Soilcrete is an innovative construction material made by combining naturally occurring earth materials with cement. It can be effectively used in areas where other construction materials are not readily available due to financial or environmental reasons since soilcrete is made from readily available natural clay. It can also help to cut down the greenhouse gas emissions from the construction industry by encouraging the use of resources that are locally available. Thus, it is imperative to reliably predict different properties of soilcrete since the accurate determination of these properties is crucial for the widespread use of soilcrete materials. However, the laboratory determination of these properties is subjected to significant time and resource constraints. As a result, this research was undertaken to provide empirical prediction models for the density, shrinkage, and strain of soilcrete mixes using two machine learning algorithms: Gene Expression Programming (GEP) and Extreme Gradient Boosting (XGB). The analysis revealed that XGB-based predictions correlated more with real-life values than GEP having training R2=0.999\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}{2}=0.999$$\end{document} for both density and shrinkage prediction and R2=0.944\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}{2}=0.944$$\end{document} for strain prediction. Moreover, several explanatory analyses including individual conditional expectation (ICE) analysis and shapely analysis were done on the XGB model which showed that water-to-binder ratio, metakaolin content, and modulus of elasticity are some of the most important variables for forecasting soilcrete materials properties. Furthermore, an interactive graphical user interface (GUI) has been developed for effective utilization in civil engineering industry to forecast these properties of soilcrete materials.

This study aims to predict compressive strength (CS) and modulus of elasticity (E) of soilcrete mixes to foster their widespread use in the industry. Soilcfigrete has the potential to promote sustainable construction practices by making use of locally available raw materials. However, the accurate determination of mechanical properties of soilcrete mixes is inevitable to foster their widespread use. Thus, this study employs different machine learning algorithms including Extreme Gradient Boosting (XGB), Gene Expression Programming (GEP), AdaBoost, and Multi Expression Programming (MEP) for this purpose. The XGB and AdaBoost algorithms were implemented using python programming language while MEP and GEP were implemented using specialized softwares. The data used for model development was obtained from previously published literature containing five input parameters and two output parameters. This data was split into two sets named training and testing sets for training and testing of the algorithms respectively. The developed models for CS and E prediction were validated using several error metrices and residual comparison. The objective function value which should be closer to zero for an accurate model is the least for XGB model for prediction of both variables (0.0036 for CS and 0.00315 for E). Moreover, shapley analysis was carried out using XGB model to get insights into the underlying model framework. The results highlighted that water-to-binder ratio (W/B), metakaolin (MK), and ultrasonic pulse velocity (UV) are the most significant variables for predicting E and CS of soilcrete materials. These insights can be used practically to optimize the mixture composition of soilcrete mixes according to different site requirements.