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

实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础。识别和理解大型复杂结构在极端载荷作用下的失效控制模式是一项具有挑战性的任务。经过深思熟虑的实验对于理解材料/结构行为是非常有价值的。然而，由于传统的基于点的传感器技术，只能在有限数量的离散点上进行测量，因此从单个测试中收集的数据是有限的。在考虑独特、复杂和昂贵的实验时，这可能特别具有限制性，在这些实验中需要收集最大数量的相关数据。数字图像相关（DIC）技术是一种可行的替代先进的非接触式传感技术。与传统仪器相反，DIC可以在加载的各个关键阶段（从损伤开始到其发展到完全破坏）获得大型结构的整个表面的完整位移/应变场。所要求的DIC系统将由卡尔加里大学舒利希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的研究，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的特征提取。拟议研究项目的目标是揭示民用基础设施多重危害的耦合复杂威胁，退役管道的机械研究，交通基础设施的数字孪生和智能监测，以及驯鹿鹿角生长的测量。正在进行的和计划中的研究既及时又有益，因为申请人及其团队提出的活动将支持并大大改善当前的监测和桥梁设计策略。研究成果将为加拿大的弹性和可持续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急切地等待这些研究的结果，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向前发展，启动新的研究项目，并增加合作机会。它将允许收集广泛的数据，以开发新知识，更好地理解结构行为，推进关键基础设施的监测，并通过图像处理跟踪野生动物的生长。最重要的是，通过将现有过时的数据采集系统升级为所要求的DIC系统，推进申请人的大量研究生和培训生的研究项目。

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.