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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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This paper presents the application of metaheuristic methods to the minimum crossing number problem for the first time. These algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The crossing number problem is NP-hard and has important applications in engineering. The proposed algorithms are tested on six complete graphs and eight complete bipartite graphs and their results are compared with some existing methods.

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A Reliability-Based Design Optimization (RBDO) framework is presented that accounts for stochastic variations in structural parameters and operating conditions. The reliability index calculation is itself an iterative process, potentially employing an optimization technique to find the shortest distance from the origin to the limit-state boundary in a standard normal space. Monte Carlo simulation (MCs) is embedded into a design optimization procedure by a modular double loop approach, which the self-adaptive version of particle swarm optimization method is introduced as an optimization technique. Double loop method has the advantage of being simple in concepts and easy to implement. First, we study the efficiency of self-adaptive PSO algorithm inorder to solve the optimization problem in reliability analysis and then compare the results with the Monte Carlo simulation. While computationally significantly more expensive than deterministic design optimization, the examples illustrate the importance of accounting for uncertainties and the need for regarding reliability-based optimization methods and also, should encourage the use of PSO as the best of evolutionary optimization methods to more such reliability-based optimization problems.

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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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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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Tuned  mass  dampers  (TMDs)  are  as  a  efficient  control  tool  in  order  to  reduce  undesired vibrations  of  tall  buildings  and  large–span  bridges  against  lateral  loads  such  as  wind  and earthquake. Although many researchers has been widely  investigated  TMD systems  due to its  simplicity  and  application,  the  optimization  of  parameters  and  placement  of  TMD  are challenging tasks. Furthermore, ignoring the effects of soil–structure interaction (SSI) may lead to unrealistic desig of structure and its dampers. Hence, the  effects of SSI should be considered  in  the  design  of  TMD.  Therefore,  the  main  aim  of  this  study  is  to  optimize parameters  of  TMD  subjected  to  earthquake  and  considering  the  effects  of  SSI.  In  this regard,  the  parameters  of  TMD  including  mass,  stiffness  and   damping  optimization  are considered  as  the  variables  of  optimization.  The  maximum  absolute  displacement  and acceleration of structure are also simultaneously selected as objective functions. The multi –objective particle  swarm optimization  (MOPSO) algorithm  is adopted  to find  the  optimal parameters  of  TMD.  In  this  study,  the  Lagrangian  method  is  utilized  for  obtaining  the equations of motion for SSI system, and the time domain analysis is implemented based on Newmark method. In order to investigate the effects of SSI in the optimal design of TMD, a 40 storey shear building with a TMD subjected to the El–Centro earthquake is considered. The  numerical  results  show  that  the  SSI  effects  have  the  significant  influence  on  the optimum parameters of TMD.

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This paper combines particle swarm optimization, grid search method and univariate method as a general optimization approach for any type of problems emphasizing on optimum design of steel frame structures. The new algorithm is denoted as the GSU-PSO. This method attempts to decrease the search space and only searches the space near the optimum point. To achieve this aim, the whole search space is divided into a series of grids by applying the grid search method. By using a method derived from the univariate method, the variables of the best particle change values. Finally, by considering an interval adjustment to the variables and generating particles randomly in new intervals, the particle swarm optimization allows us to swiftly find the optimum solution. This method causes converge to the optimum solution more rapidly and with less number of analyses involved. The proposed GSU-PSO algorithm is tested on several steel frames from the literature. The algorithm is implemented by interfacing MATLAB mathematical software and SAP2000 structural analysis code. The results indicated that this method has a higher convergence speed towards the optimal solution compared to the conventional and some well-known meta-heuristic algorithms. In comparison to the PSO algorithm, the proposed method required around 45% of the total number of analyses recorded and improved marginally the accuracy of solutions.

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This study investigates the prediction model of compressive strength of self–compacting concrete (SCC) by utilizing soft computing techniques. The techniques consist of adaptive neuro–based fuzzy inference system (ANFIS), artificial neural network (ANN) and the hybrid of particle swarm optimization with passive congregation (PSOPC) and ANFIS called PSOPC–ANFIS. Their performances are comparatively evaluated in order to find the best prediction model. In this study, SCC mixtures containing different percentage of nano SiO₂ (NS), nano–TiO₂ (NT), nano–Al₂O₃ (NA), also binary and ternary combining of these nanoparticles are selected. The results indicate that the PSOPC–ANFIS approach in comparison with the ANFIS and ANN techniques obtains an improvement in term of generalization and predictive accuracy. Although, the ANFIS and ANN techniques are a suitable model for this purpose, PSO integrated with the ANFIS is a flexible and accurate method due tothe stronger global search ability of the PSOPC algorithm.

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Optimization techniques can be efficiently utilized to achieve an optimal shape for arch dams. This optimal design can consider the conditions of the economy and safety simultaneously. The main aim is to present an applicable and practical model and suggest an algorithm for optimization of concrete arch dams to enhance their seismic performance. To achieve this purpose, a preliminary optimization is accomplished using PSO procedure in the first stage. Capabilities of Ansys Parametric Design Language (APDL) are applied for modeling the Dam-Foundation-Reservoir system. In the second stage with training the neural network, Group Method of Data Handling (GMDH) and replacement of Ansys analyst, optimal results have been achieved with the lowest error and less number of iteration respectively. Then a real world double-arch dam is presented to demonstrate the effectiveness and practicality of the PSO-GMDH. The numerical results reveal that the proposed method called PSO-GMDH provides faster rate and high searching accuracy to achieve the optimal shape of arch concrete dams and the modification and optimization of shape have a quite important role in increasing the safety against dynamic design loads.

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For seismic resistant design of critical structures, a dynamic analysis, based on either response spectrum or time history is frequently required. Due to the lack of recorded data and randomness of earthquake ground motion that might be experienced by the structure under probable future earthquakes, it is usually difficult to obtain recorded data which fit the necessary parameters (e.g. soil type, source mechanism, focal depth, etc.) well. In this paper, a new method for generating artificial earthquake accelerograms from the target earthquake spectrum is suggested based on the use of wavelet analysis and artificial neural networks. This procedure applies the learning capabilities of neural network to expand the knowledge of inverse mapping from the response spectrum to the earthquake accelerogram. At the first step, wavelet analysis is utilized to decompose earthquake accelerogram into several levels, which each of them covers a special range of frequencies. Then for every level, a neural network is trained to learn the relationship between the response spectrum and wavelet coefficients. Finally, the generated accelerogram using inverse discrete wavelet transform is obtained. In order to make earthquake signals compact in the proposed method, the multiplication sample of LPC (Linear predictor coefficients) is used. Some examples are presented to demonstrate the effectiveness of the proposed method.

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In this paper, controlling the location of the tip of an L-shape beam under gravity field is investigated. The beam is covered with piezoelectric patches. The gravity filed moves the tip of beam downward and the actuators with induced voltage move the tip to the previous location. to optimize the best location and voltages for actuators, the particle swarm optimization algorithm code is developed. The results show that the best position for the most effective actuators is located at the corner of the beam. Also with considering the best location for patches, with lower induced voltage, the location of the tip of beam cab controlled. Also, the results show that with the optimum location of actuators and appropriate voltage lead to using minimum energy with the desired shape in the beam. The results are compared with those reported in previous work.

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The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Particle Swarm Optimization (PSO) algorithm a hybrid algorithm (ANFIS-PSO) is developed for predicting the Froude number of three phase flows. This inference system is a set of if-then rules which is able to approximate non-linear functions. In this model, PSO is employed for increasing the ANFIS efficiency by adjusting membership functions as well as minimizing error values. In fact, the PSO algorithm is considered as an evolutionary computational method for optimizing the process continues and discontinues decision making functions. Additionally, PSO is considered as a population-based search method where each potential solution, known as a swarm, represents a particle of a population. In this approach, the particle position is changed continuously in a multidimensional search space, until reaching the optimal response and or computational limitations. At first, 127 ANFIS-PSO models are defined using parameters affecting the Froude number. Then, by analyzing the ANFIS-PSO model results, the superior model is presented. For the superior model, the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and the determination coefficient (R²) were calculated equal to 5.929, 0.324 and 0.975, respectively.

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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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The capacity vehicle routing problem (CVRP) is one of the most famous issues in combinatorial optimization that has been considered so far, and has attracted the attention of many scientists and researchers today. Therefore, many exact, heuristic and meta-heuristic methods have been presented in recent decades to solve it. In this paper, due to the weaknesses in the particle swarm optimization (PSO), a hybrid-modified version of this algorithm called PPSO is presented to solve the CVRP problem. In order to evaluate the efficiency of the algorithm, 14 standard examples from 50 to 199 customers of the existing literature were considered and the results were compared with other meta-heuristic algorithms. The results show that the proposed algorithm is competitive with other meta-heuristic algorithms. Besides, this algorithm obtained very close answers to the best known solutions (BKSs) for most of the examples, so that the seven BKSs were produced by PPSO.

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Presently, the introduction of intelligent models to optimize structural problems has become an important issue in civil engineering and almost all other fields of engineering. Optimization models in artificial intelligence have enabled us to provide powerful and practical solutions to structural optimization problems. In this study, a novel method for optimizing structures as well as solving structure-related problems is presented. The main purpose of this paper is to present an algorithm that addresses the major drawbacks of commonly-used algorithms including the Grey Wolf Optimization Algorithm (GWO), the Gravitational Search Algorithm (GSA), and the Particle Swarm Optimization Algorithm (PSO), and at the same time benefits from a high convergence rate. Also, another advantage of the proposed CGPGC algorithm is its considerable flexibility to solve a variety of optimization problems. To this end, we were inspired by the GSA law of gravity, the GWO's top three search factors, the PSO algorithm in calculating speed, and the cellular machine theory in the realm of population segmentation. The use of cellular neighborhood reduces the likelihood of getting caught in the local optimal trap and increases the rate of convergence to the global optimal point. Achieving reasonable results in mathematical functions (CEC 2005) and spatial structures (with a large number of variables) in comparison with those from GWO, GSA, PSO, and some other common heuristic algorithms shows an enhancement in the performance of the introduced method compared to the other ones.
 

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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.
 

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Shape optimization of a double-curved dam is formulated using control points for interpolation functions. Every design vector is decoded into the integrated water-dam-foundation rock model. An enhanced algorithm is proposed by hybridizing particle swarm algorithm with ant colony optimization and simulated annealing. The best experiences of the search agents are indirectly shared via pheromone trail deposited on a bi-partite characteristic graph. Such a stochastic search is further tuned by Boltzmann functions in simulated annealing. The proposed method earned the first rank in comparison with six well-known meta‑heuristic algorithms in solving benchmark test functions. It captured the optimal shape design of Morrow Point dam, as a widely addressed case-study, by 21% reduced concrete volume with respect to the common USBR design practice and 16% better than the particle swarm optimizer. Such an optimal design was also superior to the others in stress redistribution for better performance of the dam system.
 

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This paper presents the chaotic variants of the particle swarm optimization-statistical regeneration mechanism (PSO-SRM). The nine chaotic maps named Chebyshev, Circle, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal, and Tent are used to increase the performance of the PSO-SRM. These maps are utilized instead of the random number, which defines the solution generation method. The robustness and performance of these methods are tested in the three steel frame design problems, including the 1-bay 10-story steel frame, 3-bay 15-story steel frame, and 3-bay 24-story steel frame. The optimization results reveal that the applied chaotic maps improve the performance of the PSO-SRM.
 

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The primary objective of this paper is to propose a novel technique for hybridizing various metaheuristic algorithms to optimize the size of discrete structures. To accomplish this goal, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and enhanced colliding bodies optimization (ECBO) are hybridized to propose a new algorithm called hybrid PSO-ECBO (HPE) algorithm. The performance of the new HPE algorithm is investigated in solving the challenging structural optimization problems of discrete steel trusses and an improvement in results has been achieved. The numerical results demonstrate the superiority of the proposed HPE algorithm over the original versions of PSO, ECBO, and some other algorithms in the literature.