Damage detection accommodating nonlinear environmental effects by nonlinear principal component analysis

Damage detection in structural health monitoring should accommodate the variation caused by varying environmental conditions such as temperature, humidity, loading, and boundary conditions. A structural damage detection technique is proposed to deal with the continuous monitoring data of a structural system subjected to the complex nonlinear behavior caused by varying environmental conditions. Based on the identified or measured target features of the structural system under varying environmental conditions, e.g. stiffness of the structural components, the nonlinear principal component analysis (NLPCA) using auto-associative neural network is performed to extract the underlying environmental factors. Then a prediction model for NLPCA is proposed to estimate the damage extent. This proposed technique is capable of dealing with not only the non-increasing features, e.g. stiffness, but also the non-decreasing features, e.g. damage index, after damage is introduced without measuring the environmental factors directly. A numerical study is performed to demonstrate the advantages of the proposed technique over the technique using principal component analysis. A synthetic bridge model is simulated with the consideration of a specific element stiffness reduction together with the change due to environmental conditions including temperature, gradient of temperature, humidity, and frozen supports. Results show that the extent of stiffness loss can be quantified accurately and promptly after the damage is introduced. Copyright (C) 2009 John Wiley & Sons, Ltd.