EC Techniques in the Structural Concrete Field

J. L. P. Ordóñez, B. González-Fonteboa, F. M. Abella
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引用次数: 6

Abstract

Throughout the last decades, one of society’s concerns has been the development of new tools to optimize every aspect of daily life. One of the mechanisms that can be applied to this effect is what is nowadays called Artificial Intelligence (AI). This branch of science enables the design of intelligent systems, meaning that they display features that can be associated to human intelligence, search methods being one of the most remarkable. Amongst these, Evolutionary Computation (EC) stands out. This technique is based on the modelling of certain traits of nature, especially the capacity shown by living beings to adapt to their environment, using as a starting point Darwin’s Theory of Evolution following the principle of natural selection (Darwin, 1859). These models search for solutions in an automatized way. As a result, a series of search techniques which solve problems in an automatized and parallel way has arisen. The most successful amongst these are Genetic Algorithms (GA) and, more recently, Genetic Programming (GP). The main difference between them is rooted on the way solutions are coded, which implies certain changes in their processing, even though the operation in both systems is similar. Like most disciplines, the field of Civil Engineering is no stranger to optimization methods, which are applied especially to construction, maintenance or rehabilitation processes (Arciszewski and De Jong, 2001) (Shaw, Miles and Gray, 2003) (Kicinger, Arciszewski and De Jong, 2005). For instance, in Structural Engineering in general and in Structural Concrete in particular, there are a number of problems which are solved simultaneously through theoretical studies, based on physical models, and experimental benchmarks which sanction and adjust the former, where a large amount of factors intervene. In these cases, techniques based on Evolutionary Computation are capable of optimizing constructive processes while accounting for structural safety levels. In this way, for each particular case, the type of materials, their amount, their usage, etc. can be determined, leading to an optimal development of the structure and thus minimizing manufacturing costs (Rabunal , Varela, Dorado, Gonzalez and Martinez, 2005).
结构混凝土领域的EC技术
在过去的几十年里,社会关注的问题之一是开发新的工具来优化日常生活的各个方面。可以应用于这种效果的机制之一就是现在所谓的人工智能(AI)。这一科学分支使智能系统的设计成为可能,这意味着它们显示出与人类智能相关的特征,搜索方法是最引人注目的之一。其中,进化计算(Evolutionary Computation, EC)尤为突出。这种技术是基于对自然的某些特征的建模,特别是生物适应环境的能力,以达尔文的进化论为出发点,遵循自然选择的原则(达尔文,1859)。这些模型以自动化的方式寻找解决方案。因此,出现了一系列以自动化和并行方式解决问题的搜索技术。其中最成功的是遗传算法(GA)和最近的遗传规划(GP)。它们之间的主要区别在于解决方案的编码方式,这意味着它们的处理过程会发生某些变化,尽管两个系统中的操作是相似的。与大多数学科一样,土木工程领域对优化方法并不陌生,这些方法尤其适用于建筑、维护或修复过程(Arciszewski和De Jong, 2001) (Shaw, Miles和Gray, 2003) (Kicinger, Arciszewski和De Jong, 2005)。例如,在结构工程中,特别是在结构混凝土中,有许多问题是通过理论研究同时解决的,以物理模型为基础,实验基准对前者进行制裁和调整,其中有大量因素干预。在这些情况下,基于进化计算的技术能够在考虑结构安全水平的同时优化施工过程。通过这种方式,可以确定每种特定情况下材料的类型、数量、用途等,从而实现结构的最佳发展,从而最大限度地降低制造成本(Rabunal, Varela, Dorado, Gonzalez and Martinez, 2005)。
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