Digital Image Correlation (DIC) System for Assessment of Critical Infrastructure and Digital Twin

用于评估关键基础设施和数字孪生的数字图像相关 (DIC) 系统

• 批准号: RTI-2023-00102
• 负责人: Billah, AbuHenaMdMuntasir
• 金额: $ 10.92万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 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=749305
• 关键词: Digital; Image; Correlation; DIC; System

Experimental testing provides the ground truth to understand the behavior of different structural components and systems under various dynamic loading conditions. It is a challenging task to identify and understand the governing modes of failure in large, complex structures that are subjected to extreme loads. Well thought-out experiments can be extremely valuable in understanding material/structural behavior. However, the data that can be gathered from a single test is limited mainly due to conventional point-based sensor technology, which only allows measurements at a limited number of discrete points. This can be particularly restrictive when considering unique, complex and costly experiments in which collecting the maximum amount of relevant data is desirable. Digital image correlation (DIC) technique can be a viable alternative for advanced non-contact sensing. As opposed to conventional instruments, DIC gives access to full displacement/strain fields on the entire surface of large-scale structures at various critical stages of loading, from the initiation of damage to its progression until complete failure. The requested DIC system will be used by a group of interdisciplinary researchers in the Schulich School of Engineering at the University of Calgary. The equipment is targeted for pursuing research on new generation structural materials and systems, instrumentation for structural health monitoring of Canadian infrastructure, 3D data registration and feature extraction from point cloud data. The objectives of the proposed research programs are to uncover the coupled complex threats of multi-hazard on civil infrastructure, mechanistic studies of decommissioned pipelines, digital twin and smart monitoring of transportation infrastructure, and measurement of the growth of reindeer antlers. The ongoing and planned research are both timely and beneficial, as the activities proposed by the applicants and their team would support and drastically improve current monitoring and bridge design strategies. The research outcomes will provide valuable input for resilient and sustainable bridge design practice in Canada and advanced tools for digital twin for monitoring of civil infrastructure. Industry partners and bridge code committees are eagerly awaiting the research outcomes from these research studies to generate relevant design provisions. Acquisition of the new 3D-DIC will help move the current research forward, launch new research projects, and enhance opportunities for collaboration. It will permit collection of extensive data for the development of new knowledge and better understanding of structural behavior, advance monitoring of critical infrastructure as well as tracking the growth of wildlife through image processing. Above all, the top priority is advancing the research projects of the applicants' large number of graduate students and trainees by upgrading the existing outdated data acquisition system with the requested DIC system.

试验测试为了解不同结构部件和系统在各种动态载荷条件下的行为提供了基本事实。在承受极端荷载的大型复杂结构中，识别和理解破坏的主导模式是一项具有挑战性的任务。经过深思熟虑的实验在理解材料/结构行为方面非常有价值。然而，由于传统的基于点的传感器技术只允许在有限数量的离散点上进行测量，因此可以从一次测试中收集的数据有限。当考虑到需要收集最大量相关数据的独特、复杂和昂贵的实验时，这可能会特别受限。数字图像相关(DIC)技术可以成为先进的非接触传感的一种可行的选择。与传统仪器不同的是，DIC可以在加载的各个关键阶段获得大型结构整个表面的完整位移/应变场，从损伤的开始到破坏的发展直到完全破坏。所要求的DIC系统将由卡尔加里大学舒利赫工程学院的一组跨学科研究人员使用。该设备的目标是进行新一代结构材料和系统、加拿大基础设施结构健康监测仪器、3D数据注册和点云数据特征提取的研究。拟议研究方案的目标是揭示多种灾害对民用基础设施的复杂威胁，对退役管道进行机械研究，对交通基础设施进行数字双胞胎和智能监测，以及测量驯鹿鹿角的生长情况。正在进行和计划中的研究既及时又有益，因为申请者及其团队提出的活动将支持并大幅改进当前的监测和桥梁设计战略。研究成果将为加拿大弹性和可持续的桥梁设计实践提供宝贵的投入，并为监测民用基础设施的数字孪生提供先进的工具。行业合作伙伴和桥梁规范委员会正热切期待这些研究研究的研究结果，以产生相关的设计规定。收购新的3D-DIC将有助于推动当前的研究，推出新的研究项目，并增加合作机会。它将允许收集广泛的数据，以发展新的知识和更好地了解结构行为，推进对关键基础设施的监测，以及通过图像处理跟踪野生动物的生长。最重要的是，当务之急是通过将现有过时的数据采集系统升级为所要求的DIC系统来推进申请者的大量研究生和受训人员的研究项目。