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.
The success of any pavement system is depending on the strength of the subgrade layer that represents a foundation on which unbound and surface course layers are placed. The strength of the subgrade layer is often defined in terms of a subgrade reaction modulus (Ks) which is typically obtained from the static plate load test (PLT). The PLT test is known to be laborious, time-consuming and relatively expensive, therefore several alternative methodologies for predicting (Ks) are required. The objective of this research is developing a 3D-finite element model using Plaxis 3D software to simulate the plate load tests, and comparing the finite element results with those obtained from experimental tests. Twenty-seven plate load tests were carried out on three different types of subgrade soils. The soils collected from different sites in Kerbala city and tested under static load under three degrees of compaction. The experimental results were verified numerically using the finite element method. In the numerical simulation, the Mohr-Coulomb model was used to represent the behavior of soil. The numerical and experimental results were analyzed and compared. The results showed a good agreement with experimental work, also showed the possibility of using Plaxis 3D in the simulation of the static plate load test.
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