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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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Optimization of public space energy consumption can basically improve the savings and the ratio of energy consumption and resources entirely. In this regard any methodology and system to shorten the redundant use of energy in different spots of the public space and to distribute energy based on significance of each zone will contribute in the task. This study has sought to develop a prototype of a multi-function smart system to monitor and control the use of energy in a space in terms of temperature, brightness and ventilation based on the significance of each zone according to the traffic calculated during time periods. Although in the current prototype there has not yet been photovoltaics embedded in the device, it has been accounted for in software section.
The monitoring system performs to monitor and store temperature, light intensity, CO₂ concentration, and traffic at each zone while control system acts based on the zone significance and mechanism used in each energy consuming device including heaters, coolers, lights, etc. Findings on pilot scale shows that optimization of energy usage by such a system can drastically reduce space energy consumption while the optimal configuration of the multi-function system depends on the space conditions. Space conditions include climatic, area, etc. Although zero-energy building require further researches to be realized and utilized, this system can be perceived as first steps toward this goal.

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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.