Soft computing models for prediction of bentonite plastic concrete strength

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

Опубликована: Авг. 5, 2024

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite added to mixes for adsorption toxic metals. The modified design BPC, as compared normal concrete, requires a reliable tool predict its strength. Thus, this study presents novel attempt at application two innovative evolutionary techniques known multi-expression programming (MEP) gene expression (GEP) boosting-based algorithm AdaBoost 28-day compressive strength ( ) BPC based on mixture composition. MEP GEP algorithms expressed their outputs form an empirical equation, while failed do so. were trained using dataset 246 points gathered from published literature having six important input factors predicting. developed models subject error evaluation, results revealed that all satisfied suggested criteria had correlation coefficient (R) greater than 0.9 both training testing phases. However, surpassed terms accuracy demonstrated lower RMSE 1.66 2.02 2.38 GEP. Similarly, objective function value was 0.10 0.176 0.16 MEP, which indicated overall good performance techniques. Shapley additive analysis done model gain further insights into prediction process, cement, coarse aggregate, fine aggregate are most predicting BPC. Moreover, interactive graphical user interface (GUI) has been be practically utilized civil engineering industry

Язык: Английский

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Utilizing contemporary machine learning techniques for determining soilcrete properties

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 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

Язык: Английский

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Predicting natural vibration period of concrete frame structures having masonry infill using machine learning techniques

Journal of Building Engineering, Год журнала: 2024, Номер 96, С. 110417 - 110417

Опубликована: Авг. 10, 2024

Язык: 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, Год журнала: 2024, Номер 22, С. e04112 - e04112

Опубликована: Дек. 11, 2024

Язык: Английский

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Forecasting residual mechanical properties of hybrid fibre-reinforced self-compacting concrete (HFR-SCC) exposed to elevated temperatures

Heliyon, Год журнала: 2024, Номер 10(12), С. e32856 - e32856

Опубликована: Июнь 1, 2024

The use of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) has escalated recently due to its significant advantages in contrast normal such as increased ductility, crack resistance, and eliminating the need for compaction etc. process determining residual strength properties HFR-SCC after a fire event requires rigorous experimental work extensive resources. Thus, this study presents novel approach develop equations reliable prediction compressive (cs) flexural (fs) using gene expression programming (GEP) algorithm. models were developed data obtained from internationally published literature having eight inputs including water-cement ratio, temperature, fibre content two output parameters i.e., cs fs. Also, different statistical error metrices like mean absolute (MAE), coefficient determination (R2) objective function (OF) employed assess accuracy equations. evaluation external validation both approved suitability predict strengths. sensitivity analysis was performed on which revealed that superplasticizer are some main contributors strength.

Язык: Английский

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Impact of fine lightweight aggregates and coal waste on structural lightweight concrete: Experimental study and gene expression programming

Structures, Год журнала: 2024, Номер 63, С. 106397 - 106397

Опубликована: Апрель 12, 2024

Язык: Английский

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Advances and Applications of Carbon Capture, Utilization, and Storage in Civil Engineering: A Comprehensive Review

Energies, Год журнала: 2024, Номер 17(23), С. 6046 - 6046

Опубликована: Дек. 1, 2024

This paper thoroughly examines the latest developments and diverse applications of Carbon Capture, Utilization, Storage (CCUS) in civil engineering. It provides a critical analysis technology’s potential to mitigate effects climate change. Initially, comprehensive outline CCUS technologies is presented, emphasising their vital function carbon dioxide (CO2) emission capture, conversion, sequestration. Subsequent sections provide an in-depth capture technologies, utilisation processes, storage solutions. These serve as foundation for architectural framework that facilitates design integration efficient systems. Significant attention given inventive application building construction industry. Notable examples such include using (C) cement promoting sustainable production. Economic analyses financing mechanisms are reviewed assess commercial feasibility scalability projects. In addition, this review technological advances innovations have occurred, providing insight into future course progress. A environmental regulatory environments conducted evaluate compliance with policies technology deployment. Case studies from real world provided illustrate effectiveness practical applications. concludes by importance continued research, policy support, innovation developing fundamental component engineering practices. tenacious stride toward neutrality underscored.

Язык: Английский

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Application of carbon nanomaterials (CNMs) in ultra-high-performance concrete (UHPC): A review

Journal of Industrial and Engineering Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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Enhanced high-resolution structural crack detection using hybrid interacting Particle-Kalman filter

Structures, Год журнала: 2024, Номер 62, С. 106227 - 106227

Опубликована: Март 21, 2024

Язык: Английский

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Compressive strength prediction of nano-modified concrete: A comparative study of advanced machine learning techniques

AIP Advances, Год журнала: 2024, Номер 14(7)

Опубликована: Июль 1, 2024

This study aims to develop predictive models for accurately forecasting the uniaxial compressive strength of concrete enhanced with nanomaterials. Various machine learning algorithms were employed, including backpropagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGB), and a hybrid ensemble stacking method (HEStack). A comprehensive dataset containing 94 data points nano-modified was collected, eight input parameters: water-to-cement ratio, carbon nanotubes, nano-silica, nano-clay, nano-aluminum, cement, coarse aggregates, fine aggregates. To evaluate performance these models, tenfold cross-validation case prediction conducted. It has been shown that HEStack model is most effective approach precisely predicting properties concrete. During cross-validation, found have superior accuracy resilience against overfitting compared stand-alone models. underscores potential algorithm in enhancing performance. In study, predicted results assessed using metrics such as coefficient determination (R2), mean absolute percentage error (MAPE), root square (RMSE), ratio RMSE standard deviation observations (RSR), normalized bias (NMBE). The achieved lowest MAPE 2.84%, 1.6495, RSR 0.0874, NMBE 0.0064. addition, it attained remarkable R2 value 0.9924, surpassing scores 0.9356 0.9706 0.9884 indicating its exceptional generalization capability.

Язык: Английский

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