Diagnosis of prestressed concrete pile defects using probabilistic neural networks

Previous studies have applied artificial neural networks (ANN) with the back-propagation learning algorithm for diagnosing pre-stressed concrete piles. Recent developments of ANN breed a new form of network architectures for modeling this specific type of classification problems: Probabilistic Neural Networks (PNN). This paper presents this probabilistic neural network architecture for diagnosing the causes of prestressed concrete pile damages. In this paper, the use of neural networks for construction is first presented and the various types of neural networks are introduced. Then, based upon a set of data collected from the previous study on prestressed concrete pile diagnosis, the common features of concrete pile damage and their causes are identified. The PNN model and its architecture are described. Using the set of data, the network is trained and the procedural steps are described. A random seed approach for cross validation is used to assess the reliability of the model. Lastly, the result of the network training is discussed and analyzed, which demonstrates the robustness of the model developed. © 2004 Elsevier Ltd. All rights reserved.