Machine learning techniques for prediction of failure loads and fracture characteristics of high and ultra-high strength concrete beams

Innovative Infrastructure Solutions, Journal Year: 2023, Volume and Issue: 8(8)

Published: July 27, 2023

Language: Английский

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Application of Soft Computing Techniques for Slope Stability Analysis

Transportation Infrastructure Geotechnology, Journal Year: 2024, Volume and Issue: 11(6), P. 3903 - 3940

Published: Aug. 5, 2024

Language: Английский

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Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 18, 2024

Language: Английский

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The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(8), P. 3273 - 3290

Published: June 13, 2024

Abstract In the field of data engineering in machine learning (ML), a crucial component is process scaling, normalization, and standardization. This involves transforming to make it more compatible with modeling techniques. particular, this transformation essential ensure suitability for subsequent analysis. Despite application many conventional relatively new approaches ML, there remains conspicuous lack research, particularly geotechnical discipline. study, ML-based prediction models (i.e., RF, SVR, Cubist, SGB) were developed estimate undrained shear strength (UDSS) cohesive soil from perspective wide range data-scaling methods. Therefore, work presents novel ML framework based on Cubist regression method predict UDSS soil. A dataset including six different features one target variable used building models. The performance was examined considering impact pre-processing issue. For that purpose, scaling methods, namely Range, Z-Score, Log Transformation, Box-Cox, Yeo-Johnson, generate results then systematically compared using sampling ratios understand how model varies as various scaling/transformation methods algorithms combined. It observed or had considerable limited effects depending algorithm type ratio. Compared SGB models, provided higher metrics after applying steps. Box-Cox transformed yielded best among other an R 2 0.87 90% training set. Also, generally when transformed-based Log, Yeo-Johnson) than scaled-based Range Z-Score) show has potential prediction, have impacts predictive capacity evaluated

Language: Английский

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Reliability-Based Load and Resistance Factor Design of an Energy Pile with CPT Data Using Machine Learning Techniques

Arabian Journal for Science and Engineering, Journal Year: 2023, Volume and Issue: 49(4), P. 4831 - 4860

Published: Sept. 4, 2023

Language: Английский

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Rock Slope Stability Prediction: A Review of Machine Learning Techniques

Geotechnical and Geological Engineering, Journal Year: 2025, Volume and Issue: 43(3)

Published: Feb. 18, 2025

Language: Английский

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Damage mechanism of tunnel base heave under interlayer weakening effect in nearly horizontal layered rock

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 159, P. 106439 - 106439

Published: Feb. 20, 2025

Language: Английский

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Direct and indirect methods for uniaxial compressive strength estimation in various geo-environments: A review

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Predictive Analysis of Slope Stability via Metaheuristic Algorithms Helping Neural Networks

Geological Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 29, 2025

ABSTRACT Attaining a firm slope stability analysis holds eminent importance in civil and geotechnical projects. This study is concerned with the indirect assessment of slopes using improved versions artificial neural networks (ANN). Two novel metaheuristic techniques, namely seeker optimization algorithm (SOA) electromagnetic field (EFO) are employed for optimising ANN that aims at predicting factor safety (FOS). hybrids EFO‐ANN SOA‐ANN, as well single conventional ANN, trained tested valid dataset collected from earlier literature. First, examining input factors showed unit weight material ( γ ) most important one, followed by internal friction ϕ ), average angle β cohesion c height H pore water pressure coefficient r u ). Upon monitoring performance this model stops training after some epochs because divergence solution, whereas issue was resolved EFO SOA hybrid models. Consequently, significant improvements were achieved both testing accuracies. By comparison, while more successful task, SOA‐ANN presented reliable prediction FOS. The competency these models also verified through (a) comparison literature (b) applying them to another real‐world binary stability/failure. An explicit predictive formula derived which recommended convenient approximator FOS analysis.

Language: Английский

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Assessment of Stability of Slopes and Remedial Measures in Lesser Himalayan Region: An Overview

Indian geotechnical journal, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 9, 2024

Language: Английский

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