2019 CSCE Annual Conference - Laval (Greater Montreal)

2019 CSCE Annual Conference - Laval (Greater Montreal) Conference


Title
Productivity analysis of manual condition assessment for sewer pipes based on CCTV monitoring


Author(s)
Ms. Yuan Chen, University of Alberta (Presenter)
Mr. Xianfei Yin
Mrs. Qin Zhang
Mr. Ahmed Bouferguene, University of Alberta, Campus Saint-Jean
Dr. Hamid Zaman, City of Edmonton, OPERATIONAL SERVICES
Dr. Mohamed Al-Hussein, University of Alberta
Abstract

Closed-circuit television (CCTV) monitoring has been widely employed in North America to assess the structural integrity of sewer pipes. This operation is usually conducted in two sequential phases. The first consists of sending operators to collect videos 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. Surprisingly, in many municipalities the video-based assessment of the sewer pipes is conducted manually which is time-consuming and at times demotivating since while very important this diagnosis can be utterly boring. Hence, the duration of condition assessment for sewer pipes is diverse and influenced by multiple factors. Therefore, this paper conducts an assessment productivity analysis model using statistical regression methods in order to investigate the specific factors influencing the duration of manual condition assessment for sewer pipes based on CCTV monitoring. Finally, the proposed method is applied to the case of the City of Edmonton, Canada in order to facilitate productivity improvement for manual condition assessment and human resource allocation.