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Monitoring, Understanding and Assessment of Complex Structural Behaviour

Monitoring, Understanding and Assessment of Complex Structural Behaviour
复杂结构行为的监测、理解和评估
批准号:
RGPIN-2016-03733
负责人:
Hoult, Neil
金额:
$2.62万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2019
资助国家:
加拿大
项目状态:
已结题
起止时间:
2019-01-01 至 2020-12-31

项目摘要

项目成果

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中文摘要
翻译
由于需要建造、修理和更换基础设施,加拿大的经济和环境正承受着日益沉重的负担。对于结构工程界来说,通过优化设计、施工和评估方法,减少材料和其他资源的使用,可以帮助减轻这种负担。研究表明,精细化的设计可以减少30%的结构材料的使用,这将导致类似规模的成本和二氧化碳排放量的减少。由于钢铁和水泥生产约占工业二氧化碳排放量的50%,建筑行业占GDP的6%(738亿美元),因此这些节省将是显著的。同样,如果可以缩短施工时间,这将导致减少能源使用和降低成本。最后,对现有结构进行更准确的评估也可以通过延长现有结构的使用时间来节省大量的成本和二氧化碳。然而,由于缺乏关于结构行为的准确信息,工程师通常在设计和分析技术上受到限制。分布式光纤传感器(FOS)、无线传感器网络(WSN)和数字图像相关(DIC)等新型传感器技术可以为填补这一知识空白提供关键信息,并实现优化设计、建造和评估。该研究项目将有助于在如何使用新的传感器技术方面取得突破性进展,以改进:(i)双向板的设计,(ii)大型建筑物的建造,以及(iii)桥梁主梁的评估。***该研究项目将利用加拿大创新基金会资助的实验室设施和仪器技术。拟议的研究将通过以下方式推进结构工程的最新技术:(a)使用大规模实验和分布式传感器系统来了解复杂的结构系统,(b)开发建模或评估这些系统的方法,以便优化材料使用、施工时间和使用寿命,以及(c)为HQP提供加拿大和国外高度期望的工程技能。***研究成果将包括指导如何优化双向板的设计,改进大型建筑物的施工指导,以及改进对变荷载路径对钢筋混凝土梁抗剪能力的影响的理解,这将导致更准确的规范方法。加拿大将从这项研究中获益,降低基础设施成本和环境影响。培养具有结构优化设计领域专业技能和专业工程师核心技能的硕士生3名、博士生3名
英文摘要
An ever increasing burden is being placed on Canada's economy and the environment by the need to build, repair and replace infrastructure. For the structural engineering community there is an opportunity to help alleviate this burden by reducing the use of materials and other resources through optimized approaches to design, construction, and assessment. Research has indicated that refined design could reduce the use of materials in structures by 30%, which would result in cost and CO2 emission reductions of a similar magnitude. These savings would be significant since steel and cement production accounts for approximately 50% of industrial CO2 emissions and the construction industry represents 6% of GDP ($73.8 billion). Similarly if construction timelines can be reduced, this would result in reduced energy use and lower costs. Finally, more accurate assessments of existing structures could also enable significant cost and CO2 savings by allowing existing structures to be kept in service longer. ***However, engineers are often limited in the design and analysis techniques they can use because of the need for conservatism in the absence of accurate information about structural behaviour. New sensor technologies such as distributed fibre optic sensors (FOS), wireless senor networks (WSN) and digital image correlation (DIC) could provide the critical information to fill this knowledge gap, and enable optimized design, construction, and assessment. This research program will contribute to groundbreaking advances in how new sensor technologies can be used to improve: (i) the design of two-way slabs, (ii) the construction of large buildings, and (iii) the assessment of bridge girders.***The research program will take advantage of the unique Canada Foundation for Innovation funded lab facilities and instrumentation technologies available to the applicant. The proposed research will advance the state of the art in structural engineering by: (a) using large-scale experiments and distributed sensor systems to understand complex structural systems, (b) developing approaches to modeling or assessing these systems so that material use, construction time and service life can be optimized, and (c) providing HQP with engineering skills that are highly desired in Canada and abroad.***The outcomes of the research will include guidance for how to optimize the design of two-way slabs, improved construction guidance for large buildings, and an improved understanding of the impact of variable load paths on the shear capacity of reinforced concrete beams that will lead to more accurate code approaches. Canada will see benefits from this research in terms of reduced infrastructure costs and environmental impact. Three MASc and three PhD students with specialty skills in the area of optimized structural design and core skills required of Professional Engineers will be trained.**
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Monitoring, Understanding and Assessment of Complex Structural Behaviour
  • 批准号:
    RGPIN-2016-03733
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.62万
  • 财政年份:
    2020
  • 负责人:
    Hoult, Neil
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