Compressive strength of concrete material using machine learning techniques

Cleaner Engineering and Technology, Год журнала: 2023, Номер 15, С. 100661 - 100661

Опубликована: Июль 20, 2023

Significant efforts have been made to improve the strength of concrete by utilizing industrial waste like Fly Ash as a partial replacement cement in concrete. However, predicting compressive is one challenging tasks since it affected several factors such shape and size aggregates, water-cement ratio. The paper presents study on various investigation machine learning (ML) algorithms estimate (CS) containing fly ash (FA). research also aims compare accuracy different ML models, including non-ensemble models (Multiple Linear Regressor, Support Vector Regressor) ensemble (AdaBoost Random Forest Regression, XGBoost Bagging Regressor), CS with focus identifying most accurate estimation method. For this purpose, dataset 633 experimental results wide range values, ranging from 6.27 MPa 79.99 MPa, was collected existing literature validated using statistical analysis. primary input parameters for included quantities cement, fine aggregate (FA), coarse aggregates (CA), water content, percentage superplasticizer, curing days, output. Performance evaluation conducted performance indices, MAE, MSE, R2, MAPE, RMSE, a20-index, assess reliability. comparison reveals that Regressor reliable model, demonstrating highest coefficient determination (R2) 0.95, a-20 index 0.913, lowest RMSE value 3.06 MAE 2.13 while Multiple LR model least method R2 equal 0.52, 0.433, 9.40 7.68 MPa. Additionally, provide deeper insights into relationship between CS, sensitivity parametric analysis were employed, enabling comprehensive understanding impact other prediction. From study, observed age essential feature, followed water, information gain values 32.91, 23.50, 15.10, respectively. highlights effectiveness techniques, particularly accurately estimating Furthermore, offers researchers faster more cost-effective means evaluating effect estimation, avoiding need time-consuming costly studies.

Язык: Английский

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RETRACTED: Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with Nano-silica: Experimental and computational investigation

Construction and Building Materials, Год журнала: 2023, Номер 394, С. 132266 - 132266

Опубликована: Июнь 28, 2023

Язык: Английский

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Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

Separation and Purification Technology, Год журнала: 2023, Номер 316, С. 123807 - 123807

Опубликована: Апрель 10, 2023

Язык: Английский

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

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e02901 - e02901

Опубликована: Янв. 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.

Язык: Английский

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Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Buildings, Год журнала: 2024, Номер 14(3), С. 591 - 591

Опубликована: Фев. 22, 2024

Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges its intricate cementitious matrix and vague mix design, where the components their relative amounts influence compressive strength. In response to these challenges, application of accurate applicable soft computing techniques becomes imperative for predicting strength such composite matrix. This research aimed predict using waste resources through novel ensemble ML algorithm. The dataset comprised 156 statistical samples, 15 variables were selected prediction. model employed combination RF, GWO algorithm, XGBoost. A stacking strategy was implemented by developing multiple RF models different hyperparameters, combining outcome predictions into new dataset, subsequently XGBoost model, termed RF–XGBoost model. To enhance accuracy errors, algorithm optimized hyperparameters resulting in RF–GWO–XGBoost proposed compared stand-alone models, hybrid GWO–XGBoost system. results demonstrated significant performance improvement strategies, particularly assistance exhibited better effectiveness, an RMSE 1.712 3.485, R2 0.983 0.981. contrast, (RF XGBoost) lower performance.

Язык: Английский

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Advanced modeling for predicting compressive strength in fly ash-modified recycled aggregate concrete: XGboost, MEP, MARS, and ANN approaches

Innovative Infrastructure Solutions, Год журнала: 2024, Номер 9(3)

Опубликована: Фев. 9, 2024

Язык: Английский

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Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Buildings, Год журнала: 2024, Номер 14(2), С. 396 - 396

Опубликована: Фев. 1, 2024

Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a sculpture address the issue of brittle fracture, and primary objective this study explore potential hybridizing Grey Wolf Optimizer (GWO) with four robust intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, CatBoost, anticipate compressive strength fiber-reinforced for sculptural elements. The optimization hyperparameters these techniques performed using GWO metaheuristic algorithm, enhancing accuracy through creation hybrid models: GWO-XGBoost, GWO-LightGBM, GWO-AdaBoost, GWO-CatBoost. A comparative analysis conducted between results obtained from models their conventional counterparts. evaluation is based on five key indices: R2, RMSE, VAF, MAE, bias, addressing an assessment predictive models’ performance capabilities. outcomes reveal that exhibiting R2 values (0.971 0.978) train test stages, respectively, emerges as best model estimating compared other models. Consequently, proposed GWO-XGBoost algorithm proves be efficient tool anticipating CSFrRNSC.

Язык: Английский

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ANN-ANFIS model for optimising methylic composite biodiesel from neem and castor oil and predicting emissions of the biodiesel blend

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

Опубликована: Фев. 15, 2025

Abstract Researchers and stakeholders have shown interest in heterogeneous composite biodiesel (HCB) due to its enhanced fuel properties environmental friendliness (EF). The lack of high viscosity datasets for parent hybrid oils has hindered their commercialisation. Reliable models are lacking optimise the transesterification parameters developing HCB, scarcity predictive affected climate researchers experts. In this study, basic were analysed, developed yield HCB kinematic (KV) biodiesel/neem castor seed oil methyl ester (NCSOME) using Artificial Neural Network (ANN) Adaptive Neuro Fuzzy Inference System (ANFIS). Statistical indices such as computed coefficient determination (R 2 ), root-mean-square-error (RMSE), standard error prediction (SEP), mean average (MAE), absolute deviation (AAD) used evaluate effectiveness techniques. Emission NCSOME-diesel blends also established. study investigated impact optimised types/NCSOME-diesel (10–30 vol%), ZnO nanoparticle dosage (400–800 ppm), engine speed (1100–1700 rpm), load (10–30%) on emission characteristics (EFI) carbon monoxide (CO), Oxides Nitrogen (NOx), Unburnt Hydrocarbon (UHC) Response Surface Methodology (RSM). ANFIS model demonstrated superior performance terms R , RMSE, SEP, MAE, AAD compared ANN predicting KV HCB. optimal levels CO (49.26 NO x (0.5171 UHC (2.783) achieved with a type 23.4%, 685.432 ppm, 1329.2 rpm, 10% ensure cleaner EFI. can effectively predict fuel-related improve process, while RSM be valuable tool accurate forecasting promoting environment. provide reliable information strategic planning automotive industries.

Язык: Английский

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Revealing the nature of soil liquefaction using machine learning

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 21, 2025

Язык: Английский

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Optimizing high-strength concrete compressive strength with explainable machine learning

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(3)

Опубликована: Фев. 3, 2025

Язык: Английский

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