As the seismic hazard has been updated for the central U.S., state Departments of Transportation (DOTs) find an increasing need to assess the seismic vulnerability of their bridge network. Traditional methods to perform seismic assessment require developing dynamic models for each bridge. However, this approach requires specialized engineering knowledge and information from structural drawings, and is time-consuming. To streamline this important task, a simplified dynamic modeling procedure is described that leverages readily available information from DOTs’ asset management databases. With a minimal amount of additional data items, the asset management database can be used to identify vulnerable bridges rapidly and with sufficient accuracy for the prioritization of retrofit decisions. A detailed analysis of a 100-bridge sample set identified typical vulnerabilities and established corresponding capacity thresholds. The rapid seismic vulnerability assessment methodology is implemented as an Excel macro-enabled tool for bridge owners and asset managers to rapidly assess the vulnerability of each individual bridge based on current information in the database, and then classify the vulnerability of each individual bridge as low, medium, or high. Current DOT databases used for asset management in regions of low-to-moderate seismicity do require some data items be added for a robust assessment. These data items are identified here and leveraged to demonstrate the method. The rapid assessment methodology presented can be implemented to effectively identify the most vulnerable bridges in a bridge network, thus facilitating a rapid state bridge inventory network assessment to prioritize and inform actions such as maintenance and rehabilitation.

With the recent identification of the Wabash Valley Seismic Zone in addition to the New Madrid Seismic Zone, Indiana’s Department of Transportation (INDOT) has become concerned with ensuring the adequate seismic performance of their bridge network. While INDOT made an effort to reduce the seismic vulnerability of newly-constructed bridges, many less recent bridges still have the potential for vulnerability. Analyzing these bridges’ seismic vulnerability is a vital task. However, developing a detailed dynamic model for every bridge in the state using information from structural drawings is rather tedious and time-consuming. In this study, we develop a simplified dynamic assessment procedure using readily-available information from INDOT’s Bridge Asset Management Program (BIAS), to rapidly identify vulnerable bridges throughout the state. Eight additional data items are recommended to be added into BIAS to support the procedure. The procedure is applied in the Excel file to create a tool, which is able to automatically implement the simplified bridge seismic analysis procedure. The simplified dynamic assessment procedure and the Excel tool enable INDOT to perform seismic vulnerability assessment and identify bridges more frequently. INDOT can prioritize these bridges for seismic retrofits and efficiently ensure the adequate seismic performance of their assets.

Departments of transportation (DOTs) throughout the United States maintain vast bridge databases that house information such as bridge services, dimensions, materials, inspection reports, and photographs. These databases are expensive to maintain and have evolved quite gradually over the years. They are meant to be substantial enough, at a bare minimum, to support typical asset management activities and to prioritize maintenance tasks. There is great potential to make use of them to support other decisions. However, these databases often lack certain detailed information related to substructure elements, which is necessary for seismic vulnerability assessment, for example, and would be time-consuming to gather for thousands of bridges in a given region or state. In this study, a technique was demonstrated and validated that reduces the time needed to collect this information, by leveraging artificial intelligence to automate the identification of substructure types using images. We defined categories appropriate for vulnerability assessment task, classifiers were trained to identify visual content, and their performance evaluated. In this paper we illustrate a method to determine whether to use artificial intelligence, human visual confirmation, or a combination of the two, to identify bridge substructure types based on accuracy, cost, and risk tolerance. The technical approach was validated using images from Indiana. This leveraging of artificial intelligence for automated identification of critical bridge characteristics from readily available images could empower asset owners, such as DOTs, to assess their inventory more frequently and with confidence.

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