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Optimal design of large-scale structures is a rather difficult task and the computational efficiency of the currently available methods needs to be improved. In view of this, the paper presents a modified Charged System Search (CSS) algorithm. The new methodology is based on the combination of CSS and Particle Swarm Optimizer. In addition, in order to improve optimization search, the sequence of tasks entailed by the optimization process is changed so that the updating of the design variables can directly be performed after each movement. In this way, the new method acts as a single-agent algorithm while preserving the positive characteristics of its original multi-agent formulation.

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Vibration based techniques of structural damage detection using model updating method, are computationally expensive for large-scale structures. In this study, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), To efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the MSEBI of structural elements is evaluated using properly trained cascade feed-forward neural network (CFNN). In order to achieve an appropriate artificial neural network (ANN) model for MSEBI evaluation, a set of feed-forward artificial neural networks which are more suitable for non-linear approximation, are trained. All of these neural networks are tested and the results demonstrate that the CFNN model with log-sigmoid hidden layer transfer function is the most suitable ANN model among these selected ANNs. Moreover, to increase damage severity detection accuracy, the optimization process of damage severity detection is carried out by particle swarm optimization (PSO) whose cost function is constructed based on MSEBI. To validate the proposed solution method, two structural examples with different number of members are presented. The results indicate that after determining the damage location, the proposed solution method for damage severity detection leads to significant reduction of computational time compared to finite element method. Furthermore, engaging PSO algorithm by efficient approximation mechanism of finite element (FE) model, maintains the acceptable accuracy of damage severity detection.

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Inverse Transient Analysis (ITA) is a powerful approach for leak detection of pipelines. When the pipe transient flow is analyzed in frequency domain the ITA is called Inverse Frequency Response Analysis (IFRA). To implement an IFRA for leak detection, a transient state is initiated in the pipe by fast closure of the downstream end valve. Then, the pressure time history at the valve location is measured. Using the Fast Fourier Transform (FFT) the measured signal is transferred into the frequency domain. Besides, using the transfer matrix method, a frequency response analysis model for the pipeline is developed as a function of the leak parameters including the number, location and size of leaks. This model predicts the frequency responses of the pipe in return for any random set of leak parameters. Then, a nonlinear inverse problem is defined to minimize the discrepancies between the observed and predicted responses at the valve location. To find the pipeline leaks the method of Particle Swarm Optimization (PSO) is coupled to the transient analysis model while, the leak parameters are the optimization decision variables. The model is successfully applied against an example pipeline and in both terms of efficiency and reliability the results are satisfactory.

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Design optimization of cable-stayed bridges is a challenging optimization problem because a large number of variables is usually involved in the optimization process. For these structures the design variables are cross-sectional areas of the cables. In this study, an efficient metaheuristic algorithm namely, momentum search algorithm (MSA) is used to optimize the design of cable-stayed bridges. The MSA is inspired by the Physics and its superiority over many metaheuristics has been demonstrated in tackling several standard benchmark test functions. In the current work, the performance of MSA is compared with that of two other metaheuristics and it is shown that the MSA is an efficient algorithm to tackle the optimization problem of cable-stayed bridges.
 

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In recent years, there has been a lot of interest in the development and deployment of control methods that use different components of the building to mitigate the seismic response of the structure. Meanwhile, the building facade, as a non-structural component, can be a suitable alternative in affecting the structure's behavior because of its role as an envelope of the building with a significant weight. Among the modular cladding systems, the Double Skin Facade (DSF) can be considered a passive system due to the distance of the exterior layer from the main structure and sufficient continuity and rigidity.  In this study, DSF systems are used as Peripheral Mass Dampers (PMDs) that control structural movements by dissipating energy during strong motions. The PMD system provides a building with several inherent dampers without the need for extra mass. To show the reliability and efficiency of the proposed approach, the PMD model is investigated and compared with results available in uncontrolled and Tuned Mass Damper (TMD) models. The PMD model is examined in three structural frames with 10, 20, and 30 stories with the extreme Mass Ratios (MRs) of 5% to 20%. The Particle Swarm Optimization (PSO) is performed on damper parameters of PMD and TMD systems to minimize structural responses. The results demonstrate that an optimal PMD system with multiple inherent mass dampers outperforms a single TMD system.