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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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A new meta-heuristic algorithm is proposed for optimal design of reinforced concrete (RC) frame structures subject to combinations of gravity and lateral static loads based on ACI 318-08 design code. In the present work, artificial bee colony algorithm (ABCA) is focused and an improved ABCA (IABCA) is proposed to achieve the optimization task. The total cost of the RC frames is minimized during the optimization process subject to constraints on demand capacity ratios (DCRs) of structural members. Three benchmark design examples are tested using ABCA and IABCA and the results are compared with those of presented in the literature. The numerical results indicate that the proposed IABCA is an efficient computational tool for discrete optimization of RC frames.

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Due to several uncertainties  which  affect structural responses of Reinforced concrete (RC) frames,  it is sensibly required  to  apply  a vulnerability analysis tool such as fragility curve. To  construct an analytical fragility curve, the incremental dynamic analysis (IDA) method has been extensively used as an applicable seismic analysis tool. To employ the IDA method for constructing fragility curves of RC frames,  it is important  to know  how many records will  be  adequate  to  assess  seismic  risk  analysis  properly?  Another  issue  is  to  know  how many IDA steps  are  required for developing an accurate fitted fragility function? For this purpose,  two 3D RC frames  called  3STRCF and 5STRCF have been nonlinearly modeled and 200 2-componets actual records have been considered for  the  IDA. The results  reveal that at least 15 IDA steps  are  required  to  reduce  fragility function error to less than 5% and 10  IDA  steps  are  required  to  yield  less  than  10%  error.  In  addition,  it  is  revealed  that  a selection of 100 records is completely adequate to be certain   to have  an accurate fragility curve. It is concluded that at least 25 records are required to decrease fragility curve error to less than 5% and 15 records to  have  less than 10%. The closeness of fragility curve error variation for two models and in all limit states show that these results can be generalized to other RC frames.

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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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The main object in optimizing reinforced concrete frames based on the performance is decreasing the initial cost or life cycle cost or total cost. The optimization performed here is with the requirement of satisfying story drifts and rotation of plastic hinges. However, this optimization may decrease seismic strength of the structure. Newton Meta-Heuristic Algorithm (NMA) was used to optimize three-, six-, and twelve-story reinforced concrete frames based on the performance and utilizing the cost objective function. The seismic parameters of the optimized frames were calculated. The results showed that the inter-story drifts at the performance level of LS controls the design. According to the results, the objective function for construction cost is not useful for the optimization of the reinforced concrete frames. Because the amounts of the over strength, the absorbed plastic energy, and the ductility factor for the optimized frames are low using the objective function for the construction cost.

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The main objective of this study is to optimize reinforced concrete (RC) frames in the framework of performance-based design using metaheuristics. Three improved and efficient metaheuristics are employed in this work, namely, improved multi-verse (IMV), improved black hole (IBH) and modified newton metaheuristic algorithm (MNMA). These metaheuristic algorithms are applied for performance-based design optimization of 6- and 12-story planar RC frames. The seismic response of the structures is evaluated using pushover analysis during the optimization process. The obtained results show that the IBH outperforms the other algorithms.
 

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In this paper an enhanced ant colony optimization algorithm with a direct constraints handling strategy is proposed for the optimization of reinforced concrete frames. The construction cost of reinforced concrete frames is considered as the objective function, which should be minimized subject to geometrical and behavioral strength constraints. For this purpose, a new probabilistic function is added to the ant colony optimization algorithm to directly satisfy the geometrical constraints. Furthermore, the position of an ant in each iteration is updated if a better solution is found in terms of objective value and behavioral strength constraints satisfaction. Five benchmark design examples of planar reinforced concrete frames are presented to illustrate the efficiency of the proposed algorithm.