Abstract

Structural optimization problems aim at increasing the performance of the structure while decreasing its costs guaranteeing, however, the applicable safety requirements. As these aspects are conflicting, the formulation of the structural optimization problem as multiobjective is natural but uncommon, and has the advantage of presenting a diverse set of solutions to the decision maker(s). The literature shows that Evolutionary Algorithms (EAs) are effective to obtain solutions in multiobjective optimization problems, and that the Differential Evolution (DE) based ones are efficient when solving structural mono-objective structural optimization problems, specially those with a real encoding of the design variables. On the other hand, one can note that DE has not yet been applied to the multiobjective version of these problems. This article presents a performance analysis of a DE-based algorithm in five multiobjective structural optimization problems. The obtained results are compared to those found in the literature, and the comparisons indicate the potential of the proposed algorithm.

Abstract

In this paper we study the performance of two stochastic search methods: Genetic Algorithms and Simulated Annealing, applied to the optimization of pin‐jointed steel bar structures. We show that it is possible to embed these two schemes into a single parametric family of algorithms, and that optimal performance (in a parallel machine) is obtained by a hybrid scheme. Examples of applications to the optimization of several real steel bar structures are presented.

Abstract

Is presented hereby the creation of an optimization software for the design of reinforcing steel for any type of structural elements which may be considered as columns, pillars, pilots or foundation dies, subjected to flexo-compression mechanic stresses for rectangular and circular cross section elements with the support of computational numeric methods and a new mathematical original model named "The idealized steel profile" for the optimization of constructions costs, in which the reinforcing steel is idealized as a steel structural profile embeded in concrete columns, so that then an approxiamte required reinforcing steel area may be determined in order to compute all the possible reinforcing bar options over the element cross section making comparisons in structural efficiency and cost for each option generated to then determine the optimal topology regarding cost and efficiency. The optimization problem is adapted so that it makes the resultant designs practical and convenient for its application in the construction industry in Mexico, based on certain design criteria from the <span style="color: #800000;">NTC-2017</span> (Technical Construction Normative of Mexico City) and questioning other such criteria.

It will be shown how such optimization algorithm was adapted, as well as other general numeric methods for engineering. Moreover the implementation of this program as a software for its practical application in the industry for any concrete structure with "n" number of concrete elements is presented. At the end, results of various experiments with the optimization program are shown, making comparisons between different resultant structural models regarding structural eficciency and geometry.

Abstract

The present work aims to define formally the method termed as \textit{``Idealized Smeared Reinforcement'' (ISR)} for optimization in the design of reinforcing steel in concrete structures, its boundaries, background and potentials. Such method has been extensively used implicitly in many different studies of reinforced concrete structures related with optimization and mechanical structural behaviour, but it has not yet been formally established as a method itself even though it represents a great solution approach when designing optimally concrete sections, which is a tendency so vital nowadays for sustainability in construction projects. A general survey of such method of the ISR will be presented on in this document, presenting relevant background research related to the use of the method and optimization of reinforcing steel in general for concrete structures, thereafter proposals of different optimization methods, both classical optimization and meta-heuristic methods will be regarded as possible approaches to apply the ISR, specifically Gradient Descent Optimization methods when one variable for the ISR is considered and meta-heuristics for more than one variable involved given their versatility and flexibility to adapt to different problems, particularly the Particle Swarm Optimization PSO method and the Genetic Algorithm GA. At the end, these solution approaches for the application of the ISR method will be compared for the testing of rectangular solid geometries with different analysis parameters in order to show how adaptable and feasible such ISR method might be using a proper optimization algorithm and analysis for its approach when designing reinforcing steel.

Abstract

Nowadays, the need to deal with limited resources together with the newly discovered awareness of the human over-exploitation of the environment, has made the optimization a cutting edge topic both in scientific research and in the different professional fields. In this paper, a particular evolutionary optimization algorithm is presented: The Estimation of Distribution Algorithm (EDA). This type of algorithm has been developed to be used in search-based constrained optimization problems which are difficult and time-consuming to be solved by other general algorithms. Being an evolutionary algorithm, the main idea is to generate a population of solutions and evaluate the objective function of each one of them. Then, using the information obtained from the previous generations, the algorithm step-by-step will generate new populations that will tend to the best value of the objective function. In EDA, the population of solutions defines a probability density function (PDF) and, by integration, a cumulative density function (CDF), which is used for the generation of the next generation. In structural design optimization problems, it is very common that the best solution is very close to the constraint function. The main advantage of applying the EDA for constrained optimization problems is that each generation of solutions is obtained starting from a PDF that is defined on the whole domain. This means that, for each generation, the solutions have a probability to be on the unfeasible domain space, maintaining the information about the objective function in the evolution process. In the present research, an original EDA and related self-made code are presented together with a specific application to structural optimization problems, in order to show the effectiveness of the obtained results and to make a comparison with other evolutionary optimization algorithms.

Abstract

The manufacturing process of a multi‐axial load cell requires robust tools to analyze the structural behavior when it is subjected to static and dynamic loads. In this work, it is given focus to the problem of optimizing a multi‐axial load cell model (6 load components) of Lywood type considering concepts of reliability‐based design optimization. The reliability structural analysis problem is handled including the variability effect of geometric and physical properties. The load cell is analyzed by finite element method and the reliability constraint is applied to the strength limit. The objective is the maximization of the first natural frequency and simultaneously reducing the mass and corresponding strains without violating reliability limit set as a limit. Comparisons between the deterministic optimization procedure and the proposed method are presented and it is found that the deterministic optimization points may have a high probability of failure so justifying in this type of problem the reliability‐based optimization. Different values of the safety factor were also tested with the optimization model, and it was possible to analyze, a posteriori, the reliability of the optimization results.

Abstract