Forecasting bearing capacity of the mixed soil using artificial neural networking

Abstract. The bearing capacity of soil changes owing to the mechanical properties of the soil and influences on structural stability. In most of the geotechnical engineering projects, there are several soil mechanic experiments, they need interpretation before application. The mechanical properties of soil interaction make complex predict of soil bearing capacity. However, to enhancement safety of construction project need to the interpretation of soil experiments and design results for proper application in a geotechnical engineering project. In this study, artificial neural networking is proposed for the evaluation of the mixed soil characteristics to forecast the safe bearing capacity of soil because of the mechanical properties of the soil interaction phenomenon. The results reveal for prediction of the safe bearing capacity, the R² and RMSE for all mechanical properties effects on safe bearing capacity are 0.98 and 0.02, these values can provide a suitable accuracy for prediction safe bearing capacity of the mixed soil. The higher inaccuracy obtained when only the influence of single mechanical property on the mixed soil considered in prediction of the safe bearing capacity. This study supports the enhancement of geotechnical engineering design quality through prediction safe bearing capacity from characterized mechanical properties of the soil.