Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis.

ABSTRACT: The increasing demand for sustainable construction materials has led to the incorporation of Palm Oil Fuel Ash (POFA) into concrete to reduce cement consumption and lower CO₂ emissions. However, predicting the compressive strength (CS) of POFA-based concrete remains challenging due to the variability of input factors. This study addresses this issue by applying advanced machine learning models to forecast the CS of POFA-incorporated concrete. A dataset of 407 samples was collected, including six input parameters: cement content, POFA dosage, water-to-binder ratio, aggregate ratio, superplasticizer content, and curing age. The dataset was divided into 70% for training and 30% for testing. The models evaluated include Hybrid XGB-LGBM, ANN, Bagging, LSSVM, GEP, XGB and LGBM. The performance o

SUBMITTER: Ali T 

PROVIDER: S-EPMC11811126 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature