Assessment of Structural Damage Following Tornadoes using Point Cloud

Mario Esquivel Author

07/29/2020 Added

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This video describes a poster that assess the structural damage done following tornadoes in a point cloud.

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[00:00:00.000]Hello, my name is Mario Esquivel and here's my poster.
[00:00:04.156]The title of my summer research is the
[00:00:07.243]assessment of structural damage following
[00:00:09.296]tornadoes using point cloud.
[00:00:11.166]I am an undergraduate research assistant
[00:00:14.017]studying construction management.
[00:00:16.363]The coauthors are Dr. Mohammadi and
[00:00:18.683]my faculty mentor is Professor Wood.
[00:00:21.367]There are also two other research assistants working
[00:00:24.497]with us and their names
[00:00:26.447]are Pooja and Awang.
[00:00:28.648]Oftentimes, tornadoes severely damaged structures,
[00:00:32.648]leaving a path of their destruction
[00:00:34.721]behind. To address these disasters
[00:00:37.551]my team has made it their objective to develop a reliable post disaster damage
[00:00:41.551]identification, predictive model for damage assessment.
[00:00:45.551]This will be useful in emergency response and recovery operations
[00:00:49.551]following catastrophic events. To complete this objective,
[00:00:53.551]we have to follow these methods.
[00:00:56.872]First data is collected using a UAS with a camera. From the images taken
[00:01:01.744]we then process them using Pix4D software to create a point cloud.
[00:01:05.767]This procedure is demonstrated in figure 1. After the point cloud is created,
[00:01:10.894]the utilization of a software called CloudCompare is necessary to segment the
[00:01:15.274]cloud. The cloud is segmented into 14 different categories.
[00:01:20.731]To assess the structural damage,
[00:01:24.011]we looked at the percentage of roof failure and wall structure.
[00:01:28.036]Field assessments done during the same time are also indicators of what level of
[00:01:32.127]damage the structure has endured. Below
[00:01:36.127]there is a 3D convolutional neural network with six convolutional layers and
[00:01:40.757]four fully connected layers.
[00:01:43.029]The segmented and labeled point cloud data within this work will be used to
[00:01:47.929]develop the 3D convolutional neural network models for damage assessment
[00:01:52.300]prediction.
[00:01:55.242]There's also an example of the DJI Mavic Pro used to capture the images as well
[00:02:01.762]as the cloud compare software used to segment the point clouds. Up next,
[00:02:07.657]we have 14 different classifications for the segmented objects.
[00:02:12.156]9 of these labels are more general terms, while the other 5 are our main
[00:02:16.156]focus of work. The general labels are boats, vehicles,
[00:02:20.156]debris, fallen trees, trees, water bodies,
[00:02:24.370]roads, terrain, and poles. The five structural levels are destroyed,
[00:02:29.460]which can be classified by having more than 15% roof structure failure,
[00:02:33.928]failure of wall structure, or more than 25% roof deck loss.
[00:02:39.528]The next is severe,
[00:02:41.895]which has more than 50% roof cover or wall cladding damage, between 5 to 25%
[00:02:48.826]roof sheathing loss, or less than 15% roof structure damage. Moderate, has between
[00:02:55.296]15 to 50% roof cover, wall cladding damage, or less than 5% roof substrate
[00:03:01.810]failure.
[00:03:03.220]Minor damage is less than 15% roof cover or wall cladding damage.
[00:03:07.859]The last label is no damage, which has no visible exterior damage.
[00:03:13.048]So far, during our research time, we have worked on two datasets.
[00:03:17.428]The first was a Mount Juliet housing triple crown neighborhood
[00:03:21.432]and the second was the West Lebanon housing Stonebridge neighborhood.
[00:03:25.452]These data sets are then split up into 4 segments for us to work on together.
[00:03:29.452]For each of these sets,
[00:03:30.892]a UAS derived point cloud was collected from a tornado near Nashville,
[00:03:35.734]Tennessee, which occurred on March 3rd, 2020.
[00:03:39.734]Field assessments are assessments done by the StEER team following the event.
[00:03:45.024]These are useful because images with the UAS are not able to display what
[00:03:49.885]happened to these structures.
[00:03:51.879]They also tend to group similarly damaged structures in one area.
[00:03:58.448]For my labeling results,
[00:04:00.208]we have been able to complete the Triple Crown neighborhood as of now. Figure
[00:04:04.068]8 demonstrates a red,
[00:04:05.378]green and blue image of the point cloud while figure 9 demonstrates the same
[00:04:10.858]image except the objects have been labeled and colored they're represented
[00:04:14.598]label. Analyzing my results,
[00:04:17.780]I found there was a massive debris field toward the North region due to roof
[00:04:22.600]tile and structure failures. Meanwhile,
[00:04:25.220]a majority of the undamaged structures are located in the South of the region
[00:04:29.220]because they happen to be located further away from the path of the tornado.
[00:04:33.220]Generally, most of the structure labels are represented in the same area.
[00:04:38.723]In conclusion,
[00:04:40.204]this research has allowed me to utilize a software to classify point clouds for
[00:04:44.742]different levels of damaged structures.
[00:04:47.252]The field assessments helped create a general region of damaged structures and
[00:04:51.584]this labeled data is used for deep learning models that can identify damage
[00:04:56.560]distribution using the point clouds of damaged areas in the aftermath of
[00:05:01.280]windstorms. Lastly,
[00:05:03.055]future work within this research includes completing the labeling for
[00:05:07.252]Stonebridge, verification of the labels, and processing the tagged point clouds
[00:05:12.642]into instances with multiple labels.
[00:05:14.992]These are the acknowledgements I would like to make because they have allowed me
[00:05:19.382]to be a part of this great research.
[00:05:22.773]Funds by the NsF StEER used by Richard Wood and his team.
[00:05:27.053]Holland Computing Center of the University of Nebraska,
[00:05:29.743]which receives support from the Nebraska Research Initiative and the College of
[00:05:34.835]Engineering Graduate Program at the University of Nebraska-Lincoln through the
[00:05:39.545]Summer Nebraska Engineering Research Program (SNERP).
[00:05:43.545]And that concludes the presentation of my poster. Thank you for your time.

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Assessment of Structural Damage Following Tornadoes using Point Cloud

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