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This paper presents a service-life prediction model of expansion joints. Significant factors influencing the service lives of expansion joints were identified by statistical methods. Artificial neural network was implemented to establish the service-life prediction model of expansion joints. Taken finger plate joints for illustration, eight statistically significant factors influencing the service lives of finger plate joints are identified among twenty one factors studied. Through these eight factors, the service lives of expansion joints can be predicted by the established model. The training and testing errors indicate that the established artificial neural network model can provide accurate predictions which are essential information for maintenance strategies. [ABSTRACT FROM AUTHOR]

Structural damage detection is a very important research topic and, currently, there are not specific tools to solve it. A promising tool that can be used is the artificial neural network, ANN, which can deal with hard problems. This paper uses a back propagation ANN with Bayesian regularization training to locate and quantify damage in truss structures. The input parameters corresponded to natural frequencies combined with shape modes, modal flexibilities or modal strain energies. The ANN was trained by considering only simple damage scenarios, random multiple damage scenarios or a combination of them. The results are shown in terms of the percentage of cases in which the ANN trained achieves a determined performance in assessing both the damage extension and the presence of damaged elements. The best performance for the ANN is obtained by using modal strain energies and multiple damage scenarios. [ABSTRACT FROM AUTHOR]

In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data. [ABSTRACT FROM AUTHOR]