Determining the pile bearing capacity use of pda test results and neural networks

Abstract

The dynamic loading or PDA test is one of the on-site experiments to estimate the bearing capacity of a pile. This test is based on the wave-propagation theory and can provide a good estimate of the bearing capacity of a pile as well as an assessment of the health of the pile. In this research, using the results of 100 dynamic loading tests carried out with different piles and projects, three types of artificial neural network (ANN) have been used to estimate the load. Initially, the perceptron multi-layer neural network was one of the most used neural networks. Subsequently, the neuro-fuzzy network is used in a combination of neuro-fuzzy networks and, at the end of the radial-based neural network, a successful network was used for non-linear problems. Between the different models of the neural network used by researchers, the multi-layered perceptron network has a better performance. However, other networks used have also proven successful. Finally, different models of the neural networks were compared and the network that has the best performance was identified in both phases. The models based on neural networks, unlike conventional behavioral models, do not explain how the input parameters affect the output. In this research, by analyzing the sensitivity to the optimal structure of the introduced models in each step, we have tried to partly answer this question. Also, the extraction and presentation of the relations governing a neural network model to the user is more reliable in the use of such models, which facilitates the application of such models in engineering works. In this research, four first indicators were used to evaluate and compare the models and structures.