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This paper presents a suitable and quick way to choose earthquake records in non-linear dynamic analysis using optimization methods. In addition, these earthquake records are scaled. Therefore, structural responses of three different soil-frame models were examined, the change in maximum displacement of roof was analyzed and the damage index of whole structures was measured. The soil classification of project location was divided into 4 different types according to the velocity of shear waves in the Iranian Code for Seismic Design. As a result, 8 frame models were considered. The selection and scaling were carried out in 2 stages. In the first stage, the matching with design spectrum was carried out using genetic algorithm in order to achieve the mean of structural response. In the second stage, the matching with average of structural responses were carried out using PSO to achieve 1 or 3 accelerograms with related factors in order to be used in structural analysis.

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This paper presents a new model updating approach for structural damage localization and quantification. Based on the Modal Assurance Criterion (MAC), a new damage-sensitive cost function is introduced by employing the main diagonal and anti-diagonal members of the calculated Generalized Flexibility Matrix (GFM) for the monitored structure and its analytical model. Then, the cost function is solved by Democratic Particle Swarm Optimization (DPSO) algorithm to achieve the optimal solution of the problem lead to damage identification. DPSO is a modified version of standard PSO algorithm which is developed for presenting a fast speed evolutionary optimization strategy. The applicability of the method is demonstrated by studying three numerical examples which consists of a ten-story shear frame, a plane steel truss and a plane steel frame. Several challenges such as the efficiency of the DPSO algorithm in comparison with other evolutionary optimization approaches for solving the inverse problem, impacts of random noise in input data on the reliability of the presented method, and effects of the number of available modal data for damage identification, are studied. The obtained results reveal good, robust and stable performance of the presented method for structural damage identification using only the first several modes’ data.

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To deal with severe drought when water supply is insufficient hedging rule, based on hedging rule curve, is proposed. In general, in discrete hedging rules, the rationing factors have changed from a zone to another zone at once. Accordingly, this paper is an attempt to improve the conventional hedging rule to control the changes of rationing factors. In this regard, the simulation model has employed a fuzzy approach, and this causes rationing factor changing during a long term simulation gradually. To optimize different parameters of the purposed hedging a Multi-objective Particle Swarm Optimization (MOPSO) algorithm has been considered. The minimum of two objectives Modified Shortage Index (MSI) involving water supply of minimum flow and agriculture demands can be taken as the optimization objectives. The results of the proposed hedging rule indicate long term and annual MSI values have considerably improved compared to the conventional hedging rule. This determines that the proposed method is promising and efficient to mitigate the water shortage problem.

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In this paper, a systematic approach is presented for optimal design of tunnel support lining using two-dimensional finite element analysis models of soil-structure interaction developed in ABAQUS software and the Modified Colliding Bodies Optimization (MCBO) algorithm implemented in MATLAB environment. This approach is then employed to study the influence of variable geometrical and geo-mechanical parameters on the optimal design of a class of practical access tunnels.

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In the present study, two new hybrid approaches are proposed for predicting peak ground acceleration (PGA) parameter. The proposed approaches are based on the combinations of Adaptive Neuro-Fuzzy System (ANFIS) with Genetic Algorithm (GA), and with Particle Swarm Optimization (PSO). In these approaches, the PSO and GA algorithms are employed to enhance the accuracy of ANFIS model. To develop hybrid models, a comprehensive database from Pacific Earthquake Engineering Research Center (PEER) are used to train and test the proposed models. Earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms are used as predictive parameters. The performances of developed hybrid models (PSO-ANFIS-PSO and GA-ANFIS-GA) are compared with the ANFIS model and also the most common soft computing approaches available in the literature. According to the obtained results, three developed models can be effectively used to predict the PGA parameter, but the comparison of models shows that the PSO-ANFIS–PSO model provides better results.

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This paper aims to obtain the optimal distance between the adjacent structures using Particle Swarm Optimization (PSO) algorithm considering structure-soil-structure systems; The optimization algorithm has been prepared in MATLAB software and connected into OpenSees software (where the structure-soil-structure system has been analyzed by the direct approach). To this end, a series of adjacent structures with various slenderness have been modeled on the three soil types according to Iranian seismic code (Standard No. 2800) using the direct method. Then they have been analyzed under six earthquake excitations with different risk levels (low, moderate, and high).
The results are compared with the proposed values of separation gap between adjacent structures in the Iranian seismic code (Standard No. 2800). Results show that since structures with the same height constructed on a stiff soil will move in the same phase, there is no need to put distance between them. Although, the structures with the height more than 6-story frames where are located on a soft soil are needed to be separated. Additionally, the results show more separation gap between two adjacent structures when the risk level of earthquake is high. In general, the values which are presented in Standard No. 2800 are not suitable for low /moderate-rise structures specially when they are subjected to a high-risk level earthquake and are located on a soft soil and this separation gap should be increased about 10 to 90 percentage depend on the conditions but these values are appropriate for the adjacent structures with same height where are subjected to a low-risk level earthquakes built on soft soil.

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This study is devoted to discrete sizing optimization of truss structures employing an efficient discrete evolutionary meta-heuristic algorithm which uses the Newton gradient-based method as its updating scheme and it is named here as Newton Meta-heuristic Algorithm (NMA). In order to enable the NMA population-based meta-heuristic to effectively explore the discrete design space, a term containing the best solution found is added to the basic updating rule of the algorithm. The efficiency of the proposed NMA metaheuristic is illustrated by presenting five benchmark discrete truss optimization problems and comparing the results with literature. The numerical results demonstrate that the NMA is a robust and powerful meta-heuristic algorithm for dealing with the discrete sizing optimization problems of steel trusses.

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Pedestrian bridge is a structure constructed to maintain the safety of citizens in crowded and high-traffic areas. With the expansion of cities and the increase in population, the construction of bridges is necessary for easier and faster transportation, as well as the safety of pedestrians and vehicles. In this article, it is decided to consider the most economical cross-sections for these bridges according to the design regulations and codes of Practice in order to achieve the minimum weight, which will ultimately reduce the cost of construction and production and the usage of less resources. For this purpose, new GSS-PSO algorithm has been used and its results have been compared with GA and PSO algorithms, by the means of which an enhancement of PSO algorithm is seen. This enhancement on the conventional PSO technique reduces the search space more desirably and swiftly to a space close to the global optimum point. This algorithm has been implemented with MATLAB mathematical software and has been integrated with SAP2000v22 structural design software for analysis and optimum design under resistance and displacement constraints. The final results of the analyses are compared with an already designed and implemented infrastructure. In addition to a bridge optimization, a bench-mark frame optimization was also used in order for a better comparison between this algorithm and the other ones.