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Probabilistic Models for Performance-based Engineering, Applied to Tall Wood Buildings

Probabilistic Models for Performance-based Engineering, Applied to Tall Wood Buildings
基于性能的工程概率模型,应用于高层木结构建筑
批准号:
RGPIN-2014-05451
负责人:
Haukaas, Terje
金额:
$1.6万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

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中文摘要
翻译
加拿大人的安全和经济保障取决于建筑环境的完整性。建筑物和基础设施中结构部件的性能不足可能导致严重后果和毁灭性损失。最近发生在国外的事件引起了加拿大市政当局的注意。2011年新西兰克赖斯特彻奇的地震就是一个例子,地震造成了人员伤亡和超过200亿美元的经济损失,并对企业和地区增长产生了长期影响。当其他地震袭击世界各地的现代城市地区时,也观察到类似的影响。在此背景下,加拿大日益城市化,特别是在温哥华和蒙特利尔周围的地震活跃地区,提出了本提案中要解决的重要问题。结构工程师必须平衡许多问题,从建筑物的潜在损坏和恶化到施工对环境的影响。事实上,必须采用生命周期观点,考虑到若干直接和间接成本以及一系列危害。该提案的一个关键问题是地震造成的损害,但目标不是寻求零损害。这是因为改进新设计和现有设计的成本是巨大的,无论是在货币方面还是在环境影响方面。此外,许多不确定性来源阻碍了对未来发生的危害、结构性能和成本的确定性预测。因此,目标是帮助工程师在对风险进行全面考虑的基础上识别和沟通资源的最佳分配。虽然这是一个被广泛接受的理想,但它需要一系列模型,其中许多模型要么不可用,要么只在学术研究中使用。因此,需要大量的工作来用预测模型补充保守的代码方程。因此,自伽利略首次尝试预测梁的承载能力375年以来,结构工程师改进其模型的努力与以往一样重要。该提案的愿景是,未来的工程师将使用计算机模拟建筑环境模型作为量化、缓解和沟通风险的基础。模拟将全面模拟建筑物和基础设施在其生命周期中遭受的危害,结果将量化随之而来的成本。为了应对这一挑战,提出了一项广泛的研究计划,旨在改进现有的计算机模型,开发新的模型,并将两者统一在一个整体分析框架中。基于申请人的专业知识和新开发的软件平台,将特别关注概率分析。研究成果将具有广泛的适用性,但试验平台将应用于高层木结构建筑。在不列颠哥伦比亚省的倡议推动下,以及世界范围内高层木结构建筑的复兴,这项研究将有助于安全、可持续地将此类建筑重新引入地震区域。选择的材料是交叉层压木材(CLT),这是一种在多层建筑中市场份额不断增长的新产品。CLT的引入正值加拿大和世界各地的建筑规范委员会试图用一种更加基于性能的方法来修改规范性设计规则。由于这个原因,并且由于在地震区域的高层CLT建筑经验有限,这种类型的建筑代表了使用性能预测和整体考虑风险作为设计基础的理想机会。为此目的开发计算机模型和工具是本建议的主要目标。
英文摘要
The safety and economic security of Canadians depend on the integrity of the built environment. Inadequate performance of structural components in buildings and infrastructure can lead to dramatic consequences and devastating losses. Recent events abroad have served notice to Canadian municipalities. One example is the 2011 earthquake in Christchurch, New Zealand, which caused casualties, monetary losses in excess of $20 billion, and long-term impacts on businesses and regional growth. Similar impacts have been observed when other earthquakes have struck modern urban regions around the world. On this background the increasing urbanization of Canada, particularly in the seismically active regions around Vancouver and Montreal, raises important concerns that are addressed in this proposal. Structural engineers must balance many concerns, ranging from potential damage and deterioration of buildings to environmental impacts of construction. In fact, a life-cycle viewpoint must be adopted, which considers several direct and indirect costs in conjunction with an array of hazards. A key concern in this proposal is damage due to earthquakes, but the objective is not to seek zero damage. This is because the cost of improving new and existing designs is substantial, both in monetary terms and also in terms of environmental impacts. Moreover, many sources of uncertainty prevent deterministic predictions of what will happen in the future in terms of hazards, structural performance, and costs. As a result, the goal is rather to help engineers identify and communicate the optimum allocation of resources based on a holistic consideration of risk. While this is a broadly accepted ideal, it requires an array of models, many of which are either unavailable or employed only in academic research. Substantial work is therefore required to complement conservative code equations with predictive models. Thus, 375 years since Galileo’s first efforts to predict the load capacity of beams, efforts by structural engineers to improve their models are as important as ever. The vision leading this proposal is that engineers in the future will use computer simulation models of the built environment as a basis for the quantification, mitigation, and communication of risk. The simulations will comprehensively model the hazards that the buildings and infrastructure are subjected to during their life cycle, and the results will quantify the ensuing costs. To address this challenge, an extensive research program is proposed with the aims of improving existing computer models, developing new ones, and unifying both in a holistic analysis framework. Building on the applicant’s expertise and a newly developed software platform, particular attention will be devoted to probabilistic analysis. The research outcomes will have broad applicability but the testbed application will be tall wood buildings. Prompted by initiatives in British Columbia, as well as a renaissance in tall wood buildings worldwide, this research will contribute to the safe and sustainable re-introduction of such buildings into seismic regions. The material of choice is cross-laminated timber (CLT), which is a new product with a growing market-share in multi-storey construction. The introduction of CLT comes as building code committees in Canada and around the world are seeking to amend prescriptive design rules with a more performance-based approach. For that reason, and because there is limited experience with tall CLT buildings in seismic regions, this type of construction represents an ideal opportunity to use performance predictions and a holistic consideration of risk as design bases. The development of computer models and tools for this purpose is the principal objective of this proposal.
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会议论文
Disaster Resilience of Urban Communities in Canada: New Probabilistic Models and Computational Methods
  • 批准号:
    RGPIN-2019-03991
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.89万
  • 财政年份:
    2022
  • 负责人:
    Haukaas, Terje
  • 依托单位:
Disaster Resilience of Urban Communities in Canada: New Probabilistic Models and Computational Methods
  • 批准号:
    RGPIN-2019-03991
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.89万
  • 财政年份:
    2021
  • 负责人:
    Haukaas, Terje
  • 依托单位:
Disaster Resilience of Urban Communities in Canada: New Probabilistic Models and Computational Methods
  • 批准号:
    RGPIN-2019-03991
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.89万
  • 财政年份:
    2020
  • 负责人:
    Haukaas, Terje
  • 依托单位:
Disaster Resilience of Urban Communities in Canada: New Probabilistic Models and Computational Methods
  • 批准号:
    RGPIN-2019-03991
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.89万
  • 财政年份:
    2019
  • 负责人:
    Haukaas, Terje
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
新型手性NAD(P)H Models合成及生化模拟