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

作     者：Ullah, Irfan Javed, Muhammad Faisal Alabduljabbar, Hisham Ullah, Hafeez 

作者机构：Hohai Univ Dept Civil & Transportat Engn Nanjing 210098 Peoples R China GIK Inst Engn Sci & Technol Dept Civil Engn Swabi 23640 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan 

出 版 物：《STRUCTURES》 (Structures)

年 卷 期：2025年第71卷

核心收录：

主　　题：Basalt fibres Machine learning Optimization algorithms 

摘      要：Basalt fibre has recently become a popular choice for concrete reinforcement due to its superior mechanical properties and sustainable production process. This research presents novel hybrid machine learning models for predicting the compressive strength (CS) and tensile strength (TS) of basalt fibre reinforced concrete (BFRC). The study integrates support vector regression (SVR) with firefly algorithm (FFA), grey wolf optimization (GWO), and particle swarm optimization (PSO) to develop hybrid models for forecasting BFRC properties. Random forest (RF) and decision tree (DT) were also employed for comparison. SVR-PSO exhibited the strongest performance, achieving the highest coefficient of determination (R2) scores of 0.993 for CS and 0.954 for TS, surpassing SVRFFA (CS = 0.990, TS = 0.944) and SVR-GWO (CS = 0.977, TS = 0.930). The RF model achieved R2 values of 0.974 for CS and 0.918 for TS, while the DT model had R2 values of 0.865 for CS and 0.897 for TS. SHapley Additive exPlanations (SHAP) analysis revealed the water-to-cement ratio (W/C) as the most critical feature for CS, while fine aggregate (FA) was most significant for TS. Partial dependence plots (PDP) analysis indicated FC and FA negatively affect CS, whereas FC and CA positively influence TS. A user-friendly graphical user interface was developed to streamline the prediction of CS and TS, crucial for ensuring the safety and stability of buildings and bridges. Future research should consider incorporating additional input features to enhance the accuracy of CS and TS predictions for BFRC. Expanding datasets is essential for the effective implementation of deep learning algorithms. The proposed hybrid models demonstrated high efficacy in predicting CS and TS, suggesting their potential application in estimating the durability characteristics of BFRC.