EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS Earthquake Engng Struct. Dyn. 2002; 31:217–234 (DOI: 10.1002/eqe.106) Structural damage detection using the optimal weights of the approximating articial neural networks Shih-Lin Hung ∗; † and C. Y. Kao Department of Civil Engineering; National Chiao Tung University; 1001 Ta Hsueh Road; Hsinchu 300; Taiwan SUMMARY This work presents a novel neural network-based approach to detect structural damage. The proposed approach comprises two steps. The rst step, system identication, involves using neural system iden- tication networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identies the system in a certain undamaged or damaged state, have a negligible variation with dierent system errors. This loosely dened unique property enables these partial derivatives to quantitatively indicate system dam- age from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright ? 2001 John Wiley & Sons, Ltd. KEY WORDS: articial neural network (ANN); partial derivative form of ANN; system identication; structural damage detection INTRODUCTION Civil engineering structures are prone to damage and deterioration during their service life. Damage assessment attempts to determine whether structural damage has occurred and, if so, to determine the location and extent of the damage. However, detecting structural damage ∗ Correspondence to: Shih-Lin Hung, Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan. † E-mail: slhung@cc.nctu.edu.tw Contract=grant sponsor: National Science Council of the Republic of China; contract=grant number: NSC 89-2211- E-009-014. Received 21 November 2000 Revised 22 March 2001 Copyright ? 2001 John Wiley & Sons, Ltd. Accepted 16 April 2001