Combined Influence of Crushed Brick Powder and Recycled Concrete Aggregate on the Mechanical, Durability and Microstructural Properties of Eco-Concrete: An Experimental and Machine Learning-Based Evaluation DOI Creative Commons
Md. Habibur Rahman Sobuz, M.H. Khan, Md. Rakibul Islam

и другие.

Journal of Materials Research and Technology, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Comparative Performance Evaluation of Quartz and Snail Shell Powders Modified Concrete: Mechanical, Machine Learning, and Microstructural Assessments DOI Creative Commons
Md. Habibur Rahman Sobuz,

Md. Kanan Chowdhury Tilak,

SM Arifur Rahman

и другие.

Journal of Materials Research and Technology, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Evaluating mechanical and environmental impacts of sustainable natural fiber reinforced recycled aggregate concrete incorporating supervised machine learning methods DOI
Rahat Aayaz, Md. Habibur Rahman Sobuz,

Md. Kawsarul Islam Kabbo

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

Abstract Construction industry is increasingly focusing on sustainable decarbonized concrete using alternative materials. One promising innovation natural fiber recycled aggregate (NFRAC), which combines aggregates with fibers to enhance performance and reduce the carbon footprint from quarrying production. This study analyzes 534 data points existing literature predict compressive strength of NFRAC made jute, sisal, kenaf, ramie, coir, bamboo, across varying water-cement ratios curing ages. Five machine learning models: eXtreme Gradient Boosting (XGB), Random Forest (RF), Light Machine (LGBM), Multilayer Perceptron (MLP), Categorical (CAT), were employed strength, hyperparameters optimized Particle Swarm Optimization (PSO). SHapley Additive exPlanations (SHAP) partial dependency plots (PDP) assessed impact key factors, showing that water-binder ratio significantly affects strength. The XGB model achieved best results an RMSE 4.2 MPa R² 0.94. Life cycle analysis indicated 25% (RCA) reduces embodied CO₂ emissions by 2.7%; 50% RCA replacement, reduction reaches 5.4%. A cost-benefit revealed offers significant economic advantages over traditional concrete, particularly at higher replacement rates. validated models established a user-friendly web interface for predicting integration advanced learning, analysis, evaluation highlights potential adopting in construction, effectively mitigating environmental impact.

Язык: Английский

Процитировано

0

Combined Influence of Crushed Brick Powder and Recycled Concrete Aggregate on the Mechanical, Durability and Microstructural Properties of Eco-Concrete: An Experimental and Machine Learning-Based Evaluation DOI Creative Commons
Md. Habibur Rahman Sobuz, M.H. Khan, Md. Rakibul Islam

и другие.

Journal of Materials Research and Technology, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0