| contributor author | Alireza G. Kashani | |
| contributor author | Patrick S. Crawford | |
| contributor author | Sufal K. Biswas | |
| contributor author | Andrew J. Graettinger | |
| contributor author | David Grau | |
| date accessioned | 2017-05-08T22:21:23Z | |
| date available | 2017-05-08T22:21:23Z | |
| date copyright | May 2015 | |
| date issued | 2015 | |
| identifier other | 43036336.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/78559 | |
| description abstract | There are more than 1,000 tornadoes in the United States each year, yet engineers do not typically design for tornadoes because of insufficient information about wind loads. Collecting building-level damage data in the aftermath of tornadoes can improve the understanding of tornado winds, but these data are difficult to collect because of safety, time, and access constraints. This study presents and tests an automated geographic information system (GIS) method using postevent point cloud data collected by terrestrial scanners and preevent aerial images to calculate the percentage of roof and wall damage and estimate wind speeds at an individual building scale. Simulations determined that for typical point cloud density ( | |
| publisher | American Society of Civil Engineers | |
| title | Automated Tornado Damage Assessment and Wind Speed Estimation Based on Terrestrial Laser Scanning | |
| type | Journal Paper | |
| journal volume | 29 | |
| journal issue | 3 | |
| journal title | Journal of Computing in Civil Engineering | |
| identifier doi | 10.1061/(ASCE)CP.1943-5487.0000389 | |
| tree | Journal of Computing in Civil Engineering:;2015:;Volume ( 029 ):;issue: 003 | |
| contenttype | Fulltext | |
