Calibration and validation of mechanistic-empirical performance models for pavement design and remaining service life analysis

用于路面设计和剩余使用寿命分析的机械经验性能模型的校准和验证

• 批准号: RGPIN-2018-05010
• 负责人: Shalaby, Ahmed
• 金额: $ 5.25万
• 依托单位: University of Manitoba
• 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=749422
• 关键词: Calibration; validation; mechanistic; empirical; performance

The long-term reliability of infrastructure performance is a critical element in fulfilling the Government of Canada strategy to finance highway construction and renewal through raising private and public capital. Therefore, improving the resilience and sustainability of surface transportation infrastructure is a strategic priority for the Canadian economy, and a critical requirement for the safety and security of all Canadians. Over the past decade, the mechanistic-empirical pavement design guide and its implementation software Pavement ME, are being increasingly applied for design and life cycle analysis that are based on fundamental material properties, mechanistic structural responses and verified field performance. These tools have started to replace traditional design methods that are based on an empirical approach.The transition to mechanistic-empirical pavement design requires a substantial level of effort to establish material, traffic and distress databases, and to develop appropriate performance and damage models for the various components. Several Canadian transportation agencies have deferred plans to implement mechanistic-empirical designs until the appropriate requisite databases and models are established. Some agencies, including Manitoba, are exploring the new mechanistic design tools using only default input values that may not be fully responsive to local conditions.The purpose of this Discovery research is to accelerate the local calibration of material databases and to generate high-quality performance models to present an optimized and validated design tool that can be implemented in Manitoba and also adapted to other regions. The work will examine the sensitivity of predicted distresses to individual design inputs in order to assess the reliability of the procedure, and to justify the allocation of resources to additional data collection or laboratory testing. The research will build on the recent findings by the applicant and his research team, for the Manitoba Department of Infrastructure, the City of Winnipeg, and the Yukon Department of Public Works, which produced locally-calibrated material inputs for asphalt concrete and unbound granular materials.The Discovery research will generate fundamental knowledge, and develop design tools to benchmark and model the performance of local materials and distresses more accurately. These tools are critically-needed to support the heavy construction industry, a major sector of the Canadian economy that operates in a highly competitive knowledge-based environment both in Canada and internationally.The research will facilitate exceptional graduate training of HQPs, and will introduce undergraduate students to a research-intensive environment. The trainees will make original research contributions, and acquire highly-desirable analytical and practical engineering skills.

基础设施的长期可靠性是实现加拿大政府通过筹集私人和公共资本为公路建设和更新提供资金的战略的一个关键因素。因此，提高地面交通基础设施的弹性和可持续性是加拿大经济的战略优先事项，也是所有加拿大人安全和保障的关键要求。在过去的十年中，机械经验路面设计指南及其实施软件Pavement ME越来越多地应用于基于基本材料特性、机械结构响应和经验证的现场性能的设计和生命周期分析。这些工具已经开始取代传统的设计方法，是基于经验的approaches.The过渡到机械经验路面设计需要大量的努力，建立材料，交通和损坏数据库，并开发适当的性能和损坏模型的各个组成部分。几个加拿大运输机构推迟了实施机械经验设计的计划，直到建立了适当的必要数据库和模型。包括马尼托巴在内的一些机构正在探索新的机械设计工具，这些工具仅使用可能无法完全响应当地条件的默认输入值。这项发现研究的目的是加速材料数据库的本地校准，并生成高质量的性能模型，以提供一种优化和验证的设计工具，该工具可以在马尼托巴实施，也适用于其他地区。这项工作将检查预测的痛苦的敏感性，以个人的设计输入，以评估程序的可靠性，并证明资源分配额外的数据收集或实验室测试。该研究将建立在申请人及其研究团队最近为马尼托巴省基础设施部、温尼伯市和育空地区公共工程部所做的调查结果的基础上，这些部门为沥青混凝土和非结合颗粒材料生产了当地校准的材料投入。开发设计工具，以更准确地衡量和模拟当地材料和灾害的性能。这些工具是至关重要的，以支持重型建筑业，加拿大经济的一个主要部门，在一个高度竞争的知识为基础的环境中运作，在加拿大和国际。研究将促进特殊的研究生培养的HQP，并将介绍本科生的研究密集型环境。学员将做出原创性的研究贡献，并获得非常理想的分析和实用的工程技能。