The City of Markham performs CCTV inspections on city-wide storm and sanitary sewers on a 10-year cycle. The City has approximately 15,400 storm and 14,600 sanitary sewer sections respectively. CCTV inspections determine the internal condition of pipes by reporting observed defects using NASSCO’s PACP standard.
The PACP standard provides a condition grade (0-5) for each code recorded in order to quantify the severity of a defect; grade 0 represents a defect does not exist, while grade 5 represents the most severe defect. PACP also provides several condition ratings that accounts for both the severity of the defect(s) observed as well as the number of defects observed. The PACP Quick Rating, due to its ability to report the worst case scenario within the pipe section(s) proves to be the most useful condition rating for City staff to manage their sewer assets and prioritize rehabilitation, replacement and maintenance activities.
The City of Markham recognizes that efficiencies can be gained in the area of CCTV data processing. Their current CCTV data processing model involves 100 per cent QA/QC of CCTV data, sewer condition mapping, condition analysis for prioritization, assignment of rehabilitation/replacement/maintenance activities and extent, followed by manual project-based planning. Without consistent, quality condition data, all proceeding planning and pipe management decisions may be negatively affected and/or perceived as ineffective. In order to improve data management and asset planning practices, the City of Markham has put an emphasis on quality condition assessment data involving full review of submitted inspections.
NASSCO provides a QA/QC guideline to report on the accuracy of a CCTV inspection survey. This method calculates an “error count” over the total number of observations that should have been made. This guideline lacked the ability to account for the quality of a CCTV video, the accuracy of the codes recorded, the extent of the error(s), and codes that require deletion.
A simplified example suggests; if an operator missed one tap code within a pipe that has four taps present (assuming no other defects), the accuracy of the inspection may be 75 per cent. Conversely, should an operator miss one tap code within a pipe that has 10 taps present (assuming no other defects) then the accuracy of that inspection may result in 90% accuracy. In both circumstances, the operator missed one tap code however, one inspection would have a failed accuracy and the other a pass.
In addition, this method does not account for incorrectly coding cracks in lieu of a fractures; which results in a different condition grade (and potentially a different asset management decision), or coding a tap factory versus a tap break-in hammer; which shows no change in condition grade. Along with knowledgeable PACP certified professionals, the City opted to produce their own QA/QC procedures that both quantifies and qualifies the accuracy and quality of their CCTV inspections.
The City’s CCTV inspection QA/QC protocol first reviews the quality of the CCTV video; camera set-up, clarity of video, cable calibration, etc. Surveys may be rejected and requested for re-survey should the quality of the video impede the operator’s ability to properly record observations and defects. No further review is required.
If the video is accepted, the PACP data undergoes review using a deduct method from a starting score of 100. Errors are categorized as a Large Error (-10 points), Medium Error (-5 points) or Small Error (-2 points) and a set of rules defines each error category.
For example, an error in a mandatory Header field, such as a manhole ID, is considered a Large Error while an error in a non-mandatory field is considered a Small Error. Similarly, in the Details Table, if a code was recorded incorrectly (for example, the code itself or the percentage); then the change/no change in condition grade would result in the extent of the error. No change in condition grade would be considered a Small Error while a change in two condition grades or more (with some exceptions) would result in a Large Error. This method proves to be more flexible in allowing owners to set their own pass/fail thresholds.
The City’s QA/QC protocol was presented to NASSCO’s Infrastructure Assessment Committee and has been accepted to be published in the PACP manual version 8.0 as an alternative method to the original guideline.
One of the most important challenges that municipalities face is finding efficiencies in analyzing and processing data to make informed and defensible asset management decisions.
CCTV inspection programs are NOT limited to the physical field inspections of pipe. There’s an enormous amount of effort spent on data QC, data management, mapping, analysis, pipe and project prioritization before a decision is made on the course of action a particular pipe section should undergo.
TDS Consulting developed and provided the City with a prescriptive decision-support tool using a logic matrix to mimic the manual decision-making processes. Several departments attended a workshop to review defect codes, the extent and occurrences of defects recorded, and map the decision that warrants emergency response, replacement (spot or full length), rehabilitation (spot or full length), maintenance, review or do nothing.
This allows the City to make a decision based on the worst case scenario and focus on three general areas:
1) Emergency Response; includes situations such as missing MH lids, crossbores, cross contamination, and surcharging
2) Assets Requiring Immediate Attention; for capital improvement and the ability to group individual projects into programs,
3) Maintenance programs and maintenance cycles
It also determines when no action is required and the asset is slated for re-inspection. The logic performs as an artificial intelligence as it follows the initial human decision without relying on expertise for data interpretation. The logic is defensible, editable and customizable. In addition, it is simple to build upon by adding financials (cost per linear metre) to the outputs.
City staff have found the tool to be a cost-effective way to automate large-scale condition datasets to drive optimal rehabilitation, capital improvement and maintenance planning, considerably reducing staff time spent on data management, analysis, criticality and determining required action(s).