Autonomous Monitoring and Decision-Making System for Bridge Infrastructure

点击翻译按钮获取中文摘要

There has been an unprecedented growth and adoption of sensors during the last two decades for the purpose of monitoring performance and health of civil infrastructure. This growth has resulted primarily from a combination of factors: unanticipated high-profile failures (e.g., 2013 CP Rail bridge in Calgary, 2007 I-35W Mississippi River bridge), the need to extend the service life of aging infrastructure, and an availability of relatively low-cost measurement and computing hardware. However, this growth has been largely lopsided, with our ability to measure and store data far surpassing our ability to extract meaningful information from them. As a result, scores of structures globally have been instrumented with sensors, while the key task of interpreting useful information from data remains inadequately addressed. Through the proposed research program, we aim to bridge the gap between measurements obtained through sensors and the interpretation of those measurements to produce crucial information regarding the health of structures, specifically bridge infrastructure, through the development of theoretical, computational, and algorithmic tools. This will enable the delivery of powerful technology into the hands of the end user, without the need for the user to have advanced expertise of the technology in order to use it effectively.******To successfully bridge this gap, the following items must be adequately addressed: (i) development of an integrated framework bridging measurements with knowledge (knowledge management); (ii) autonomous implementation, where such information is extracted without the need for intervention of an expert user. Such a framework will allow users to respond if a structural component of a bridge has been compromised following a shock event (e.g., earthquake or vehicular impact), and it will enable users to estimate the remaining lifetime of the structure so that a proper intervention strategy, such as detailed inspection or replacement, can be planned. Through the development of autonomous algorithms, such information can be extracted with a limited need for an expert user. ******The proposal outlines a novel approach to integrate measurements with decision-making, made possible through a combination of theoretical, algorithmic and experimental developments. The proposed research program is transformative by trying to address the crucial gap between measurements and decision-making, thereby reducing the societal risk through the use of sensors. A successful implementation of this technology would enable us to not only save lives from catastrophic failures, but also allow us to extend the life of existing infrastructure, thereby saving capital costs. The inter-disciplinary training plan will train engineers who are equipped to use technology outside the traditional confines of civil engineering such as electronics and signal processing.**