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Grillages are widely used in various structures. In this research, the Colliding Bodies Optimization (CBO) and Enhanced Colliding Bodies Optimization (ECBO) algorithms are used to obtain the optimum design of irregular grillage systems. The purpose of this research is to minimize the weight of the structure while satisfying the design constraints. The design variables are considered to be the cross-sectional properties of the beams and the design constraints are employed from LRFD-AISC. In addition, optimum design of grillages is performed for two cases: (i) without considering the warping effect, and (ii) with considering the warping effect. Also, several examples are presented to show the effect of different spacing and various boundary conditions. Finally, the results show that warping effect, beam spacing and boundary conditions have significant effects on the optimum design of grillages.

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In  this  paper,  a  method  is  presented  for  damage  detection  of  bridges  using  the  Enhanced Colliding Bodies Optimization (ECBO)  utilizing time-domain responses. The finite element modeling of the structure is based on  the equation of motion under the moving load, and the flexural stiffness of the structure is determined by the acceleration responses obtained via sensors placed in different places. Damage detection problem presented in this research is an inverse  problem,  which  is  optimized  by  the  ECBO  algorithm,  and  the  damages  in  the structures  are  fully  detected.  Furthermore,  for  simulating  the  real  situation,  the  effect  of measured noises is considered on the structure, to obtain more accurate results.

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The p-median problem is one of the discrete optimization problem in location theory which aims to satisfy total demand with minimum cost. A high-level algorithmic approach can be specialized to solve optimization problem. In recent years, meta-heuristic methods have been applied to support the solution of Combinatorial Optimization Problems (COP). Collision Bodies Optimization algorithm (CBO) and Enhanced Colliding Bodies Optimization (ECBO) are two recently developed continuous optimization algorithms which have been applied to some structural optimization problems. The main goal of this paper is to provide a useful comparison between capabilities of these two algorithms in solving p-median problems. Comparison of the obtained results shows the validity and robustness of these two new meta-heuristic algorithms for p-median problem.

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The present study is concerned with the simultaneous optimization of the size of components and the arrangement of connections for performance-based seismic design of low-rise SPSWs. Design variables include the size of beams and columns, the thickness of the infill panels, the type of each beam-to-column connection and the type of each infill-to-boundary frame connection. The objective function is considered to be the sum of the material cost and rigid connection fabrication cost. For comparison purposes, the SPSW model is also optimized with regard to two fixed connection arrangements. To fulfill the optimization task a new hybrid optimization algorithm called CBO-Jaya is proposed. The performance of the proposed hybrid optimization algorithm is assessed by two benchmark optimization problems. The results of the application of the proposed algorithm to the benchmark problem indicate the efficiency, robustness, and the fast convergence of the proposed algorithm compared with other meta-heuristic algorithms. The achieved results for the SPSWs demonstrate that incorporating the optimal arrangement of beam-to-column and infill-to-boundary frame connections into the optimization procedure results in considerable reduction of the overall cost.

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Structural damage detection is a field that has attracted a great interest in the scientific community in recent years. Most of these studies use dynamic analysis data of the beams as a diagnostic tool for damage. In this paper, a massless rotational spring was used to represent the cracked sections of beams and the natural frequencies and mode shape were obtained. For calculation of rotational spring stiffness equivalent of uncracked and cracked sections, finite element models and experimental test were used. The damage identification problem was addressed with two optimization techniques of different philosophers: ECBO, PSO and SQP methods. The objective functions used in the optimization process are based on the dynamic analysis data such as natural frequencies and mode shapes. This data was obtained by developing a software that performs the dynamic analysis of structures using the Finite Element Method (FEM). Comparison between the detected cracks using optimization method and real beam shows an acceptable agreement.

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In this paper an optimization framework is presented for automated performance-based seismic design of bridges consisting of multi-column RC pier substructures. The beneficial effects of fusing components on seismic performance of the quasi-isolated system is duly addressed in analysis and design. The proposed method is based on a two-step structural analysis consisting of a linear modal dynamic demand analysis and a nonlinear static capacity evaluation of the entire bridge structure. Results indicate that the proposed optimization method is capable of producing cost-effective design solutions combining the fusing behavior of bearings and yielding mechanism of piers. The optimal designs obtained from models addressing the performance of fusing components are far more efficient than those that do not take care of quasi-isolation behavior. 

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One of the most important factors that affects construction costs of highways is the earthwork cost. On the other hand, the earthwork cost strongly depends on the design of vertical alignment or project line. In this study, at first, the problem of vertical alignment optimization was formulated. To this end, station, elevation and vertical curve length in case of each point of vertical intersection (PVI) were considered as decision variables. The objective function was considered as earthwork cost and constraints were assumed as the maximum and minimum grade of tangents, minimum elevation of compulsory points, and the minimum length of vertical curves. For solving this optimization problem, the Colliding Bodies Optimization (CBO) algorithm was employed and results were compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In order to evaluate the effectiveness of formulation and CBO algorithm, three different highways were designed with respect to three different terrains including level, rolling and mountainous. After designing the preliminary vertical alignment for each highway, the optimal vertical alignments were determined by different optimization algorithms. The results of this research show that the CBO algorithm is superior to GA and PSO. Percentage of optimality (saving in earthworks cost) by CBO algorithm for level, rolling and mountainous terrains was determined as 44.14, 21.42 and 22.54%, respectively.

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In structural design, either the experience of designer is used or a uniform grouping is usually utilized to group the elements. This type of grouping affects the fundamental cost of the buildings, including the cost of concrete, steel and formwork, as well as secondary costs such as laboratory, checking, fabrication and etc. However, the secondary costs are not usually considered in the cost function. Strategies can also be used to automate the grouping of members in structural design. In this strategy beams and columns are automatically grouped into a limited number of groups to achieve the lowest cost. In this study, enhanced colliding bodies optimization algorithm is used to automatically group the beams and columns of the reinforced concrete structures and also to optimize their cost. The proposed procedure applied to three reinforced concrete frames with four, eight and twelve stories and the influence of automatic grouping of the members in optimal cost is investigated. Using this method, the beams and columns are automatically grouped and the results show that the optimal cost obtained from the automatic grouping is less than the manual grouping of the members.

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By incorporating structural engineering, animal husbandry, and veterinary, this interdisciplinary research accomplishes the following two main objectives: 1) design and optimization to reduce the weight of the steel structure skeleton of the stable with ECBO & CBO algorithms; 2) improving the performance of the natural ventilation system in the stable with some changes in the structure's geometric design.
In this study, each algorithm's performance will be investigated in the course of accomplishing the aforementioned objective. Furthermore, using stress ratios by algorithms in each member will be studied. Finally, using the algorithms, a stable steel structure with lower weight is designed.
In this paper, through changing and improving the structure's geometric design, a structure more compatible with the natural ventilation system's requirements is designed. These changes are as follows: 1) design of a taller stable structure; 2) larger design of the air inlets in the joint line between the upper part of the side walls and the lower part of the pitched roof.

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In recent decades, steel was used more than other materials in structural engineering. However, the safety of high-heat steel structures dramatically decreased, due to steel mechanical properties. Therefore, the design process should be done in a way that the structure has the required resistance at high temperatures and during the fire, according to the effect of heat on the performance of steel structures. In this study, the optimal design process of steel structures is considered under the fire load. In the optimal design process, the failure risk of the structure members is considered as a constraint. Therefore, the optimization process requires thermal and structural reliability analysis. A parametric model has been used to analyse the reliability of the structure in the fire limit state. The optimization process is also performed based on the Colliding Bodies Optimization (CBO) algorithm. In order to evaluate the optimal design process, 3 and 6-floors frames have been investigated. The results showed that the members' condition is effective in the structural resistance for the thermal loading. On the contrary, the structure design based on the reliability under the fire load provides a proper prediction from the behaviour of the structure and satisfies the requirements for the common state of design.

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Optimization has always been a human concern from ancient times to the present day, also in light of advances in computing equipment and systems, optimization techniques have become increasingly important in different applications. The role of metaheuristic algorithms in optimizing and solving engineering problems is expanding every day, optimization has also had many applications in water engineering. Every year, the effects of climate change and the water crisis deepen and worsen in many parts of the world, and existing water management becomes much more vital and critical. One of the main centers for water management and control dams reservoirs. In this paper, applying the CBO metaheuristic algorithm, the results of optimization in the operation of the Haraz dam reservoir in northern Iran, which has previously been done with FA and GA algorithms and standard operation system (SOP), are reviewed and compared. With the implementation of the CBO algorithm, all results and key outputs such as program runtime, annual water shortages, and vulnerabilities are much better than previous calculations, all the results are mentioned in the text of the article, but for example, the annual water shortage has reached about 38% of the FA algorithm, about 25% of the GA algorithm and about 13% of the SOP method. The numerical results demonstrate that the CBO algorithm has merits in solving challenging optimization problems and using this innovative algorithm can be an important starting point in the operation of dam reservoirs around the world.

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In this paper the parametric study is carried out to investigate the effect of number of cells in optimal cost of the non-prismatic reinforced concrete (RC) box girder bridges. The variables are geometry of cross section, tapered length, concrete strength and reinforcement of the box girders and slabs that are obtained using ECBO metaheuristic algorithm. The design is based on AASHTO standard specification. The constraints are the bending and shear strength, geometric limitations and superstructure deflection. The link of CSiBridge and MATLAB software are used for the optimization process. The methodology carried out for two-cell, three-cell and four-cell box girder bridges. The results show that the total cost of the concrete, bars and formwork for two-cell box girder is less than those of the three- and four-cell box girder bridges.

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In this study, the finite element model updating was simulated by reducing the stiffness of the members. Due to lack of access to the experimental results, the data obtained from an analytical model were used in the proposed structural damage scenarios. The updating parameters for the studied structures were defined as a reduction coefficient applied to the stiffness of the members. Parameter variations were calculated by solving an unconstrained nonlinear optimization problem. The objective function in the optimization problem was proposed based on the Multi-Degree-of-Freedom (MDOF) equations of motion as well as the dynamic characteristics of the studied structure. Only the first natural frequency of the damaged structure was used in the proposed updating process, and only one vibration mode was used in the updating problem and damage identification procedure. In addition, as elimination of high-order terms in the proposed formula introduced errors in the final response, the variations of natural frequency and vibration mode for higher-order terms were included in the free vibration equation of the proposed objective function. The Colliding Bodies Optimization (CBO) algorithm was used to solve the optimization problem. The performance of the proposed method was evaluated using the numerical examples, where different conditions were applied to the studied structures. The results of the present study showed that, the proposed method and formulation were capable of efficiently updating the dynamic parameters of the structure as well as identifying the location and severity of the damage using only the first natural frequency of the structure.
 

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This paper presents a computational framework for optimal design of non-prismatic reinforced concrete box girder bridges. The variables include the geometry of the cross section, tapered length, concrete strength and reinforcement of box girders and slabs. These are obtained by the enhanced colliding bodies optimization algorithm to optimizing the cost and again CO₂ emission. Loading and design is based on the AASHTO standard specification. The methodology is illustrated by a three-span continuous bridge. The trade-off between optimal cost and CO₂ emission in this type of bridge indicates that the difference of costs, as well as CO₂ emissions in the solution with both objectives is less than 1%. However, the optimal variables in the cost objective are different from the variables of CO₂ emission objective.
 

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Energy production and consumption play an important role in the domestic and international strategic decisions globally. Monitoring the electric energy consumption is essential for the short- and long-term of sustainable development planned in different countries. One of the advanced methods and/or algorithms applied in this prediction is the meta-heuristic algorithm. The meta-heuristic algorithms can minimize the errors and standard deviations in the data processing. Statistically, there are numerous methods applicable in the uncertainty analysis and in realizing the errors in the datasets, if any. In this article, the Mean Absolute Percentage Error (MAPE) is used in the error’s minimization within the relevant algorithms, and the used dataset is actually relating to the past fifty years, say from 1972 to 2021. For this purpose, the three algorithms such as the Imputation–Regularized Optimization (IRO), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO) have been used. Each one of the algorithms has been implemented for the two linear and exponential models. Among this combination of the six models, the linear model of the ECBO meta-heuristic algorithm has yielded the least error. The magnitude of this error is about 3.7%. The predicted energy consumption with the winning model planned for the year 2030 is about 459 terawatt-hours. The important socio-economical parameters are used in predicting the energy consumption, where these parameters include the electricity price, Gross Domestic Product (GDP), previous year's consumption, and also the population. Application of the meta-heuristic algorithms could help the electricity generation industries to calculate the energy consumption of the approaching years with the least error. Researchers should use various algorithms to minimize this error and make the more realistic prediction.