Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI).

ABSTRACT: The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including Multi Expression Programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc. A dataset of 159 experimental results was collected from published literature for this purpose. The collected dataset consisted of four input parameters including strength of masonry units ( f b ), height-to-thickness ratio (h/t), strength of mortar ( f m ), and ratio of f m /

SUBMITTER: Bin Inqiad W 

PROVIDER: S-EPMC11402141 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature