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This study deals with predicting nonlinear time history deflection of scallop domes subject to earthquake loading employing neural network technique. Scallop domes have alternate ridged and grooves that radiate from the centre. There are two main types of scallop domes, lattice and continuous, which the latticed type of scallop domes is considered in the present paper. Due to the large number of the structural nodes and elements of scallop domes, nonlinear time history analysis of such structures is time consuming. In this study to reduce the computational burden radial basis function (RBF) neural network is utilized. The type of inputs of neural network models seriously affects the computational performance and accuracy of the network. Two types of input vectors: cross-sectional properties and natural periods of the structures can be employed for neural network training. In this paper the most influential natural periods of the structure are determined by adaptive neuro-fuzzy inference system (ANFIS) and then are used as the input vector of the RBF network. Results of illustrative example demonstrate high performance and computational accuracy of RBF network.

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This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative process needed to meet code requirements. In the PBDO procedure, the metaheuristics minimize the structural weight subjected to performance constraints on inter-story drift ratios at various performance levels. Two numerical examples are presented demonstrating the superiority of the PSO to the GA, ACO and HS metaheuristic algorithms.

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This paper presents an efficient meta-heuristic algorithm for optimization of double-layer scallop domes subjected to earthquake loading. The optimization is performed by a combination of harmony search (HS) and firefly algorithm (FA). This new algorithm is called harmony search firefly algorithm (HSFA). The optimization task is achieved by taking into account geometrical and material nonlinearities. Operation of HSFA includes three phases. In the first phase, a preliminary optimization is accomplished using HS. In the second phase, an optimal initial population is produced using the first phase results. In the last phase, FA is employed to find optimum design using the produced optimal initial population. The optimum design obtained by HSFA is compared with those of HS and FA. It is demonstrated that the HSFA converges to better solution compared to the other algorithms.

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In this study an efficient meta-heuristic is proposed for layout optimization of truss structures by combining cellular automata (CA) and firefly algorithm (FA). In the proposed meta-heuristic, called here as cellular automata firefly algorithm (CAFA), a new equation is presented for position updating of fireflies based on the concept of CA. Two benchmark examples of truss structures are presented to illustrate the efficiency of the proposed algorithm. Numerical results reveal that the proposed algorithm is a powerful optimization technique with improved convergence rate in comparison with other existing algorithms.