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The objective of this paper is to present an optimal design for single-layer barrel vault frames via improved magnetic charged system search (IMCSS) and open application programming interface (OAPI). The IMCSS algorithm is utilized as the optimization algorithm and the OAPI is used as an interface tool between analysis software and the programming language. In the proposed algorithm, magnetic charged system search (MCSS) and improved harmony search (IHS) are utilized to achieve a good convergence and good solutions especially in final iterations. The results confirm the efficiency of OAPI as a powerful interface tool in the analysis process of barrel vault structures and also the ability of IMCSS algorithm in fast convergence and achieving optimal results.

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In this paper, controlling the location of the tip of an L-shape beam under gravity field is investigated. The beam is covered with piezoelectric patches. The gravity filed moves the tip of beam downward and the actuators with induced voltage move the tip to the previous location. to optimize the best location and voltages for actuators, the particle swarm optimization algorithm code is developed. The results show that the best position for the most effective actuators is located at the corner of the beam. Also with considering the best location for patches, with lower induced voltage, the location of the tip of beam cab controlled. Also, the results show that with the optimum location of actuators and appropriate voltage lead to using minimum energy with the desired shape in the beam. The results are compared with those reported in previous work.

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The present work deals with optimization and reliability assessment of double layer barrel vaults. In order to achieve the optimization task an improved colliding bodies optimization algorithm is employed. In the first phase of this study, different forms of double layer barrel vaults namely, square-on-square, square-on-diagonal, diagonal-on-diagonal and diagonal-on-square are considered and designed for optimal weight by the improved colliding bodies optimization algorithm. In the second phase, in order to account for the existing uncertainties in action and resistance of the structures, the reliability of the optimally designed double layer barrel vaults is assessed using importance sampling method by taking into account a limit-state function on the maximum deflection of the structures. The results demonstrate that the minimum reliability index of the optimal designs is 0.92 which means that all the optimally designed double layer barrel vaults are reliable and safe against uncertainties.  

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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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Despite comprehensive literature works on developing fitness-based optimization algorithms, their performance is yet challenged by constraint handling in various engineering tasks. The present study, concerns the widely-used external penalty technique for sizing design of pin-jointed structures. Observer-teacher-learner-based optimization is employed here since previously addressed by a number of investigators as a powerful meta-heuristic algorithm. Several cases of penalty handling techniques are offered and studied using either maximum or summation of constraint violations as well as their combinations. Consequently, the most successive sequence, is identified for the treated continuous and discrete structural examples. Such a dynamic constraint handling is an affordable generalized solution for structural sizing design by iterative population-based algorithms.
 

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The cycle basis of a graph arises in a wide range of engineering problems and has a variety of applications. Minimal and optimal cycle bases reduce the time and memory required for most of such applications. One of the important applications of cycle basis in civil engineering is its use in the force method to frame analysis to generate sparse flexibility matrices, which is needed for optimal analysis.
In this paper, the simulated annealing algorithm has been employed to form suboptimal cycle basis. The simulated annealing algorithm works by using local search generating neighbor solution, and also escapes local optima by accepting worse solutions. The results show that this algorithm can be used to generate suboptimal and subminimal cycle bases. Compared to the existing heuristic algorithms, it provides better results. One of the advantages of this algorithm is its simplicity and its ease for implementation.
 

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The main objective of this study is to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures at different seismic performance levels using feed-forward back-propagation (FFBP) neural network models. FFBP neural network models with varying numbers of hidden layer neurons (5, 10, 15, 20, and 50) were trained to predict the maximum inter-story drift ratios of 5- and 10-story steel MRF structures. The numerical simulations indicate that FFBP neural network models with ten hidden layer neurons better predict the inter-story drift ratios at seismic performance levels for both 5- and 10-story steel MRFs compared to other neural network models.