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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High-Strength Self-Compacting Concrete Production Incorporating Supplementary Cementitious Materials: Experimental Evaluations and Machine Learning Modelling

International Journal of Concrete Structures and Materials, Год журнала: 2024, Номер 18(1)

Опубликована: Сен. 30, 2024

Язык: Английский

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Multi-performance optimization of low-carbon geopolymer considering mechanical, cost, and CO2 emission based on experiment and interpretable learning

Construction and Building Materials, Год журнала: 2024, Номер 425, С. 136013 - 136013

Опубликована: Апрель 1, 2024

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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Experimental assessment and hybrid machine learning-based feature importance analysis with the optimization of compressive strength of waste glass powder-modified concrete

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112081 - 112081

Опубликована: Март 1, 2025

Язык: Английский

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Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning

Journal of Composites Science, Год журнала: 2024, Номер 8(8), С. 287 - 287

Опубликована: Июль 26, 2024

High-Performance Concrete (HPC) and Ultra-High-Performance (UHPC) have many applications in civil engineering industries. These two types of concrete as similarities they differences with each other, such the mix design additive powders like silica fume, metakaolin, various fibers, however, optimal percentages mixture properties element these concretes are completely different. This study investigated between to find better mechanical behavior through parameters concrete. In addition, this paper studied correlation matrix machine learning method predict relationship elements properties. way, Linear, Ridge, Lasso, Random Forest, K-Nearest Neighbors (KNN), Decision tree, Partial least squares (PLS) regressions been chosen best regression types. To accuracy, coefficient determination (R2), mean absolute error (MAE), root-mean-square (RMSE) were selected. Finally, PLS, Lasso had results than other regressions, R2 greater 93%, 92%, respectively. general, present shows that HPC UHPC different designs for predicting

Язык: Английский

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NSGA-II based short-term building energy management using optimal LSTM-MLP forecasts

International Journal of Electrical Power & Energy Systems, Год журнала: 2024, Номер 159, С. 110070 - 110070

Опубликована: Июнь 3, 2024

To conduct analysis on the field of electricity management in buildings is crucial to contribute clean energy promotion, efficiency, and resilience against climate change. This manuscript proposes a methodology for modeling predictive calibrated system (EMS) using hybrid that combines long short-term memory multilayer perceptron models (LSTM-MLP) optimized by non-dominated sorting genetic algorithm II (NSGA-II). The proposed approach utilizes global forecast (GFS) data anticipate consumption fluctuations optimize use distributed sources, such as photovoltaic (PV) production, based knowledge prices free market one day ahead. trade-off building conducted with NSGA-II, guaranteeing exploration exploitation while minimizing costs wastes. research carried out demonstrates effectiveness LSTM-MLP model advantages NSGA-II hyperparameter tuning balance sustainable practices. tested an existing building, Industrial Engineering School located Campus Lagoas-Marcosende Universidade de Vigo, Spain.

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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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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Utilization of Industrial, Agricultural, and Construction Waste in Cementitious Composites: A Comprehensive Review of their Impact on Concrete Properties and Sustainable Construction Practices.

Materials Today Sustainability, Год журнала: 2025, Номер unknown, С. 101080 - 101080

Опубликована: Янв. 1, 2025

Язык: Английский

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