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Crossover operator plays a crucial role in the efficiency of genetic algorithm (GA). Several crossover operators have been proposed for solving the travelling salesman problem (TSP) in the literature. These operators have paid less attention to the characteristics of the traveling salesman problem, and majority of these operators can only generate feasible solutions. In this paper, a crossover operator is presented that has the capability of generating solutions based on a logical reasoning. In other words, the solution space is explored by the proposed method purposefully. Numerical results based on 26 benchmark instances demonstrate the efficiency of the proposed method compared with the previous meta-heuristic methods.

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Depending on the tectonic activities, most buildings subject to multiple earthquakes, while a single design earthquake is suggested in most seismic design codes. Perhaps, the lack of easy assessment to second shock information and sometimes use of inappropriate methods in estimating these features cause successive earthquakes mainly were ignored in the analysis procedure. In order to overcome to above deficiencies, the learning abilities of artificial neural networks (ANNs) are used in two steps to evaluate the seismic capacity of steel frames consisting moment-resisting frames, ordinary concentrically, and buckling restrained brace (BRB) under critical consecutive earthquakes. For this purpose, peak ground acceleration of second shock (PGA_a) is estimated based on the first shock features in the first step. Next, second ANNs estimate the decreased capacity of the damaged structure for LS and CP performance level according to the proposed PGA_a from the previous step and some seismic and structural features. The results indicate that ANNs are trained to generalize the unseen information very well and reflect good precision in predicting target results in both steps. Finally, the effect of different parameters and repeated shocks is investigated on the seismic performance of mentioned frames. The results show the proper performance of BRB frames in the case of real and repeated earthquakes.
 

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This paper presents a method for detecting the location and severity of damage in shell structures. The method relies on extracting time-domain damage-sensitive features from vibrational responses and applying topology optimization. To achieve this, singular values are extracted from the Hankel matrix using singular value (SVD) decomposition and selected as damage-sensitive features. The damage detection problem is formulated as a topology optimization problem in which damage is modeled using the solid isotropic material with penalization (SIMP) method. Sensitivity analysis is carried out using the finite difference method to compute the derivatives of the objective function with respect to the design variables, thereby enabling efficient gradient-based optimization. The objective function is defined to minimize the differences between the singular values of the reference structure and those of the model. Abaqus software is used to perform dynamic finite element analysis of the shell model and to derive acceleration responses at selected nodes, which serve as sensor locations. The results from several numerical examples demonstrate the high capability of the proposed method in accurately identifying both the location and severity of damage.