Predicting load-settlement relationship of driven piles in sand and mixed soils using artificial neural networks - Alkroosh, Iyad; Shahin, Mohamed; Nikraz, Hamid (CI-Premier, 2010)
An accurate prediction of pile behaviour under axial loads is necessary for safe and cost effective design. This paper presents the development of a new model, based on artificial neural networks (ANNs), to predict the load-settlement relationship of driven piles in sand and mixed soils, and subjected to axial loads. ANNs have been recently applied to many geotechnical engineering problems and have shown to provide high degree of success. Two models are developed; one for steel piles and the other for concrete piles. The data used for ANN models development are collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. Predictions from the ANN models are compared with the results of experimental data, and statistical analysis is conducted to verify the performance of ANN models. The results indicate that ANN models perform well and able to predict the pile load-settlement relationship quite accurately.