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

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Фев. 1, 2025

Язык: Английский

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A Novel Hybrid Bayesian-Group-Based Machine Learning (HB-GML) Method for Predicting Uniaxial Compressive Strength (UCS) of Rock

Rock Mechanics and Rock Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 20, 2025

Язык: Английский

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Assessment of compressive strength of eco-concrete reinforced using machine learning tools

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 11, 2025

Predicting the compressive strength of Compressed Earth Blocks (CEB) is a challenging task due to nonlinear relationships among their diverse components, including cement, clay, sand, silt, and fibers. This study employed PyCaret, an automated machine learning platform, address this complexity by developing evaluating predictive models. The analysis demonstrated that fiber content exhibited strong positive correlation with cement content, coefficient 0.9444, indicating significant influence on strength. Multiple algorithms were tested using metrics such as determination (R2), root mean square error (RMSE), absolute (MAE) assess model performance. Among these, Extra Trees Regressor showed best capability R2 = 0.9444 (highly accurate predictions), RMSE 0.4909 (low variability in prediction errors) MAE 0.1899 (minimal average error). results confirm PyCaret effectively automates workflow, enabling modeling complex material behavior. outperformed other its ability handle highly multivariate datasets, making it particularly well-suited for predicting CEB. approach offers advantage over traditional laboratory testing, which time-consuming resource-intensive. By incorporating techniques, especially PyCaret's streamlined processes, CEB becomes more efficient reliable, providing practical tool engineers researchers science.

Язык: Английский

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Estimation of soil liquefaction using artificial intelligence techniques: an extended comparison between machine and deep learning approaches

Environmental Earth Sciences, Год журнала: 2025, Номер 84(5)

Опубликована: Фев. 21, 2025

Язык: Английский

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Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 2, 2025

Soil classification and analysis are essential for understanding soil properties serve as a foundation various engineering projects. Traditional methods of rely heavily on costly time-consuming laboratory in-situ tests. In this study, Support Vector Machine (SVM) models were trained using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ( $$q_c$$ ) sleeve friction $$f_s$$ input variables. Pearson correlation sensitivity confirmed that these variables highly correlated with the results. To enhance performance, 25 optimization algorithms applied, validated against an independent dataset 208 CPT records. The results revealed 23 successfully improved SVM accuracy. Among these, 18 achieved higher accuracy than current standard, "Code Measurement Railway Engineering Geology." Notably, Thermal Exchange Optimization (TEO) algorithm resulted in most significant improvement, increasing original model by 10% exceeding standard 4.3%. Moreover, thoroughly evaluated Monte Carlo simulations, confusion matrices, ROC curves, 10 key performance metrics. conclusion, integrating evolutionary offers promising approach to enhancing efficiency applications.

Язык: Английский

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Prediction of time-dependent bearing capacity of concrete pile in cohesive soil using optimized relevance vector machine and long short-term memory models

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 30, 2024

Язык: Английский

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Effect of multicollinearity in assessing the compaction and strength parameters of lime-treated expansive soil using artificial intelligence techniques

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 8(1)

Опубликована: Ноя. 18, 2024

Язык: Английский

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Predictive Genetic Programming Approaches for Swell-Shrink Soil Compaction

Earth Science Informatics, Год журнала: 2024, Номер unknown

Опубликована: Сен. 19, 2024

Язык: Английский

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Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0311749 - e0311749

Опубликована: Янв. 24, 2025

Since the dissemination of information is more rapid and scale users on online platforms enormous, public opinion risk visible harder to tackle for universities authorities. Improving accuracy predictions regarding crises, especially those related campuses, crucial maintaining social stability. This research proposes a crisis prediction model that applies Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) implements it analyze trending topic Sina Weibo validate its accuracy. A full-chain analytical framework established in this study, which enables illustrate level variation trend by introducing index. The validated through several evaluation criteria, comparison between real predicted results, simulation intervention incident indicates proposed competent both assisting intervention. study also demonstrates importance immediate response crises.

Язык: Английский

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Evaluating the RMR correlation with the rock mass wave velocity using the meta-heuristics algorithms

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 21, 2025

Язык: Английский

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