Title:
Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators
Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators
| dc.contributor.advisor | DesRoches, Reginald | |
| dc.contributor.author | Ray, Soumitry J. | |
| dc.contributor.committeeMember | Chau, Duen Horng (Polo) | |
| dc.contributor.committeeMember | Vela, Patricio A. | |
| dc.contributor.committeeMember | Cho, Yong K. | |
| dc.contributor.committeeMember | Narasimha, Rajesh | |
| dc.contributor.department | Computational Science and Engineering | |
| dc.date.accessioned | 2014-06-09T18:19:29Z | |
| dc.date.available | 2014-06-09T18:19:29Z | |
| dc.date.issued | 2014-03-26 | |
| dc.description.abstract | Struck-by fatalities involving heavy equipment such as trucks and cranes accounted for 24.6% of the fatalities between 1997-2007 in the construction industry. Limited visibility due to blind spots and travel in reverse direction are the primary causes of these fatalities. Blind spots are spaces surrounding an equipment that are invisible to the equipment operator. Thus, a hazard is posed to the ground personnel working in the blind spaces of an equipment operator. This research presents a novel approach to intelligently identify potential hazards posed to workers operating near an equipment by determining the visible and blind space regions of an equipment operator in real-time. A depth camera is used to estimate the head posture of the equipment operator and continuously track the head location and orientation using Random Forests algorithm. The head posture information is then integrated with point cloud data of the construction equipment to determine both the visible and the blindspots region of the equipment operator using Ray-Casting algorithm. Simulation and field experiments were carried out to validate this approach in controlled and uncontrolled environment respectively. Research findings demonstrate the potential of this approach to enhance safety performance by detecting hazardous proximity situations. | en_US |
| dc.description.degree | Ph.D. | |
| dc.embargo.terms | null | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/51968 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Head posture estimation | en_US |
| dc.subject | Vehicle blindspots | en_US |
| dc.subject | Proximity | en_US |
| dc.subject | Safety | en_US |
| dc.subject.lcsh | Construction industry | |
| dc.subject.lcsh | Construction equipment Safety measures | |
| dc.title | Intelligent hazard identification: Dynamic visibility measurement of construction equipment operators | en_US |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | School of Civil and Environmental Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isOrgUnitOfPublication | 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| thesis.degree.level | Doctoral |