Utilizing contemporary machine learning techniques for determining soilcrete properties

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Abstract Soilcrete is an innovative construction material made by combining naturally occurring earth materials with cement. It can be effectively used in areas where other are not readily available due to financial or environmental reasons since soilcrete from natural clay. also help cut down the greenhouse gas emissions industry encouraging use of resources that locally available. Thus, it imperative reliably predict different properties accurate determination these crucial for widespread materials. However, laboratory subjected significant time and resource constraints. As a result, this research was undertaken provide empirical prediction models density, shrinkage, strain mixes using two machine learning algorithms: Gene Expression Programming (GEP) Extreme Gradient Boosting (XGB). The analysis revealed XGB-based predictions correlated more real-life values than GEP having training $${\text{R}}^{2}=0.999$$ R 2 = 0.999 both density shrinkage $${\text{R}}^{2}=0.944$$ 0.944 prediction. Moreover, several explanatory analyses including individual conditional expectation (ICE) shapely were done on XGB model which showed water-to-binder ratio, metakaolin content, modulus elasticity some most important variables forecasting properties. Furthermore, interactive graphical user interface (GUI) has been developed effective utilization civil engineering forecast

Language: Английский

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Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e36841 - e36841

Published: Aug. 27, 2024

Language: Английский

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Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 22, P. e04112 - e04112

Published: Dec. 11, 2024

Language: Английский

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Ensemble machine learning models for predicting concrete compressive strength incorporating various sand types

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(4)

Published: March 14, 2025

Language: Английский

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An Explainable Machine Learning (XML) approach to determine strength of glass powder concrete

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112181 - 112181

Published: March 1, 2025

Language: Английский

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Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

Interface yield stress and plastic viscosity of fresh concrete significantly influences its pumping ability. The accurate determination these properties needs extensive testing on-site which results in time resource wastage. Thus, to speed up the process accurately determining properties, this study tends use four machine learning (ML) algorithms including Random Forest Regression (RFR), Gene Expression Programming (GEP), K-nearest Neighbor (KNN), Extreme Gradient Boosting (XGB) a statistical technique Multi Linear (MLR) develop predictive models for interface concrete. Out all employed algorithms, only GEP expressed output form an empirical equation. were developed using data from published literature having six input parameters cement, water, after mixing etc. two i.e., stress. performance was assessed several error metrices, k-fold validation, residual assessment comparison revealed that XGB is most algorithm predict (training [Formula: see text], text]) text]). To get increased insights into model prediction process, shapely individual conditional expectation analyses carried out on highlighted are influential estimate both In addition, graphical user has been made efficiently implement findings civil engineering industry.

Language: Английский

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Hybrid Machine Learning Based Strength and Durability Predictions of Polypropylene Fiber-Reinforced Graphene Oxide Based High-Performance Concrete

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 19, 2025

Language: Английский

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Compressive strength of bentonite concrete using state-of-the-art optimised XGBoost models

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: Nov. 22, 2024

This study proposes an advanced soft-computing approach for predicting the compressive strength (CS) of bentonite concrete using optimised XGBoost model. Bentonite is valued as a partial cement replacement its environmental benefits and improved properties, but CS remains challenging due to complex constituent interactions. The study's motivation increasing interest in sustainable materials like replacement, which presents unique challenges high plasticity swelling properties. While hybrid models are effective civil engineering, their application prediction limited. research simulates particle swarm optimisation (PSO), genetic algorithm (GA), dragonfly (DO), supported by comprehensive dataset with varied mix proportions multicollinearity analysis. Hyperparameter tuning feature selection techniques were applied optimise model's performance. results demonstrate that PSO-XGBoost best performing model (R2 = 0.974, RMSE 0.038), followed DO-XGBoost GA-XGBoost. All perform better than conventional developed robust based methodology can serve reliable alternative tool concrete, thereby facilitating design development mixtures enhanced performance characteristics.

Language: Английский

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Integrating testing and modeling methods to examine the feasibility of blended waste materials for the compressive strength of rubberized mortar

REVIEWS ON ADVANCED MATERIALS SCIENCE, Journal Year: 2024, Volume and Issue: 63(1)

Published: Jan. 1, 2024

Abstract This research integrated glass powder (GP), marble (MP), and silica fume (SF) into rubberized mortar to evaluate their effectiveness in enhancing compressive strength ( f c ′ {f}_{\text{c}}^{^{\prime} } ). Rubberized cubes were produced by replacing fine aggregates with shredded rubber varying proportions. The decrease mortar’s was controlled substituting cement GP, MP, SF. Although many literature studies have evaluated the suitability of industrial waste, such as SF, construction material, no yet included combined effect these wastes on mortar. study aims provide complete insight waste By cement, SF added different proportions from 5 25%. Furthermore, artificial intelligence prediction models developed using experimental data assess determined that optimal substitution levels for 15, 10, 15%, respectively. Similarly, partial dependence plot analysis suggests GP a comparable machine learning demonstrated significant resemblance test results. Two individual techniques, support vector random forest, generate R 2 values 0.943 0.983,

Language: Английский

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