Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms

Structural Concrete, Год журнала: 2024, Номер unknown

Опубликована: Май 19, 2024

Abstract This paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting compressive strength ultra‐high‐performance concrete (UHPC). Phasor particle swarm (PPSO), dwarf mongoose (DMO), and atom search (ASO), which have been very popular recently, are preferred as algorithms. A comprehensive reliable data set is used to develop models, include 785 test results with 15 input features. The performance (PPSO‐CatBoost, DMO‐CatBoost, ASO‐CatBoost) optimized different algorithms thoroughly assessed by means statistical metrics error analysis determine model best capability, this compared obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) ensure interpretability overcome “black box” problem machine learning (ML) models. demonstrate that all outstandingly forecast UHPC. Among these DMO‐CatBoost stands out other in metrics, such high coefficient determination ( R 2 ) values, low root mean squared (RMSE), absolute percentage (MAPE), (MAE) along a smaller ratio. words, RMSE, , MAPE, MAE values training 3.67, 0.993, 0.019, 2.35, respectively, whereas those 6.15, 0.978, 0.038, 4.51. Additionally, ranking optimize hyperparameters follows: DMO > PPSO ASO. On hand, SHAP showed age, fiber dosage, cement dosage significantly influence These findings can guide structural engineers design UHPC, thus assisting them developing strategies improve properties material. Finally, based developed work, graphical user interface has easily UHPC practical applications without additional tools or software.

Язык: Английский

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AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

Materials Today Communications, Год журнала: 2024, Номер 40, С. 109915 - 109915

Опубликована: Июль 22, 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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Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03373 - e03373

Опубликована: Июнь 7, 2024

Copper mining produces significant amounts of copper mine tailings (CMT), necessitating appropriate waste handling and disposal practices. By substituting a portion cement with CMT as supplementary cementitious materials (SCMs), we aim to address two environmental issues simultaneously: reducing in landfills decreasing embodied carbon by using less cement. The exploration recycling replacement requires evaluation its impact on material performance, such compressive strength. In this paper, machine learning that features data fusion large public our own small strength CMT-incorporated We developed critically evaluated three models: simple linear model, Gaussian process, random forest predict the pastes different mix designs (e.g., varying water-binder ratios) curing ages. Hyperparameters model were tuned Bayesian optimization. Following comprehensive models, find can accurately estimate paste across designs. Furthermore, results from SHapley Additive exPlanation (SHAP), Individual Conditional Expectation (ICE), Partial Dependence Plots (PDP) revealed cement, ground-granulated blast furnace slag, superplasticizers, ages positively influence This study contributes acceleration sustainable technology obtain best design desired

Язык: Английский

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Applications of machine learning methods for design and characterization of high-performance fiber-reinforced cementitious composite (HPFRCC): a review

Journal of Sustainable Cement-Based Materials, Год журнала: 2025, Номер unknown, С. 1 - 24

Опубликована: Фев. 6, 2025

Язык: Английский

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Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 10, 2025

The increasing demand for sustainable construction materials has led to the incorporation of Palm Oil Fuel Ash (POFA) into concrete reduce cement consumption and lower CO₂ emissions. However, predicting compressive strength (CS) POFA-based remains challenging due variability input factors. This study addresses this issue by applying advanced machine learning models forecast CS POFA-incorporated concrete. A dataset 407 samples was collected, including six parameters: content, POFA dosage, water-to-binder ratio, aggregate superplasticizer curing age. divided 70% training 30% testing. evaluated include Hybrid XGB-LGBM, ANN, Bagging, LSSVM, GEP, XGB LGBM. performance these assessed using key metrics, coefficient determination (R2), root mean square error (RMSE), normalized means (NRMSE), absolute (MAE) Willmott index (d). XGB-LGBM model achieved maximum R2 0.976 lowest RMSE, demonstrating superior accuracy, followed ANN with an 0.968. SHAP analysis further validated identifying most impactful factors, ratio emerging as influential. These predictive offer industry a reliable framework evaluating concrete, reducing need extensive experimental testing, promoting development more eco-friendly, cost-effective building materials.

Язык: Английский

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AI-Powered Optimization of Engineered Cementitious Composites Properties and CO₂ Emissions for Sustainable Construction

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04405 - e04405

Опубликована: Фев. 1, 2025

Язык: Английский

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Metaheuristic-driven CatBoost model for accurate seepage loss prediction in lined canals

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(5)

Опубликована: Март 25, 2025

Язык: Английский

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Concrete compressive strength classification using hybrid machine learning models and interactive GUI

Innovative Infrastructure Solutions, Год журнала: 2025, Номер 10(5)

Опубликована: Апрель 28, 2025

Abstract Concrete Compressive Strength (CCS) is a critical parameter in structural engineering, influencing durability, safety, and load-bearing capacity. This study explores the classification of CCS using hybrid Machine Learning (ML) techniques an interactive Graphical User Interface (GUI). Advanced ML algorithms: Random Forest (RF), Adaptive-Boosting (AdaBoost), Extreme-Gradient-Boosting (XGBoost), Light-Gradient Boosting (LightGBM), Categorical-Boosting (CatBoost) were applied to categorize strength into Low, Normal, High classes. The dataset, comprising 1298 samples, was split 80% training 20% testing for evaluation. Hyperparameter tuning Bayesian Optimization with fivefold stratified cross-validation, resulting greatly improved model’s performance. Results showed that LightGBM achieved highest accuracy, scores 0.931 (Low), 0.865 (Normal), 0.935 (High), corresponding area under curve values 0.967, 0.938, 0.981. CatBoost also performed well, particularly Normal classes, while XGBoost slight overfitting class. RF AdaBoost had acceptable performance but struggled boundary cases. To interpret model predictions, SHapley-Additive-exPlanations (SHAP) analysis used. Curing duration cement content most influential factors across all water superplasticizer played secondary roles. Coarse aggregate became more significant High-Strength (HSC). A GUI developed allow practitioners input data receive real-time classifications, bridging gap between machine learning practical applications concrete design.

Язык: Английский

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A metaheuristic Multi-Objective optimization of energy and environmental performances of a Waste-to-Energy system based on waste gasification using particle swarm optimization

Energy Conversion and Management, Год журнала: 2024, Номер 317, С. 118844 - 118844

Опубликована: Авг. 6, 2024

Язык: Английский

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