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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.

来源平台：GEOMECHANICS AND ENGINEERING