Principal Component Analysis and Neural Networks for Predicting the Pile Capacity Using SPT

Abstract

A neural network is, in essence, an attempt to simulate the brain. Neural network theory revolves around the idea that certain key properties of biological neurons can be extracted and applied to simulations, thus creating a simulated (and very much simplified) brain. The first important thing to understand then is that the components of an artificial neural network are an attempt to recreate the computing potential of the brain. This famous network memorizes information by a process of training, to this effect the theory of artificial neural network is developed and is applied in several fields of sciences. The geotechnical domain is among them and in particular the resolution of problems of which parameters that govern them have an uncertain character, as the case of the prediction of the pile capacity. For it we collected 120 cases of the literature, sweeping a variety of sites through the world. The model conceived by an iterative process that is, the retropropagation was validated by experimental tests and was compared with the values predicted by four of the most commonly used traditional methods. In this paper, the developed neural network model is based on the principal component analysis approach (PCA) for data analysis in the aim to improve the generalization process. The results indicate that the ANN model is able to accurately predict the capacity in several cases, including the experiments on model piles. The PCA technique shows the efficiency in the variable analysis in order to determine their relative contribution on the pile capacity and improve the generalization capacity. This study is limited for the driven piles. Pile foundations are used extensively around the world to support both inland and offshore structures, including nuclear plants and oil drilling platforms. They are mainly used in sites where the presence of soft soil layers would cause excessive deformation or failure of more conventional types of foundations. The two major categories of piles in common use are: friction or floating piles, whose load carrying capacity depends mostly on the amount of friction resistance that can develop at the interface between the pile shaft and the soil; and end bearing piles, which rely primarily on the concentrated soil resistance at the tip of the pile. To estimate the load-bearing capacity of the piles, therefore, one or more of several pile loading tests (PLTs) and pile dynamic analysis (PDA) tests may be performed, depending on the importance of a project. Due to the high cost and the time required for conducting such tests. Many researches reports dealing with the ultimate bearing capacity of pile foundations have been listed in the literature during the past four decades. There are two approaches employed in the study of the behaviour of the pile foundations: theoretical and experimental. The problem of estimating the capacity of deep foundations is very complex and the mechanisms are not yet entirely understood. This can be attributed the sensitive nature of the factors affecting the behaviour of the pile. Among these factors are the stress-strain history of the soil, soil compressibility, and the difficulty in obtaining undisturbed samples of cohesionless soil, the installation