Prediction of dynamic shear modulus of saturated coral sand based on explainable artificial intelligence

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 461, P. 139878 - 139878

Published: Jan. 1, 2025

Language: Английский

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Geotechnical and microstructural analysis of high-volume fly ash stabilized clayey soil and machine learning application

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03628 - e03628

Published: Aug. 10, 2024

The weak soil stabilization using solid wastes is one of the most common solutions for improving geotechnical characteristics as well problematic waste dumping in landfills. present experimental study aims to examine effect high-volume Class-F fly ash on and microstructural properties clayey by adding them ranges between 5 % 50 %. results show that amount increases, like specific gravity, plasticity index, permeability, optimum moisture content, maximum dry density free swelling index improves. Moreover, these were analyzed develop machine learning models three different algorithms, namely K-nearest neighbor regression, random forest, support vector obtaining contents expansive soils. predicted found be close-relation predicting behavior modified soil. Furthermore, performance ML degrades number components reduces, with KNN regression consistently outperforming SVR RF but suffering significantly fewer components. testing set case four are MSE 77, R² 0.896, RMSE 0.846, MAE 0.327, SEE 0.858, indicating precise consistent predictions. However, prediction accuracy considering lesser shows 262, 0.648, 5.606, 16.707, GPI 1.056, confirming elevated error rates. Overall, it has been concluded combining comprehensive work techniques outperforms enhancing data processing, optimized soils, improves sustainability construction, saves resources, reduces possibility human mistakes, increases reliability.

Language: Английский

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A comprehensive review on the application of microbially induced calcite precipitation (MICP) technique in soil erosion mitigation as a sustainable and environmentally friendly approach

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103235 - 103235

Published: Oct. 25, 2024

Language: Английский

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Application of natural and synthetic fibers in bio-based earthen composites: A state-of-the-art review

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103732 - 103732

Published: Dec. 1, 2024

Language: Английский

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A comparison of temperature and freeze-thaw effects on high-swelling and low-swelling soils stabilized with xanthan gum

Results in Engineering, Journal Year: 2024, Volume and Issue: 25, P. 103719 - 103719

Published: Dec. 10, 2024

Language: Английский

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Performance assessment of a foundation resting on reinforced collapsible Sabkha soil by deep soil mixing columns using machine learning analyses

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 118, P. 591 - 605

Published: Jan. 28, 2025

Language: Английский

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Dust suppression performance and dust suppression mechanism of microbial induced struvite precipitation technology

Powder Technology, Journal Year: 2025, Volume and Issue: unknown, P. 120905 - 120905

Published: March 1, 2025

Language: Английский

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Enhancing biocement performance via brushite-to-hydroxyapatite transformation from waste bones

Acta Geotechnica, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Language: Английский

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Predictive Modeling of Shear Strength of Enzyme-Induced Calcium Carbonate Precipitation (EICP)-Solidified Rubber–Clay Mixtures Using Machine Learning Algorithms

Polymers, Journal Year: 2025, Volume and Issue: 17(7), P. 976 - 976

Published: April 3, 2025

The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly optimizing material compositions and reducing experimental uncertainties. Traditional approaches determining the optimal rubber particle size content are often resource-intensive, time-consuming, subject to significant variability. In this study, shear strength clay mixed with particles solidified by Enzyme-Induced Calcium Carbonate Precipitation (EICP) technique was investigated predictively modeled using machine learning algorithm. effects different contents sizes on were analyzed experimentally, hybrid model convolutional neural network (CNN) long short-term memory (LSTM) optimized based crown porcupine optimization (CPO) algorithm proposed predict EICP-treated particles. superiority CPO-CNN-LSTM predicting verified comparing multiple algorithms. results show that addition significantly improves clay, especially at 5% content. coefficient determination (R2) training test datasets reaches 0.98 0.97, respectively, which exhibit high prediction accuracy generalization ability.

Language: Английский

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Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3558 - 3558

Published: March 25, 2025

Stabilizing sandy soil with inadequate engineering properties is essential for constructing infrastructure systems in all regions, especially desertification-prone areas. Enzymatically Induced Carbonate Precipitation (EICP) offers an innovative solution, advantages over conventional reinforcement methods due to its low energy consumption and carbon emission. This emerging technique has proven effective enhancing strength, yet the effects of variables such as curing time cementation solution concentration, their micro-mechanistic implications on soil, remain understudied. study conducted a series unconfined compressive strength (UCS) tests microstructural analyses EICP-treated sand. The results showed that optimal EICP-reinforced sand seven days, being contingent upon density. maximum UCS value was observed at relative density 0.7 concentration 1 mol/L. Mechanistically, EICP strengthens integrity through calcium carbonate-mediated particle bridging, thereby boosting strength. Micro-CT imaging fractal dimension reveal precipitation process decreases both size connectivity pores, while simultaneously increasing surface heterogeneity overall toughness. research establishes foundational framework advancing applications stabilization engineering.

Language: Английский

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