Recent years have witnessed a burgeoning interest in sustainable, eco-friendly, and cost-effective construction materials for civil engineering projects. Soilcrete, an innovative blend of soil and cement, has gained significant acclaim for its versatility and effectiveness. It serves not only as grout for soil stabilization in corrosive environments like landfills and coastal regions but also as a reliable material for constructing structural elements. Understanding the mechanical properties of soilcrete is crucial, yet traditional laboratory tests are prohibitively expensive, time-consuming, and often imprecise. Machine learning (ML) algorithms present a superior alternative, offering efficiency and accuracy. This research focuses on the application of the adaptive neuro-fuzzy inference system (ANFIS) algorithm to predict the uniaxial compressive strength (UCS) of soilcrete. A total of 300 soilcrete specimens, crafted from two types of soil (clay and limestone) and enhanced with metakaolin as a pozzolanic additive, were meticulously prepared and tested. The dataset was divided, with 80% used for training and 20% for testing the model. Eight parameters were identified as key determinants of soilcrete's UCS: soil type, metakaolin content, superplasticizer content, shrinkage, water-to-binder ratio, binder type, ultrasonic velocity, and density. The analysis demonstrated that the ANFIS algorithm could predict the UCS of soilcrete with remarkable accuracy. By combining laboratory results with ANFIS model predictions, the study identified the optimal conditions for maximizing soilcrete's UCS: 11% metakaolin content, a 0.45 water-to-binder ratio, and 1% superplasticizer content.

Soil in combination with cement, more commonly referred to as soilcrete, has gained great popularity within the construction sector. To this end, its mechanical properties have to be determined quickly and accurately. Unfortunately, the conventional methods for determining them include lab tests which are rather expensive and error prone. A better solution however comes in the form of machine learning (ML) algorithms which have tremendous potential. Hence, this study sought to analyze how efficient the three algorithms in predicting the uniaxial compressive strength (UCS) of soilcrete. 400 samples of soilcrete were manufactured and analyzed, using two types of soil including clay and limestone along with metakaolin which served as a mineral additive. A total of 80% of the dataset was made use of for training while the remaining 20% served a testing purpose, in addition to the 37 datasets which were specifically designed for evaluation purposes. A Stepwise procedure was completed and a total of 8 parameters were identified including metakaolin and soil type, super plasticizer content, water to binder ratio, shrinkage, binder density and finally ultrasonic velocity. Most of the algorithms were able to achieve satisfactory results however Gaussian process regression (GPR), support vector regression (SVR) and null-space SVR (NuSVR) were able to stand out due to their potential performance. Focusing on the trained models and lab tests that were done, this research managed to establish the proper superplasticizer constitution (1%), water-to-binder ratio (0.4) and metakaolin content (12%) with the goal of achieving the highest UCS value in the provided soilcrete specimens. Furthermore, a graphical user interface (GUI) was created based on the trained ML models. For the civil engineers and researchers who need to estimate the UCS of the soilcrete specimens, this GUI greatly simplifies the process.

Soil-cement is gaining acceptance in the construction industry for use in the improvement of sandy soil, despite its low strength Research has attempted to increase the strength of this material by increasing the percentage of additives. The current study investigated the effect of steel fibre (SF) as a reinforcer on the performance, UCS and TS at different fibre contents, lengths, diameters and shapes. The results showed that the use of 2% straight fibre significantly increased the UCS and that the samples performed better than those containing hooked or crimped. A decrease in the SF length from 10 to 5 mm and increase in the diameter from 0.3 to 0.6 mm caused decreases in the UCS. The greatest increase in TS occurred with the addition of 2% hooked fibres and was 4.6 times the increase in strength without fibres. The reason for the increase in the strength of the samples was bridge-like performance of the SFs in the soil-cement. The use of SFs together with cement to improve sandy soil is a new and effective way of improving the mechanical behaviour of the soil. This indicates that the addition of SFs can be a step towards more optimal use of soil-cement in engineering projects.

Soilcrete is a composite material of soil and cement that is highly valued in the construction industry. Accurate measurement of its mechanical properties is essential, but laboratory testing methods are expensive, timeconsuming, and include inaccuracies. Machine learning (ML) algorithms provide a more efficient alternative for this purpose, so after assessment with a statistical extraction method, ML algorithms including back-propagation neural network (BPNN), K-nearest neighbor (KNN), radial basis function (RBF), feed-forward neural networks (FFNN), and support vector regression (SVR) for predicting the uniaxial compressive strength (UCS) of soilcrete, were proposed in this study. The developed models in this study were optimized using an optimization technique, gradient descent (GD), throughout the analysis (direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters). After doing laboratory analysis, data pre-preprocessing, and data-processing analysis, a database including 600 soilcrete specimens was gathered, which includes two different soil types (clay and limestone) and metakaolin as a mineral additive. 80% of the database was used for the training set and 20% for testing, considering eight input parameters, including metakaolin content, soil type, superplasticizer content, water-to-binder ratio, shrinkage, binder, density, and ultrasonic velocity. The analysis showed that most algorithms performed well in the prediction, with BPNN, KNN, and RBF having higher accuracy compared to others (R2 = 0.95, 0.95, 0.92, respectively). Based on this evaluation, it was observed that all models show an acceptable accuracy rate in prediction (RMSE: BPNN = 0.11, FFNN = 0.24, KNN = 0.05, SVR = 0.06, RBF = 0.05, MAD: BPNN = 0.006, FFNN = 0.012, KNN = 0.008, SVR = 0.006, RBF = 0.009). The ML importance ranking-sensitivity analysis indicated that all input parameters influence the UCS of soilcrete, especially the water-to-binder ratio and density, which have the most impact.

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.