Reconnaissance teams are charged with collecting perishable data after a natural disaster. In the field, these engineers typically record their observations through images. Each team takes many views of both exterior and interior buildings and frequently collects associated metadata that reflect information represented in images, such as global positioning system (GPS) devices, structural drawings, timestamp, and measurements. Large quantities of images with a wide variety of contents are collected. The window of opportunity is short, and engineers need to provide accurate and rich descriptions of such images before the details are forgotten. In this paper, an automated approach is developed to organize and document such scientific information in an efficient and rapid manner. Deep convolutional neural network algorithms were successfully implemented to extract robust features of key visual contents in the images. A schema is designed based on the realistic needs of field teams examining buildings. A significant number of images collected from past earthquakes were used to train robust classifiers to automatically classify the images. The classifiers and associated schema were used to automatically generate individual reports for buildings.

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.

Communities need seismic vulnerability indices to identify which buildings are most susceptible to severe damage during earthquakes. To be of greatest value, these indices should be easy to use and should be vetted against data from previous earthquakes. To date, more than 800 reinforced concrete buildings have been surveyed after earthquakes for the purpose of evaluating a seismic vulnerability index proposed by Hassan and Sozen in 1997. This number includes 130 buildings surveyed after the 6 February 2016 earthquake in Taiwan. The data collected during these surveys consist of descriptions and photographs of damage, structural sketches, and measurements. Analyses of the data indicate that probability of severe damage and failure increases with decreasing column index and wall index (normalized measures of column and wall areas). They also suggest that the exact form of the threshold used to distinguish more vulnerable structures from less vulnerable structures is of little consequence in terms of the probable cost and benefits of the strengthening program this threshold may inform.

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.

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