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Different methods are available for simultaneous optimization of cross-section, topology and geometry of truss structures. Since the search space for this problem is very large, the probability of falling in local optimum is considerably high. On the other hand, different types of design variables (continuous and discrete) lead to some difficulties in the process of optimization. In this article, simultaneous optimization of cross-section, topology and geometry of truss structures is performed by utilizing the Multi Heuristic based Search Method (MHSM) that overcome the above mentioned problem and obtains good results. The presented method performs the optimization by dividing the searching space into five subsections in which an MHSM is employed. These subsections are named procedure islands. Some examples are then presented to scrutinize the method more carefully. Results show the capabilities of the present algorithm for optimal design of truss structures.

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The main objective of this paper is to use ant optimized neural networks to generate artificial earthquake records. In this regard, training accelerograms selected according to the site geology of recorder station and Wavelet Packet Transform (WPT) used to decompose these records. Then Artificial Neural Networks (ANN) optimized with Ant Colony Optimization and resilient Backpropagation algorithm and learn to relate the dimension reduced response spectrum of records to their wavelet packet coefficients. Trained ANNs are capable to produce wavelet packet coefficients for a specified spectrum, so by using inverse WPT artificial accelerograms obtained. By using these tools, the learning time of ANNs reduced salient and generated accelerograms had more spectrum-compatibility and save their essence as earthquake accelerograms.

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The prevalent strategy in the topology optimization phase is to select a subset of members existing in an excessively connected truss, called Ground Structure, such that the overall weight or cost is minimized. Although finding a good topology significantly reduces the overall cost, excessive growth of the size of topology space combined with existence of varied types of design variables challenges applicability of evolutionary algorithms tailored for simultaneous optimization of topology, shape and size (TSS) in more complicated cases which are of great practical interest. In practice, large-scale truss structures are often modular, formed by joining periodically repeated units. This article organizes a novel simulation approach for this class of truss structures where the main drawbacks of the ground structure-based simulation approach are greatly moderated. The two approaches are independently employed for simultaneous TSS optimization of a modular truss example and the size of topology space as well as the required computation budget to generate an acceptable candidate design is compared. Result comparison reveals by employing the novel approach, problem complexity grows linearly with respect to the number of modules which allows for expanding application of TSS optimizers to complex modular trusses. Use of relative coordinates is also warranted for shape optimization which concludes to a more efficient optimization process.

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Endurance Time Method (ET) is a dynamic analysis in which structures are subjected to intensifying accelerograms that are optimized in a way that seismic performance of structures can be estimated at different hazard levels with the best possible accuracy. For the currently available ET accelerograms, regardless of the shaking characteristic, an excitation level is recognized as a representative of a specific hazard level, when the acceleration and the displacement spectrum produced by the ET accelerograms up to that excitation level will be compatible with the acceleration and the displacement spectrum associated with that hazard level. This study compares the shaking characteristics of the current ET accelerograms with the ground motions. For this purpose, distribution of plastic cycles and the equivalent number of the cycles are considered as shaking properties of a motion. This study suggests a procedure to achieve the best possible consistency between the equivalent number of cycles of the current ET records and the ground motions. Moreover, a procedure to generate the new generation and optimization of the ET accelerograms which are more consistent with the ground motions are suggested.

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In recent years some multi-mode pushover procedures taking into account higher mode effects, have been proposed. The responses of considered modes are combined by the quadratic combination rules, while using the elastic modal combination rules in the inelastic phases is not valid. Here, an optimum weighted mode combination method for nonlinear static analysis is presented. Genetic algorithm is used for optimization of the modal weight. The proposed procedure is applied for a sample building. The results show that the resulted response from the proposed method has minimal error in comparison with the response of the nonlinear time history analysis.

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The present study attempts to apply an efficient yet simple optimization (SOPT) algorithm to optimum design of truss structures under stress and displacement constraints. The computational efficiency of the technique is improved through avoiding unnecessary analyses during the course of optimization using the so-called upper bound strategy (UBS). The efficiency of the UBS integrated SOPT algorithm is evaluated through benchmark sizing optimization problems of truss structures and the numerical results are reported. A comparison of the numerical results attained using the SOPT algorithm with those of modern metaheuristic techniques demonstrates that the employed algorithm is capable of locating promising designs with considerably less computational effort.

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Tuned mass damper (TMD) have been studied and installed in structures extensively to protect the structures against lateral loads. Multiple tuned mass dampers (MTMDs) which include a number of TMDs with different parameters have been proposed for improving the performance of single TMDs. When the structural system is considered as multiple degrees of freedom (MDOF) and implemented with MTMDs, there is no effective closed-form solution to determine the optimal parameters of MTMDs. On the other hand designing optimal MTMDs include a large number of variables. For optimal design of MTMDs, in this research an effective method has been proposed in which the parameters of TMDs are determined based on minimizing the Hankel’s norm of structure. Since the optimization procedure includes a large number of variables, hence it has been decided to use Genetic Algorithms (GAs) for determining the variables. For numerical simulation, the method has been utilized on an eight-storey shear frame modeled as MDOF, and optimal MTMDs have been designed. The results show that using the Hankel’s norm of structure as objective function has led to design effective MTMDs which could be effective in reducing the response of structure, especially the average value, under different far-field and near-field earthquakes. Also it has been found that the method is effective regarding its simplicity and convergence in solving complex optimization problem. Through extensive numerical analysis the effect of MTMDs mass ratio and TMDs number in MTMDs has been studied.

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This article introduces three simple ideas that lead to the efficient design of regular moment frames. The finite module concept assumes that the moment frame may be construed as being composed of predesigned, imaginary rectangular modules that fit into the bays of the structure. Plastic design analysis aims at minimizing the demand-capacity ratios of elements of ductile moment frames by inducing the strength and stiffnesses of groups of members in accordance with certain design criteria, rather than investigating their suitability against the same rules of compliance. Collapse modes and stability conditions are imposed rather than investigated. In short, theory of structures is applied rather than followed. Plastic displacement control suggests that in addition to conducting failure analysis, the maximum displacements of plausible failure modes at incipient collapse should also be taken into consideration. While two collapse mechanisms may share the same carrying capacity, their maximum displacements may be different.

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The Isogeometric Analysis (IA) method is applied for structural topology optimization instead of the finite element method. For this purpose, the material density is considered as a continuous function throughout the design domain and approximated by the Non-Uniform Rational B-Spline (NURBS) basis functions. The coordinates of control points which are also used for constructing the density function, are considered as design variables of the optimization problem. In order to change the design variables towards optimum, the Method of Moving Asymptotes (MMA) is used. To alleviate the formation of layouts with porous media, the density function is penalized during the optimization process. A few examples are presented to demonstrate the performance of the method.

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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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An excavation damage zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. This paper presents an approach to build a model for the identification and classification of the EDZ. The Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linearity and high dimension. However, the practicability of the SVM is influenced by the difficulty of selecting appropriate SVM parameters. In this study, the proposed hybrid Harmony search (HS) with the SVM was applied for identification and classification of damaged zone, in which HS was used to determine the optimized free parameters of the SVM. For identification and classification of the EDZ, based upon the modulus of the deformation modulus and using the hybrid of HS with the SVM a model for the identification and classification of the EDZ was built. To illustrate the capability of the HS-SVM model defined, field data from a test gallery of the Gotvand dam, Iran were used. The results obtained indicate that the HS-SVM model can be used successfully for identification and classification of damaged zone around underground spaces.

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In this article, the ant colony method is utilized for topology optimization of space structures. Strain energy of the structure is minimized while the material volume is limited to a certain amount. In other words, the stiffest possible structure is sought when certain given materials are used. In addition, a noise cleaning technique is addressed to prevent undesirable members in optimum topology. The performance of the method for topology optimization of space structures are demonstrated by three numerical examples.

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This study was prompted by the need to elaborate on recent developments in plastic design of, parallel chord Vierendeel girders (VG). The paper proposes exact, general solutions to two novel classes of VG under practical loading conditions, a-VG of uniform section, where the chords and the verticals may be composed of two different prismatic sections, and b-VG of uniform strength, where the constituent elements are selected in such a way as to induce a state of equal stress for all members of the structure. It has been shown that the total weight of both classes of VG can be minimized by the proper selection of the relative strengths of the members of each system. The essence of the paper is based on a novel failure mechanism presented for the first time in this article. It has been shown that racking moments can be utilized to conduct spot checks on final solutions. Several generic examples have been provided to demonstrate the applications and the validity of the proposed solutions.

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The excavation damaged zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin hypercube sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.

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This paper investigates the optimized parameters of Tuned Mass Dampers (TMDs) for high-rise structures considering Soil Structure Interaction (SSI) effects. Three optimization methods, namely the ant colony optimization (ACO) technique together with artificial bee colony (ABC) and shuffled complex evolution (SCE) methods are utilized for the optimization of TMD Mass, damping coefficient and spring stiffness as the design variables. The objective is to decrease the maximum displacement of structure. The 40 story structure with three soil types is employed to design TMD for six types of far field earthquakes. The results are then utilized to obtain relations for the optimized TMD parameters with SSI effects. The relations are then applied to design TMD for the same structure with another five types of far field oscillations, and reasonable results are achieved. For further investigations, the obtained relations are utilized to design TMD for a new structure, and the reduction values are obtained for five types of earthquakes, which show acceptable results. This study improves the understanding of earthquake oscillations, and helps the designers to achieve the optimized TMD for high-rise buildings.

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A novel optimization algorithm named teaching-learning-based optimization (TLBO) algorithm and its implementation procedure were presented in this paper. TLBO is a meta-heuristic method, which simulates the phenomenon in classes. TLBO has two phases: teacher phase and learner phase. Students learn from teachers in teacher phases and obtain knowledge by mutual learning in learner phase. The suitability of TLBO for size and geometry optimization of structures in structural optimal design was tested by three truss examples. Meanwhile, these examples were used as benchmark structures to explore the effectiveness and robustness of TLBO. The results were compared with those of other algorithms. It is found that TLBO has advantages over other optimal algorithms in convergence rate and accuracy when the number of variables is the same. It is much desired for TLBO to be applied to the tasks of optimal design of engineering structures.

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In this paper an efficient method is developed for the analysis of non-regular graphs which contain regular submodels. A model is called regular if it can be expressed as the product of two or three subgraphs. Efficient decomposition methods are available in the literature for the analysis of some classes of regular models. In the present method, for a non-regular model, first the nodes of the non-regular part of such model are ordered followed by ordering the nodes of the regular part. With this ordering the graph matrices will be separated into two blocks. The eigensolution of the non-regular part can be performed by an iterative method, and those of the regular part can easily be calculated using decomposition approaches studied in our previous articles. Some numerical examples are included to illustrate the efficiency of the new method.

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In this study, an approach for damage detection of large-scale structures is developed by employing kinetic and modal strain energies and also Heuristic Particle Swarm Optimization (HPSO) algorithm. Kinetic strain energy is employed to determine the location of structural damages. After determining the suspected damage locations, the severity of damages is obtained based on variations of modal strain energy between the analytical models and the responses measured in damaged models using time history dynamic analysis data. In this paper, damages are modeled as a reduction of elasticity modulus of structural elements. The detection of structural damages is formulated as an unconstrained optimization problem that is solved by HPSO algorithm. To evaluate the performance of the proposed method, the results are compared with those provided in previous studies. To demonstrate the ability of this method for detection of multiple structural damages, different types of damage scenarios are considered. The results show that the proposed method can detect the exact locations and the severity of damages with a high accuracy in large-scale structures.

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The main aim of the present study is to achieve optimum design of reinforced concrete (RC) plane moment frames using bat algorithm (BA) which is a newly developed meta-heuristic optimization algorithm based on the echolocation behaviour of bats. The objective function is the total cost of the frame and the design constraints are checked during the optimization process based on ACI 318-08 code. Design variables are the cross-sectional assignments of the structural members and are selected from a data set containing a finite number of sectional properties of beams and columns in a practical range. Three design examples including four, eight and twelve story RC frames are presented and the results are compared with those of other algorithms. The numerical results demonstrate the superiority of the BA to the other meta-heuristic algorithms in terms of the frame optimal cost and the convergence rate.

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In this study, teaching-learning-based optimization (TLBO) algorithm is employed for the first time for optimization of real world truss bridges. The objective function considered is the weight of the structure subjected to design constraints including internal stress within bar elements and serviceability (deflection). Two examples demonstrate the effectiveness of TLBO algorithm in optimization of such structures. Various design groups have been considered for each problem and the results are compared. Both tensile and compressive stresses are taken into account. The results show that TLBO has a great intrinsic capability in problems involving nonlinear design criteria.