Post-Quake Structural Damage Evaluation by Intelligent System: The Complementary Components of Structures

The prediction of post-quake structural damages evaluation in an important task because of the complexities of the damages observed on each of the constitutive components of the structure. The aim of this work is to improve an accurate global structural damage prediction with additional components the geotechnical disorders and building regularity by using with artificial neural networks (ANNs). Three groups of governing parameters are considered: the structural components group, the secondary components group and the qualitatively evaluated group (the geotechnical disorders and building regularity). Several possible combinations between the global and the individual (components) damages are investigated in order to identify the best relationship between them, as already explained by [19]-[20]. Principal-component analysis is used to evaluate the effects of each input variable on the global structural damage. On the basis of the comparisons, it is found that the ANNs prediction when the global damage is assumed to be influenced by three components groups, i.e. structural, secondary components and qualitatively evaluated group is better than the ANNs prediction when the global damage is assumed to be influenced by both building components groups, i.e. structural and secondary components.