Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e03030 - e03030

Опубликована: Март 5, 2024

The construction industry is making efforts to reduce the environmental impact of cement production in concrete by incorporating alternative and supplementary cementitious materials, as well lowering carbon emissions. One such material that has gained popularity this context rice husk ash (RHA) due its pozzolanic reactions. This study aims forecast compressive strength (CS) RHA-based (RBC) examining effects several factors cement, RHA content, curing age, water usage, aggregate amount, superplasticizer content. To accomplish this, collected analyzed data from literature, resulting a dataset 1404 observations. Several machine learning (ML) models, light gradient boosting (LGB), extreme (XGB), random forest (RF), hybrid (HML) approaches like XGB-LGB XGB-RF were employed thoroughly analyze these parameters assess their on strength. was split into training testing groups, statistical analyses performed determine relationships between input CS. Moreover, performance all models evaluated using various evaluation criteria, including mean absolute percentage error (MAPE), coefficient efficiency (CE), root square (RMSE), determination (R2). model found have higher precision (R2 = 0.95, RMSE 5.255 MPa) compared other models. SHAP (SHapley Additive exPlanations) analysis revealed RHA, had positive effect Overall, study's findings suggest with identified can be used accurately predict CS RBC. application technologies sector facilitate rapid low-cost identification qualities parameters.

Язык: Английский

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

Results in Engineering, Год журнала: 2024, Номер 21, С. 101837 - 101837

Опубликована: Фев. 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.

Язык: Английский

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

Results in Engineering, Год журнала: 2024, Номер 23, С. 102637 - 102637

Опубликована: Июль 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.

Язык: Английский

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

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

Опубликована: Март 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.

Язык: Английский

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Prediction of mechanical properties of eco-friendly concrete using machine learning algorithms and partial dependence plot analysis

Smart Construction and Sustainable Cities, Год журнала: 2025, Номер 3(1)

Опубликована: Янв. 26, 2025

Язык: Английский

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Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e03018 - e03018

Опубликована: Март 2, 2024

Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the concrete. Nevertheless, complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints comprehensively capturing intricate compositions. Thus, posing challenges delivering precise dependable estimations. Therefore, current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), conjunction experimental investigation effect integration graphene nanoplatelets (GNPs) on electrical resistivity (ER) compressive (CS) containing GNPs. Concrete GNPs demonstrated an fractional change (FCR) strength. measures depict that enhancement was notable at GNP concentrations 0.05% 0.1%, showcasing increases 13.23% 16.58%, respectively. Simultaneously, highest observed FCR reached -12.19% -13%, prediction efficacy proved be outstanding forecasting characteristics For CS, GEP, ANN, ANFIS impressive correlation coefficient (R) values 0.974, 0.963, 0.954, resistivity, exhibited high R-values 0.999, 0.995, 0.987, comparative analysis revealed GEP model delivered predictions for ER CS. mean absolute error (MAE) GEP-CS 14.51% reduction compared ANN-CS substantial 48.15% improvement over ANFIS-CS model. Similarly, displayed MAE 38.14% lower Moreover, GEP-ER 56.80% 82.47% Shapley Additive explanation (SHAP) provided curing age SHAP score. indicating its predominant contribution CS prediction. In predicting ER, content score, signifying estimation. This highlights ML's accuracy properties nanoplatelets, offering fast cost-effective alternative time-consuming experiments.

Язык: Английский

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Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures

Materials Today Communications, Год журнала: 2024, Номер 39, С. 108832 - 108832

Опубликована: Апрель 6, 2024

Язык: Английский

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Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04209 - e04209

Опубликована: Янв. 1, 2025

Язык: Английский

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Ensemble machine learning algorithms for efficient prediction of compressive strength of concrete containing tyre rubber and brick powder

Cleaner Waste Systems, Год журнала: 2025, Номер unknown, С. 100236 - 100236

Опубликована: Фев. 1, 2025

Язык: Английский

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A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete

Construction and Building Materials, Год журнала: 2024, Номер 440, С. 137370 - 137370

Опубликована: Июль 16, 2024

Язык: Английский

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