Title: Autonomous Evaluation of Fire-damaged Concrete Structures

Author(s): M. Z. Naser

Publication: Web Session

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Date: 4/1/2021

Abstract:
We, as civil engineers, and practitioners, continue to favor traditional methods to assess the state of damaged structures in the aftermath of an extreme event (fire, earthquake etc.). Not only that these methods provide us with bare qualitative/opinion-based assessment, but also require the allocation of tremendous resources as well as physical presence of on-site experts – a condition that may not be possible in many scenarios (i.e. remote areas, toxicity/temperature, fear of imminent collapse etc.). The majority of these limitations can be overcome by leveraging recent technological advancements in parallel fields (i.e. computer science, robotics, sensing etc.). This article showcases how such technologies can be adopted to realize a modern, safe, instant, and realistic (quantitative) evaluation of damaged concrete structures. More specifically, this article explores concepts for autonomous assessment (AA) via computer vision (CV) and machine learning (ML) techniques and how AA can be used to; (1) identify failure mechanisms, and (2) propose suitable repair strategies.