Prediction of Seismic Collapse Risk in Steel Moment Framed Structures by Metaheuristic Algorithm

Depending on the many aspects of seismic performance evaluation procedures, different collapse performance levels may be obtained. Fragility curves in different limit states are the most important tools for evaluating the performance of structures marred by varying degrees of damage. These curves are also vital in determining the decision making variables as specified by the procedure developed by researchers of Pacific Earthquake Engineering Research Center. These curves play a significant role in the determination and management of consequences of earthquakes. The present thesis is mainly focused on the fragility curve for the sidesway collapse limit state. One important issue in deriving fragility curves is how uncertainties are blended and incorporated into the model under seismic conditions. The fragility curve for sidesway collapse limit state is influenced by different uncertainty sources including, aleatory, epistemic (modelling) and cognitive uncertainties. In this study, incremental dynamic analysis is applied to consider aleatory uncertainty, while strong ground motion selected by K-Means algorithm, which is used for proper selection of record to record uncertainty and reduction of time cost instead of random selection. Analytical equations of Response Surface Method are obtained through incremental dynamic analysis results by Cuckoo algorithm which predicts mean and standard deviation of collapse fragility curve. Takagi-Sugeno-Kang model is used for material quality by response surface coefficient. Finally collapse fragility curves with various sources of uncertainty are derived through large number of material quality values and meta variable inferred by Takagi-Sugeno-Kang fuzzy model based on response surface method coefficients. On the other hand, the optimized fuzzy method is used to compile the fragility curves by considering epistemic and aleatory uncertainties for the model under collapse conditions at 2% interstory drift ratio and sidesway collapse. In proposed method, model parameters are fuzzy values and the Fuzzy C-means based on particle swarm optimization is used to estimate mean and standard deviation to derive the fragility curve (these two being fuzzy values themselves). The Fuzzy C-means based on particle swarm optimization algorithm is trained using scenarios compiled via the incremental dynamic analysis method. Results obtained from the full Monte Carlo method were used for comparison and verification. According to the comparison of results, it is observed that the proposed method is very efficient and decreasing computational run time compared with the full Monte Carlo method. Keywords: Modelling uncertainty, Cognitive uncertainty, TSK model, Cuckoo algorithm, FCM-PSO.