
npj Materials Sustainability, Journal Year: 2025, Volume and Issue: 3(1)
Published: May 17, 2025
Language: Английский
npj Materials Sustainability, Journal Year: 2025, Volume and Issue: 3(1)
Published: May 17, 2025
Language: Английский
Construction and Building Materials, Journal Year: 2023, Volume and Issue: 367, P. 130339 - 130339
Published: Jan. 13, 2023
Language: Английский
Citations
72Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 80, P. 108065 - 108065
Published: Nov. 3, 2023
Language: Английский
Citations
65Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 445, P. 141045 - 141045
Published: Feb. 8, 2024
Language: Английский
Citations
57Applied Soft Computing, Journal Year: 2024, Volume and Issue: 159, P. 111661 - 111661
Published: April 23, 2024
This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion, in recycled aggregate concrete (RAC) compared to natural concrete. Traditional carbonation depth assessment methods RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There's deficiency application machine learning techniques accurately predicting RAC, gap this aims fill. Utilizing extreme gradient boosting (XGBoost) technique, recognized its efficacy ensemble learning, innovates modeling RAC. It emphasizes criticality hyperparameter optimization XGBoost algorithm maximizing model accuracy. To achieve this, three novel metaheuristic algorithms, including reptile search (RSA), Aquila optimizer (AO), arithmetic (AOA), were introduced as global optimizers tunning hyperparameters. The was underpinned by comprehensive database compiled from extensive literature, facilitating development an accurate model. Through rigorous evaluations, sensitivity analyses, Wilcoxon signed-rank test, runtime comparisons, synthesized models demonstrated exceptional accuracy, with coefficients determination exceeding 0.95. XGBoost-AO algorithm, particular, showcased superior performance, XGBoost-RSA providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions significant influencers. A user-friendly graphical user interface developed, enhancing practical utility findings progression over time. research significantly advances predictive accuracy contributing sustainable management infrastructures emphasizing integration advanced structural engineering advancements.
Language: Английский
Citations
20Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 80, P. 107977 - 107977
Published: Oct. 20, 2023
Language: Английский
Citations
30Case Studies in Construction Materials, Journal Year: 2023, Volume and Issue: 19, P. e02557 - e02557
Published: Oct. 7, 2023
Focusing on sustainable development, the demand for alternative materials in concrete, especially Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions. As Compressive Strength (CS) is dominant among concrete properties, this research concentrates developing SCC by incorporating Rice Husk Ash (RHA) Marble Powder (MP) as filler replacements, respectively, while applying Machine Learning (ML) Deep (DL) techniques forecast CS of RHA/MP-based SCC. The further evaluates material characteristics, with a strong emphasis ML DL prediction. samples various mixed ratios were cast examined after 91 days collect data model application. In experimental technique, 133 gathered, was predicted using seven input factors (cement, RHA, MP, superplasticizer, coarse aggregate, fine water) an 80:20 ratio. Various algorithms, including linear regression, ridge lasso K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), boosting methods such gradient boost (GB), XG (XGB), adaptive (ADB) are employed, along technique backpropagation neural network (BPNN) different optimizer algorithms (Adam, SGD, RMSprop) predict validated evaluation parameters R-squared (R2), mean squared error (MSE), normalized root (NRMSE), absolute (MAE), percentage (MAPE). Comparatively, ensemble BPNN Adam RMSprop optimizers demonstrate high accuracy predicting outcomes, indicated their coefficient correlation R2 values low values.
Language: Английский
Citations
27Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 88, P. 109002 - 109002
Published: March 12, 2024
Language: Английский
Citations
16Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112017 - 112017
Published: Feb. 1, 2025
Language: Английский
Citations
2Construction and Building Materials, Journal Year: 2023, Volume and Issue: 400, P. 132828 - 132828
Published: Aug. 6, 2023
Language: Английский
Citations
19International Journal of Geo-Engineering, Journal Year: 2024, Volume and Issue: 15(1)
Published: Feb. 12, 2024
Abstract The present study proposes a novel ML methodology for differentiating between unstabilized aggregate specimens and those stabilized with triangular rectangular aperture geogrids. This utilizes the compiled experimental results obtained from under repeated loading into balanced, moderate-sized database. efficacy of five models, including tree-ensemble single-learning algorithms, in accurately identifying each specimen class was explored. Shapley’s additive explanation used to understand intricacies models determine global feature importance ranking input variables. All could identify an accuracy at least 0.9. outperformed when all three classes (unstabilized by geogrids) were considered, light gradient boosting machine showing best performance—an 0.94 area curve score 0.98. According explanation, resilient modulus confining pressure identified as most important features across models. Therefore, proposed may be effectively type presence geogrid reinforcement aggregates, based on few material properties performance loading.
Language: Английский
Citations
9