Data-Driven Reliability-Based Design Methods for Piles in Glacial Deposits

冰川沉积物中桩的数据驱动的基于可靠性的设计方法

• 批准号: RGPIN-2020-05451
• 负责人: Liu, Jinyuan
• 金额: $ 1.89万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717366
• 关键词: Data; Driven; Reliability; Based; Design

This proposed research will conduct first of its kind and comprehensive investigation on a large amount of full-scale pile load tests in Ontario soils using innovative artificial intelligence (AI) techniques and develop reliability-based design methods for piles in glacial deposits. Although piles have been used for centuries to support structures in Canada and around the world, challenges still exist today to accurately predict their capacities and settlements due to many uncertainties, in particular ground conditions. Glacial deposits covering most of Canada are well known for their heterogeneous material properties. Thus, it is difficult to accurately characterize the soil properties for optimum designs. Due to these uncertainties, expensive and time-consuming pile load tests are commonly used in practice to verify designs in local ground conditions. Despite plentiful research on pile load tests around the world, a limited number of research has been conducted on piles in glacial deposits, particularly in Canadian soils. For this research, a database of over 200 pile load tests was established over the last few years with accompanying geotechnical reports. These load tests will be back-analyzed using innovative and efficient AI techniques to correlate pile behavior with soil measurements. Based on these AI-driven back-analyses, innovative and reliable reliability-based design methods will be developed to assist engineers to mitigate the risks associated with inconsistent soil conditions. The research findings will result in cost-effective infrastructure developments in Canada. The proposed research is also vital in training HQP for their career development in Canada.

这项拟议的研究将使用创新的人工智能（AI）技术对安大略土壤中的大量全尺寸桩载荷试验进行首次全面调查，并为冰川沉积物中的桩开发基于可靠性的设计方法。虽然桩在加拿大和世界各地已经被用于支撑结构几个世纪，但由于许多不确定性，特别是地面条件，今天仍然存在准确预测其容量和沉降的挑战。覆盖加拿大大部分地区的冰川沉积物以其异质材料特性而闻名。因此，很难准确地描述土壤特性以进行优化设计。由于这些不确定性，在实践中通常使用昂贵且耗时的桩载荷试验来验证当地地基条件下的设计。尽管世界各地对桩载荷试验进行了大量研究，但对冰川沉积物中的桩进行的研究数量有限，特别是在加拿大土壤中。对于这项研究，在过去几年中建立了一个包含200多个桩载荷试验的数据库，并附有岩土工程报告。这些载荷测试将使用创新和高效的人工智能技术进行反分析，以将桩的行为与土壤测量相关联。基于这些人工智能驱动的反分析，将开发创新和可靠的基于可靠性的设计方法，以帮助工程师减轻与不一致的土壤条件相关的风险。研究结果将导致加拿大具有成本效益的基础设施发展。该研究对加拿大HQP的职业发展培训也是至关重要的。