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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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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 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 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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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.
 

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Tower cranes are essential for both vertical and horizontal movement of materials in construction and port operations. Optimizing their placement is crucial for reducing costs and enhancing overall efficiency. This study addresses the optimization of tower crane placement using the recently developed Mouth Brooding Fish (MBF) algorithm. The MBF algorithm is inspired by the life cycle of mouth-brooding fish, employing their behavioral patterns and the survival challenges of their offspring to find optimal solutions. The performance of the MBF algorithm is compared with the Genetic Algorithm (GA), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO). The results demonstrate that the MBF algorithm is effective and has potential advantages in tackling complex optimization problems.