Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Abstract

Numerous methods have been proposed to assess the axial capacity of pile foundations. Most of the methods have limitations and therefore cannot provide consistent and accurate evaluation of pile capacity. However, in many situations, the methods that correlate cone penetration test (CPT) data and pile capacity have shown to provide better results, because the CPT results provide more reliable soil properties. In an attempt to obtain more accurate correlation of CPT data with axial pile capacity, gene expression programming (GEP) technique is used in this study. The GEP is a relatively new artificial intelligent computational technique that has been recently used with success in the field of engineering. Three GEP models have been developed, one for bored piles and two other models for driven piles (a model for each of concrete and steel piles). The data used for developing the GEP models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. For each GEP model, the data are divided into a training set for model calibration and an independent validation set for model verification. The performances of the GEP models are evaluated by comparing their results with experimental data and the robustness of each model is investigated via sensitivity analyses. The performances of the GEP models are evaluated further by comparing their results with the results of number of currently used CPT-based methods. Statistical analyses are used for the comparison. The results indicate that the GEP models are robust and perform well.

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