Civil & Environmental Engineering  


Mehdi Kalantari Khandani
Electrical and Computer Engineering

According to the National Bridge inventoryDatabase of the Federal HighwayAdministration in year 2008, the U.S.transportation infrastructure has 601,027bridges from which 71,429 are rated asstructurally deficient. State of Maryland isnot an exception of this issue. Accordingto the above database, the State has atotal of 5,168 bridges from which 396 arerated as structurally deficient. Structuralhealth monitoring is required to anticipatethe impending failure of bridges – as wellas other critical infrastructure such aspipelines, railways, and drilling platforms.Yet, existing instrumentation techniquesfor structural health monitoring of bridgessuffer from non-scalability due to highcost of instrumentation devices, large installationcosts (e.g., due to wiring needs),or high maintenance costs. Currently, theonly practice for monitoring the health ofbridges is a mandated bi-annual manualinspection; however, manual inspectionhas proved extremely insufficient to ensuresafety of bridges, as such inspectionsdo not provide enough information toprevent catastrophic failures.To protect the infrastructure systemsagainst aging, structural malfunction, andcollapse, this project will offer a cost effectiveand scalable solution for the realtime monitoring of important structuralstate quantities such as stress, strain, fatiguecracks, vibration, etc. The solution isbased on patent pending Active RF Test(ART) technology, which incorporatesnovel sensing, energy harvesting, andwireless communication technologies intoa flexible, wireless, and batteryless sensor.The proposed approach is capable ofaccurately identifying structural distresssuch as overstrain, crack initiation andgrowth, and deformation. Key advantagesof the proposed system include (1) flexibilityand compact size of sensors, allowingthem to be applied to curved surfacesand complex geometries; (2) wirelessoperation with a self-contained energyharvesting device as a power supply;and (3) very low cost, enabling a largescale distribution in which data fusiontechniques can be utilized for enhanceddamage severity evaluation and sourcelocation.




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