Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e02901 - e02901

Published: Jan. 19, 2024

The construction sector is a major contributor to global greenhouse gas emissions. Using recycled and waste materials in concrete practical solution address environmental challenges. Currently, agricultural widely used as substitute for cement the production of eco-friendly concrete. However, traditional methods assessing strength such are both expensive time-consuming. Therefore, this study uses machine learning techniques develop prediction models compressive (CS) rice husk ash (RHA) ML present include random forest (RF), light gradient boosting (LightGBM), ridge regression, extreme (XGBoost). A total 348 values CS were collected from experimental studies, five characteristics RHA taken input variables. For performance assessment models, multiple statistical metrics used. During training phase, correlation coefficients (R) obtained RF, XGBoost, LightGBM 0.943, 0.981, 0.985, 0.996, respectively. In testing set, these demonstrated even higher performance, with 0.971, 0.993, 0.992, 0.998 LightGBM, analysis revealed that model outperformed other whereas regression exhibited comparatively lower accuracy. SHapley Additive exPlanation (SHAP) method was employed interpretability developed model. SHAP water-to-cement controlling parameter estimating conclusion, provides valuable guidance builders researchers estimate it suggested more variables be incorporated hybrid utilized further enhance reliability precision models.

Language: Английский

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Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101837 - 101837

Published: Feb. 6, 2024

Contemporary infrastructure requires structural elements with enhanced mechanical strength and durability. Integrating nanomaterials into concrete is a promising solution to improve However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions provide accurate reliable estimations. This study focuses on developing robust prediction for compressive (CS) graphene nanoparticle-reinforced (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), AdaBoost (AR), were employed predict CS based comprehensive dataset 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand (SC), curing age (CA), thickness (GT), considered. The trained 70 % data, remaining 30 data was used testing models. Statistical metrics as mean absolute error (MAE), root square (RMSE) correlation coefficient (R) assess predictive accuracy DT AR demonstrated exceptional accuracy, yielding high coefficients 0.983 0.979 training, 0.873 0.822 testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted influential role positively impacting CS, while an increased (w/c) negatively affected CS. showcases efficacy techniques accurately predicting nanoparticle-modified concrete, offering swift cost-effective approach assessing nanomaterial impact reducing reliance time-consuming expensive experiments.

Language: Английский

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Performance evaluation of concrete made with plastic waste using multi-expression programming

Materials Today Communications, Journal Year: 2024, Volume and Issue: 39, P. 108789 - 108789

Published: April 1, 2024

Language: Английский

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Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102637 - 102637

Published: July 29, 2024

Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air compared to other photocatalysts due its inherent inertness, cost-effectiveness, photostability. To assess effectiveness, laboratory examinations are frequently employed measure photocatalytic rate TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, high costs. In literature, ensemble or standalone models commonly used for assessing performance TiO2 water contaminants. Nonetheless, application metaheuristic hybrid potential be more effective predictive accuracy efficiency. Accordingly, research utilized machine learning (ML) algorithms estimate photo-degradation constants organic pollutants using nanoparticles exposure ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction (NRO), differential evolution algorithm (DEA), human felicity (HFA), lightning search (LSA), Harris hawks (HHA), tunicate swarm (TSA) were combined with random forest (RF) technique establish models. A database 200 data points was acquired from experimental studies model training testing. Furthermore, multiple statistical indicators 10-fold cross-validation examine established model's robustness. The TSA-RF demonstrated superior prediction among six suggested models, achieving an impressive correlation (R) 0.90 lower root mean square error (RMSE) 0.25. contrast, HFA-RF, HHA-RF, NRO-RF exhibited slightly R-value 0.88, RMSE scores 0.32. DEA-RF LSA-RF while effective, showed marginally 0.85, values 0.45 0.44, respectively. Moreover, SHapley Additive exPlanation (SHAP) results indicated that rates through photocatalysis most notably influenced by factors such reactor sizes, dosage, humidity, intensity.

Language: Английский

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An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 449, P. 138346 - 138346

Published: Sept. 17, 2024

Language: Английский

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Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming

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

Published: March 13, 2024

Abstract Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria strength, stiffness, and permeability. High workability consistency are attributes BPC because is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting slump BPC. In addition, prediction models offer valuable tools estimate various strength parameters, enabling adjustments mixing designs optimize project construction, leading cost time savings. Therefore, this study explores multi-expression programming (MEP) technique predict key characteristics BPC, such slump, compressive ( fc ), elastic modulus Ec ). present study, 158, 169, 111 data points were collected from experimental studies , Ec, respectively. The dataset was divided three sets: 70% training, 15% testing, another model validation. MEP exhibited excellent accuracy with correlation coefficient (R) 0.9999 0.9831 fc, 0.9300 Ec. Furthermore, comparative analysis between conventional linear non-linear regression revealed remarkable precision in predictions proposed models, surpassing traditional methods. SHapley Additive exPlanation indicated that water, cement, bentonite exert significant influence on water having greatest impact while curing cement exhibit higher modulus. summary, application machine learning algorithms offers capability deliver prompt precise early estimates properties, thus optimizing efficiency design processes.

Language: Английский

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Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103909 - 103909

Published: Jan. 1, 2025

Language: Английский

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Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models

Structures, Journal Year: 2025, Volume and Issue: 71, P. 108138 - 108138

Published: Jan. 1, 2025

Language: Английский

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Revealing the nature of soil liquefaction using machine learning

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

Published: Jan. 21, 2025

Language: Английский

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Optimizing high-strength concrete compressive strength with explainable machine learning

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(3)

Published: Feb. 3, 2025

Language: Английский

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