Structural inverse analysis by hybrid simplex artificial bee colony algorithms Fei Kang * , Junjie Li, Qing Xu School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China Abstract A hybrid simplex artificial bee colony algorithm (HSABCA) which combines Nelder-Mead simplex method with artificial bee colony algorithm (ABCA) is proposed for inverse analysis problems. The proposed algorithm is applied to parameter identification of concrete dam-foundation systems. To verify the performance of HSABCA, it is compared with the basic ABCA and a real coded genetic algorithm (RCGA) on two examples: a gravity dam and an arc dam. Results show that the proposed algorithm is an efficient tool for inverse analysis and it performs much better than ABCA and RCGA on such problems. Keywords: Inverse/back analysis; Artificial bee colony algorithms; Nelder-Mead simplex search method; Real coded genetic algorithms; Parameter identification; Concrete dams; 1. Introduction The quantitative assessment of constitutive parameters by inverse analysis exhibits at present growing scientific interest and practical usefulness, as material models become more realistic and complex, and computational tools more and more powerful [1]. In general, forward modeling allows us to answer questions such as “what response should be expected from this distribution of material properties under these initial conditions?” Reverse problems require an answer to a question that goes in the opposite direction. Reverse analysis has been widely used in diverse fields, such as geological process modeling [2], structural damage or crack detection [3-5], drainage system design [6], excavation support system simulation [7], parameter identification of earth-rockfill dams [8] and concrete dams [9-11]. The Young modulus is a necessary parameter in structural analysis for the determination of the stress distributions and displacements, especially when the design of the structure is based on elasticity considerations. In a dam-foundation system, the modulus of elasticity of dam concrete is hard to be determined directly from tests due to the necessity for large specimens and testing machines [12]; the modulus of the rock is also hard to be determined because of the complicated geological situations. Inverse analysis is a powerful tool to determine the mechanical parameters of dam-foundation systems. Through inverse analysis, exact parameters of dam-foundation systems can be determined, and a precise evaluation on the safety condition of dams can be made. Several strategies have been developed for the diagnostic inverse analysis of concrete dams. A damage diagnosis approach for concrete dams by radar monitoring is proposed by Ardito et al[10,13]. An overall inverse analysis method for concrete dams based on neural networks is proposed by Fedele et al[9,14]. An inverse analysis method for the identification of stress states and elastic properties in concrete dams by flat-jack test is developed by Fedele and Maier [11]. In recent years, reverse analysis is mainly based on two methodologies: neural networks [8-9,15] and optimization algorithms. In this paper, we focus on optimization-based inverse 1 * Corresponding author. Tel.:+86-0411-84708516; Fax: +86-0411-84708501 E-mail address: kangfei2009@163.com