Predicting compressive strength of cement-stabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity

The quality monitoring technique for Cement stabilised earth blocks (CSEBs) is so challenging that it is often neglected. This study has investigated the possibility of using machine learning to predict the compressive strength of CSEBs based on cement content, electrical resistivity and Ultrasonic pulse velocity (UPV) as a potential way to enhance quality control. The study considered three types of soil and different cement content in the preparation of CSEBs with 10 different cement-soil mixtures. Various machine learning models were proposed to predict the compressive strength of CSEBs. The models were evaluated using 180 experimental datasets, and the best model for predicting the compressive strength of CSEBs was selected. The ANN and BTR models performed better than the other machine learning models tested in this study for predicting the compressive strength of CSEBs. The results show that a combination of cement content, electrical resistivity and UPV can be used to assess the quality of CSEBs more accurately, which can contribute to the knowledge base and be applied in the real world. Materials scientists and engineers can use reliable predictive models to assess the strength properties of both new and old brick structures without damage or loss of use.

Automated summary

This study investigates the use of machine learning to predict the compressive strength of Cement stabilised earth blocks (CSEBs) in order to enhance quality control. Different types of soil and cement content were considered, and various machine learning models were proposed. The results show that a combination of cement content, electrical resistivity, and Ultrasonic pulse velocity (UPV) can accurately assess the quality of CSEBs.