Large quantities of data which contain detailed condition information over an extended period of time should be utilized to prioritize infrastructure repairs. As the temporal and spatial resolution of monitoring data drastically increase by advances in sensing technology, structural health monitoring applications reach the thresholds of big data. Deep neural networks are ideally suited to use large representative training datasets to learn complex damage features. In the previous study of authors, a real-time deep learning platform was developed to solve damage detection and localization challenge. The network was trained by using simulated structural connection mimicking the real test object with a variety of loading cases, damage scenarios, and measurement noise levels for successful and robust diagnosis of damage. In this study, the proposed damage diagnosis platform is validated by using temporally and spatially dense data collected by Digital Image Correlation (DIC) from the specimen. Laboratory testing of the specimen with induced damage condition is performed to evaluate the performance and efficiency of damage detection and localization approach.
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Scheduling Post disaster Repairs in Electricity Distribution Networks with Uncertain Repair Times
Natural disasters, such as hurricanes, large wind and ice storms, typically require the repair of a large number of components in electricity distribution networks. Since power cannot be restored before the completion of repairs, optimally scheduling the available crews to minimize the cumulative duration of the customer interruptions reduces the harm done to the affected community. We have previously proposed approximation algorithms to schedule post-disaster repairs in electricity distribution networks with complete damage information [1]. In this paper, we extend our previous work to the case with incomplete damage information. We model this problem as scheduling a set of jobs with stochastic processing times on parallel identical machines in order to minimize the total weighted energization time. A linear programming (LP) based list scheduling policy is proposed and then analyzed in terms of theoretical performance.
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- Award ID(s):
- 1832287
- PAR ID:
- 10099588
- Date Published:
- Journal Name:
- ACM SIGMETRICS performance evaluation review
- ISSN:
- 1557-9484
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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