International Journal of Optimization in Civil Engineering
Volume:10 Issue: 4, Autumn 2020

Passive systems are preferred tools for seismic control of buildings challenged by probabilistic nature of the input excitation. However, other types of uncertainty still exist in parameters of the control device even when optimally tuned. The present work concerns optimal design of multiple-tuned-mass-damper embedded on a shear building by a number of meta-heuristics. They include well-known genetic algorithm and particle swarm optimization as well as more recent gray wolf optimizer and its hybrid method embedding swarm intelligence. The study is two-fold: first, optimal designs by different meta-heuristics are compared concerning their reduction in structural seismic responses; second, the effect of uncertainty in Multi-Tuned-Mass-Damper parameters, is studied offering new reliability-based curves. Monte Carlo Simulation is employed to evaluate failure probabilities. A variety of structural responses are assessed against seismic excitation including maximal displacement, velocity and acceleration. It is declared that the best algorithm for efficiency and effectiveness has not coincided the best based on the reliability traces. Such traces also show that in a specific range of limit-states, algorithm selection has a serious effect on the reliability results. It was found even more than 35% and depends on the response type.

The purpose of the present study is the damage detection in the thin plates in terms of the wide application of such structures in various branches of engineering such as structural, mechanical, aerospace, shipbuilding, etc. using gradient-based second-order numerical optimization techniques. The technique used for optimization in this study is the second-order Levenberg-Marquardt algorithm (SOLMA). Using the acceleration response in a number of structural nodes under dynamic excitation, identification of the location and extent of damage in the plate elements are obtained by the proposed algorithm over an iterative cycle and by updating the sensitivity matrix. The damage has been assumed in the form of decreased modulus of elasticity in linear mode. A numerical problem has been solved and presented in order to verify and compare the proposed damage detection method with other methods. Also several numerical problems have been solved and its results have been presented in order to evaluate different scenarios such as one or more damages, small or large damage extent, absence or presence of noise with different levels, number of measured responses (number of sensors), position of measured points and the dynamic analysis time of the damage detection problem with the proposed method. The results show the appropriate accuracy, efficiency and performance of the proposed damage detection method.

The present work is aimed at assessing the impact of strong column-weak beam (SCWB) criterion on seismic performance of optimally designed steel moment frames. To this end, different SCWB ratios are considered for steel special moment resisting frame (SMRF) structures and performance-based design optimization process is implemented with the aid of an efficient metaheuristic. The seismic collapse performance of the optimally designed SMRFs is assessed by performing incremental dynamic analysis (IDA) and determining their adjusted collapse margin ratios. Three design examples of 5-, 10-, and 15-story SMRFs are presented to illustrate the efficiency of the proposed methodology.

This paper proposes a GA-based reduced search space technique (GA-RSS) for the optimal design of steel moment frames. It tries to reduce the computation time by focusing the search around the boundaries of the constraints, using a ranking-based constraint handling to enhance the efficiency of the algorithm. This attempt to reduce the search space is due to the fact that in most optimization problems the optimal solution lies on or near the boundaries of the feasible region. All the analyses/optimization steps have been implemented in MATLAB and the method has been validated by optimizing three moment-frame benchmark problems. According to the results, the algorithm performs fit and needs relatively fewer analyses than other metaheuristic algorithms to reach a global optimum solution.

One of the most crucial problems in geo-engineering is the instability of unsaturated slopes, causing severe loss of life and property worldwide. In this study, five novel meta-heuristic methods are employed to optimize locating the Critical Failure Surface (CFS) and corresponding Factor of Safety (FOS). A Finite Element Method (FEM) code is incorporated to convert the strong form of the Richard’s differential equation to the weak form. More importantly, the derived code can consider both the seismic and seepage conditions additional to the static loading. Eventually, the proposed optimization procedure is validated against benchmark examples and some insights are provided.

The efficiency of braced structures depends significantly on structure response under seismic loads. The main design challenge for these type of structures is to select shape, number of spans, and type of connections appropriately. Therefore, introducing an optimized and cost-effective design including a certain level of safety and performance against natural hazards seems to be an inevitable necessity. The present work introduces a performance-based design for braced steel structures as well as an optimized arrangement of braces and connection types via using finite difference algorithm. The results show that the latter two factors are very important and necessary to achieve an optimized design for braced steel structures.

In this paper, set theoretical variants of the artificial bee colony (ABC) and water evaporation optmization (WEO) algorithms are proposed. The set theoretical variants are designed based on a set theoretical framework in which the population of candidate solutions is divided into some number of smaller well-arranged sub-populations. The framework aims to improve the compromise between diversification and intensification of the search and makes it possible to design various variants of a P-metaheuristic. In order to verify the stability and robustness of the set theoretical framework, the proposed algorithms are applied to solve three different benchmark structural design optimization problems. The results show that the set theoretical framework improves the performance of the ABC and WEO algorithms, especially in terms of robustness and convergence characteristics.

Nowadays, the optimal design of structures based on reliability has been converted to an active topic in structural engineering. The Reliability-Based Design Optimization (RBDO) methods provide the structural design with lower cost and more safety, simultaneously. In this study, the optimal design based on reliability of dome truss structures with probability constraint of the frequency limitation is discussed. To solve the RBDO problem, nested double-loop method is considered; one of the loops performs the optimization process and the other one assesses the reliability of the structure. The optimization process is implemented using ECBO and EVPS algorithms and the reliability index is calculated using the Monte Carlo simulation method. Finally, the size and shape reliability-based optimization of 52-bar and 120-bar dome trusses has been investigated.

The increase in the number of construction projects and the involvement of a large amount of resources show that one of the most important actions of any construction project is to select the right contractor for the project. Delays in most construction projects and increased costs compared to initial estimates are often due to inadequacies by contractors, indicating that the contractor has not been properly selected. The complexities of the construction industry and the existing uncertainties have led experts to point out that choosing a contractor is a sensitive and difficult task. The purpose of this paper is to design a fuzzy inference system (FIS) to select the best contractor in conditions of uncertainty. The fuzzy inference system is a powerful tool for handling the uncertainties and subjectivities arising in the evaluation process of contractors. The proposed FIS has a two-step computational process in which 28 criteria are determined to evaluate the contractors. The proposed FIS is applied to evaluate and select the best contractor among 5 contractors considered by the general department of roads and urban development in Shahrekord. The studied criteria for evaluating contractors are categorized in six groups, including good history and credibility, equipment, management and specialized staff, economic-financial, skills-ability, and technical criteria. The results show that technical criteria are determined as the most important criteria for evaluating contractors. Furthermore, the results of applying the proposed FIS reveal that contractor C is the best contractor with the final score of 31.40.

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 structurechr('39')s geometric design. In this study, each algorithmchr('39')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 structurechr('39')s geometric design, a structure more compatible with the natural ventilation systemchr('39')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.