MULTI-OBJECTIVE OPTIMIZATION OF GENETIC ALGORITHM-BASED FAILURE MODE FOR REINFORCED CONCRETE FRAME-SHEAR WALL STRUCTURES

The multi-objective optimization method of genetic algorithm (GA) based failure mode is proposed for reinforced concrete (RC) frame-shear wall structure. The sectional dimensions and materials consumption are served as the optimization variable and constraint condition, respectively. The maximum drift ratio and global structural damage index are used to construct the objective functions of GA related, and the favorable gene is transmitted by the crossover and mutation of the gene sequence. With simplifying a 5-story RC frame structure to a lumped mass system, the validity of the algorithm is proved. A case study of a 10-story RC frame-shear wall structure is carried out. Applying Incremental Dynamic Analysis (IDA), the severest ground motion and corresponding peak ground acceleration are determined to serve as the seismic input during the process of optimization. Meanwhile, pushover analysis is implemented on the structure to obtain the values of the yield and ultimate displacements, which are used to calculate the global damage index. A linear weighted method for the multi-objective minimum optimization problem is proposed to evaluate the algorithm convergence speed for each evaluation criteria of the performance. After 654 random samples' elastic-plastic time-history analyses in 4 generations, on the condition of little increase of materials consumption, it is indicated that the maximum drift ratio of the structure and the global damage index are reduced by 16.3% and 20.8%, respectively. The mean annual exceeding probability of each limit state is decreased, the Collapse Margin Ratio (CMR) is increased at the same time, and the aseismic performance of structure is effectively improved.