Progress Report Edit #10 Progress Report: Infrastructure Monitoring for Gradual Damage Detection Form an Inservice Light Rail Vehicle Progress Updated Sept. 30, 2017, 4:56 p.m. Principal Investigator Jacobo Bielak Status Active Start Date Jan. 1, 2017 End Date Aug. 31, 2018 Research Type Applied Grant Type Research Grant Program UTC FAST Act Grant Cycle 2017 TSET UTC Abstract Globally, infrastructure is a vital asset for economic prosperity. However, condition assessments to ensure the continued safe operation of these assets tend to be subjective and infrequent. We are developing a low-cost, objective method to continuously monitor rail systems from the vibrations recorded in a passing train as a complement to traditional inspection techniques. With previous support from the University Transportation Center, we have established a long-term monitoring project with the Port Authority of Allegheny County. We have developed and deployed a robust automatic data acquisition and management system. Through this deployment we have been testing and refining our technique. We have instrumented two of Pittsburgh’s light rail vehicles and have been monitoring the rail system from the vibrations in the operational vehicle over the last three years. Our long-term objective is to provide accurate, rapid, nearly continuous assessments of the tracks, track structures and bridges along the line as cost-efficiently as possible. We already have obtained useful results, as we have been able to detect discrete changes in the recorded signals after construction activity on both the track and ballast. We have also developed a systematic method of data analysis to fuse data from multiple instrumented vehicles, and better detect changes in the condition of the rail/track system, and, thus, provide better insights for the Port Authority on high-priority maintenance issues. Our next immediate challenge is to develop suitable algorithms that will enable our monitoring system to detect small gradual continuous degradation in the rail/track system. This is a key step for making the monitoring system a practical damage detection tool. In addition, even though we have recorded vehicle motion as it moves along the entire track, until now, we have analyzed only data recorded on parts of the line that are supported on firm ground. During the course of the proposed project, we plan to begin analyzing also data from the vehicles as they traverse bridges within the line. Ultimately, our goal is to develop an infrastructure asset management technology that can serve not only the Port Authority of Allegheny County but also infrastructure owners for a variety of transportation modes. Through this improved maintenance technology, we will enable safer more efficient transportation. Description Timeline Deployment Plan Expected Accomplishments and Metrics Individuals Involved Email Name Affiliation Role Position marioberges@cmu.edu Berges, Mario CEE Co-PI Faculty - Adjunct jbielak@cmu.edu Bielak, Jacobo CEE PI Faculty - Tenured noh@cmu.edu Noh, Hae Young CEE Co-PI Faculty - Adjunct Budget Funding $127500.00 Documents Type Name Uploaded Proposal Form FY2016_UTC_Technical_Proposal_JBielak_et_al_.doc Jan. 23, 2017, 10:54 a.m. Proposal Budget Bielak_UTC_budget_v1.0.xlsx Jan. 23, 2017, 10:54 a.m. Presentation Damage Diagnosis Algorithms for Indirect Structural Health Monitoring of Bridges Sept. 30, 2017, 4:56 p.m. Presentation Structures as Sensors: Indirect Monitoring of Humans and Surrounding Sept. 30, 2017, 4:56 p.m. Presentation Structures as Sensors: Indirect Monitoring of Humans and Surrounding Sept. 30, 2017, 4:56 p.m. Presentation Structures as Sensors: Indirect Monitoring of Humans and Surrounding Sept. 30, 2017, 4:56 p.m. Match Sources No match sources! Progress - Reporting Period 10/1/2016 - 3/31/2017 #FIXME: get from the grant cycle % Project Completed to Date 25 % Grant Award Expended 0 % Match Expended & Document 0 USDOT Requirements Anticipated Research Outcomes The key distinguishing aspect of our approach in contrast with other indirect monitoring approaches for bridges is that rather than pre-define the features we extract from the sensor acceleration signals, the features we use are automatically defined by the algorithm during the training process, and thus does make assumptions about the generalized applicability of any pre-defined feature to different structural/environmental settings. We anticipate that our approach will open a new research direction for advancing the practice of indirect structural health monitoring, and could finally allow for the implementation and use of these approaches in practice. Did research results confirm or change practice? Our results have the potential of radically changing the way railway bridges and other related rail transportation structures are inspected and monitored. Web Links N/A Issues The remaining challenges to be addressed in the following project period include 1. expanding and validating our approach to be robust to various levels and types of noise. For this purpose, we plan to utilize both numerical simulations and laboratory experiments 2. testing the sensitivity of our approach to the intensity and type of damage. 3. large-scale evaluation with the field data collected from the Pittsburgh light rail system that we already installed through previous years' funding. Accomplishments 1. Demonstrated state-of-the-art breakthrough results on unsupervised gradual damage detection and estimation algorithm for vehicle-based bridge and rail track damage diagnosis 2. Presented our work at PIANC this Fall 3. Engaged in discussions with Arup and Union Pacific for follow-on funding opportunities. In discussion to submit a proposal to other agencies such as FRA and TRB 3. Drafted a journal paper on unsupervised damage diagnosis, to be submitted Fall 2017 4. Drafted a journal paper on the light rail train response data collected from the indirect monitoring system we developed through the collaboration with the Port Authority of Allegheny County 5. The project has been introduced and utilized for a graduate level project course on sensing and data mining for smart structures and systems. This project provided hands-on experience to the students and helped with engaging/inspiring them in transportation safety-related projects. 6. The work is presented through multiple invited seminars at Stanford, CalTech, Princeton, Georgia Tech, etc. to promote collaboration and expansion of the work. 7. Invited as a panel at the Smart Cities Summit to introduce this project and potential applications to and collaboration with the USPS.