Main Article Content
Using agricultural and industrial waste such as bagasse ash, groundnut shell ash and coal ash in stabilizing expansive soils are used as a subgrade material to reduce harmful impaction of swelling/shrinkage of expansive soils, reduce construction costs. It is also a solution for environmental protection. California Bearing Ratio (CBR) is an important criterion to evaluate the application technique of stabilized expansive soil such as road construction, building construction, highway construction, airport construction, etc. Using the traditional method such as experimental methods or empirical approach, the estimation of CBR of stabilized expansive soils is costly, time consuming for the experiment or low accuracy for empirical method. In this investigation, open-source code of Machine Learning technique Light Gradient Boosting Machine algorithm is introduced to predict the CBR. In order to build model, data of 207 experimental samples was synthesized from the literature to create a database.
The database consists of 6 input variables (ash content, ash type, liquid limit LL, plastic limit PL, optimum moisture content OMC and maximum dry density MDD) to obtain output variable CBR. The results show that the LightGBM model can successfully predict the CBR of stabilized expansive soils with high accuracy. The ash content is the most important input factor for CBR prediction using LightGBM model. In order of importanc input factor affecting CBR prediction are ash content, MDD, ash type, OMC, LL, PL.