Generative Design for a New Generation of Resilient Steel Structures

新一代弹性钢结构的生成设计

• 批准号: RGPIN-2022-03284
• 负责人: Driver, Robert
• 金额: $ 2.62万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749041
• 关键词: Generative; Design; New; Generation; Resilient

Over the past 20 years, there has been a perceived proliferation of structural collapses triggered by intentional attacks, accidents, errors in design or maintenance, and extreme environmental actions. As a result, intensive research has been carried out to acquire a better understanding of structural behaviour under loading for which a building was not explicitly designed, and perhaps that could not have reasonably been foreseen. The emphasis has been squarely on understanding and diminishing the possibility of what has become known as a "disproportionate collapse", an unfolding of events wherein the extent of collapse becomes completely disproportionate to the magnitude of its initiating event, and often taking place in a progressive "domino" effect from one structural element or system to another. Despite advances in the understanding of structural behaviour under rare, but extreme, loading scenarios, the approach to designing buildings has remained largely conventional, with no paradigmatic shift in the way structures are designed and built for the express purpose of enhancing structural resilience. Instead, resilience is treated as a separate post-design assessment, rather than woven into the design process from the outset. With recent ground-breaking innovations in both computing power and data science, a rich environment has been created wherein generative design techniques have the potential not only to create advanced analytical solutions, but also to evolve our well-worn structural systems into advanced systems never before conceived. The generative design method involves the generation of multiple solutions based on high-level design goals and parameterised constraints. This process inherently sheds pre-existing biases from past experience that tend to limit creativity, and uses data to engender continual improvements over time. The use of evolutionary principles leverages the enormous power of computing and data, while leaving all final design decisions in the sacrosanct domain of the engineering (human) expert. In summary, the proposed research, through the use of generative design principles and algorithms, will demonstrate that not only can greatly enhanced structural resilience be achieved, but it can be done at a modest cost premium. The research program outcomes will provide structural engineers with essential tools for designing buildings capable of arresting collapse by regarding structural resilience as a fundamental design objective from the conceptual design stage through to completion. The research is expected to have a significant impact on standard practice for the design and construction of steel buildings in Canada.

过去 20 年来，故意攻击、事故、设计或维护错误以及极端环境行为引发的结构倒塌现象明显增多。因此，人们进行了深入的研究，以便更好地了解建筑在荷载作用下的结构行为，而建筑没有明确设计，而且也许无法合理预见。重点一直放在理解和减少所谓“不成比例的崩溃”的可能性上，“不成比例的崩溃”是一种事件的展开，其中崩溃的程度与其引发事件的严重程度完全不成比例，并且通常以渐进的方式发生。从一个结构元素或系统到另一个结构元素或系统的“多米诺骨牌”效应。尽管对罕见但极端的荷载情况下结构行为的理解取得了进展，但设计建筑物的方法仍然基本上是传统的，没有为了增强结构弹性的明确目的而设计和建造结构的方式发生范式转变。相反，弹性被视为单独的设计后评估，而不是从一开始就融入到设计过程中。随着最近在计算能力和数据科学方面的突破性创新，已经创建了一个丰富的环境，其中生成设计技术不仅有可能创建先进的分析解决方案，而且有可能将我们陈旧的结构系统发展为前所未有的先进系统受孕了。生成设计方法涉及基于高级设计目标和参数化约束生成多个解决方案。这个过程本质上消除了过去经验中预先存在的、往往会限制创造力的偏见，并使用数据随着时间的推移产生持续的改进。进化原理的使用利用了计算和数据的巨大力量，同时将所有最终设计决策留给工程（人类）专家的神圣领域。总之，所提出的研究通过使用生成设计原理和算法，将证明不仅可以大大增强结构的弹性，而且可以以适度的成本溢价来​​完成。研究项目的成果将为结构工程师提供必要的工具，通过将结构弹性作为从概念设计阶段到竣工的基本设计目标，设计能够阻止倒塌的建筑物。该研究预计将对加拿大钢结构建筑设计和施工的标准实践产生重大影响。