Closed-circuit television (CCTV) monitoring has been widely employed in North America to assess the structural integrity of underground drainage infrastructure. This operation is usually conducted in two sequential phases. The first consists of dispatching operators to collect video of sections of pipes using remotely controlled robots equipped with specialized television cameras. In the second phase, the data collected in the field is delivered to the analysis facility, where technologists trained in defect classification can examine the video footage. In many municipalities the video-based assessment of the sewer pipes is conducted manually, and little is known about the productivity of this process. This knowledge gap, combined with the desire to implement better resource management solutions, forms the motivation for this research, in which the efficiency of the analysis of video footage of sewer pipe condition is explored. In fact, the duration of condition assessment of sewer pipes is influenced by multiple factors. Therefore, this paper conducts an assessment productivity analysis that uses statistical regression methods to investigate the specific factors influencing the duration of manual condition assessments for sewer pipes by each technologist using footage from CCTV monitoring. Finally, the proposed method is applied to the case of sewer infrastructure in the City of Edmonton, Canada, in order to facilitate productivity improvement for manual condition assessment and human resource allocation.
|Number of pages||10|
|Publication status||Published - 2019|
|Event||2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019 - Sheraton Laval and Convention Centre, Laval, Canada|
Duration: 12 Jun 2019 → 15 Jun 2019
|Conference||2019 Canadian Society for Civil Engineering Annual Conference, CSCE 2019|
|Abbreviated title||CSCE 2019|
|Period||12/06/19 → 15/06/19|