiple linear regression (MLR) has been presented. completeness, comprehensive database established partitioned into 80% for training 20% testing developed models. Inputs included varying levels like Cd, Cu, Cr, Pb Zn, along OPC. The results revealed that GEP model outperformed its counterparts: explaining approximately 96% variability in both (R2 = 0.964) phases 0.961), thus achieving lowest RMSE MAE values. ELM performed commendably but slightly less accurate than whereas MLR had performance metrics. also provided benefit traceable mathematical equation, enhancing applicability not just as explanatory tool. Despite insights, is limited by focus specific set soil samples particular region, which may affect generalizability findings different contamination profiles or environmental conditions. recommends predicting Qu soils, suggests further research adapt these

Language: Английский

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