Application of Artificial Neural Networks in Predicting Subbase CBR Values Using Soil Indices Data

Abstract

Abstract Subbase strength characteristics is one of the main inputs of pavement design, and such strength characteristics are normally represented by indices such as resilient modulus, dynamic modulus, and California Bearing Ratio (CBR), with the latter being a widely used index among pavement and geotechnical engineers. This paper examines the capability of Artificial Neural Networks (ANN) to develop a correlation between subbase CBR and primary soil data, which could help with estimating CBR for prediction purposes and with identifying the significance of each index with regard to subbase strength. Data were sampled from different areas in Karbala, Iraq, and a total of 358 subbase samples were used for model training and validation. The results showed that the proposed ANN model could successfully predict the CBR value using soil index data. Additionally, a sensitivity analysis was conducted to determine the importance of each contributing factor, and within the boundaries of the local subbase characteristics, the test results indicated that soluble salts were the most effective factor among soil parameters with an importance percentage of 39.46%, while the Plasticity Index (PI) was the least important factor, with a percentage of 2.06%. Based on the validity and quality of subbase soil tests, using ANN to predict CBR value may offer a suitable replacement for lengthy and expensive laboratory testing based on validated data for materials supplied from Karbala quarries.