Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models

Structures, Год журнала: 2025, Номер 71, С. 108138 - 108138

Опубликована: Янв. 1, 2025

Язык: Английский

---

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

---

Hybrid Machine Learning Based Strength and Durability Predictions of Polypropylene Fiber-Reinforced Graphene Oxide Based High-Performance Concrete

Iranian Journal of Science and Technology Transactions of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 19, 2025

Язык: Английский

---

Prediction of Bond Strength between Fibers and the Matrix in UHPC Utilizing Machine Learning and Experimental Data

Materials Today Communications, Год журнала: 2024, Номер unknown, С. 111136 - 111136

Опубликована: Ноя. 1, 2024

Язык: Английский

---

Study on hydrophobic characteristics and mechanism of modified biomass-coal mixed combustion ash geopolymer sustainable backfill

Sustainable Chemistry and Pharmacy, Год журнала: 2024, Номер 43, С. 101883 - 101883

Опубликована: Дек. 10, 2024

Язык: Английский

---