RJN Group is a professional engineering consulting firm that aims to provide innovative engineering solutions for its clients. As their company value statement says:
Becky Salazar, a Project Engineer at RJN Group in Denver, Colorado, contributes to this mission as she assists in the development of asset management programs for RJN Group clients.
They have used InfoAsset Planner (InfoMaster) since 2013 and use the software daily to Quality Control (QC) survey data, process large amounts of CCTV data, and prioritize asset rehabilitation programs with risk analysis and rehabilitation planning tools.
RJN Group serves utilities of all sizes across the country - some are quite large in terms of both network size and data. The largest pipe system reviewed with InfoAsset Planner consisted of ~6,000 miles of gravity main pipe!
Salazar has been involved in nine total projects that require advanced analysis based on data that can sometimes be time-consuming to process. She said, “most of RJN’s analysis took place within Access databases and internally developed software. Inflow and infiltration (I/I) rates were assigned, and a cost-benefit ratio was utilized to prioritize rehabilitation recommendations.” However, when a large amount of time is spent processing and QC’ing survey data from field crews, it can limit the number of variables used to obtain a more robust analysis.
According to Salazar, “Previously, RJN primarily used a cost-benefit ratio based on I/I rate, but [the software] has allowed multiple variables to be considered in the same amount of time – like Pipeline Assessment Condition Program (PACP) and Manhole Assessment Condition Program (MACP) scoring, risk scoring, I/I rate, and more.”
Processing, QC’ing, and analysis of data - like CCTV surveys, manhole inspection data, and GIS data – is streamlined with software specifically designed to handle large amounts of asset condition data from various sources.
Salazar said, “The feature I use the most is the survey import. It is an excellent way to QC CCTV survey data, manhole inspection data, and the GIS. It ensures any map updates located in the field are reflected in the GIS and made properly.”
She added, “[The software] has allowed us to take our analysis to the next level, evaluating multiple variables and being able to handle a significantly larger amount of data.”
In describing her approach to asset prioritization with the software, Salazar starts with the data import and validation. Then, she creates a Consequence of Failure (COF) analysis. “This is typically based upon the diameter of the pipes, and proximity to different types of streets, creeks, flood zones, railroads, and facilities like hospitals, fire stations, schools, golf courses, wastewater treatment plants, pump stations, and others.”
“Each receives different scores and buffers. For example, a highway has a buffer of 60ft and a score of five, while an alley has a buffer of 15 ft and a score of two. We work with each client to develop the best fit for them. Therefore, each project can have a unique set of variables to analyze,” she said.
Then, by using an I/I score and peak PACP/MACP scores as a basis, RJN Group quantifies a Likelihood of Failure (LOF) for assets in the network. The integration of the two makes up a risk scenario framework that is flexible to meet the needs of RJN Group and its clients.
“Through discussions with our clients, we develop risk scenarios that most accurately reflected their system. For example, one client preferred a weighted COF x peak LOF. The weighted COF had the most emphasis on proximity to creeks, flood zones, and the diameter of the pipes. The peak LOF - based upon I/I score and peak PACP/MACP Score - ensures that not only structural defects are addressed, but also I/I defects. The risk matrix was slightly altered from the default, and more assets received a high or extreme risk,” Salazar said.
Following the analysis of multiple risk scenarios, RJN Group can generate a decision tree in the software that maps out prescriptive actions to repair, replace, or re-inspect assets in their system.
“The decision tree ultimately sends assets to either a rehabilitation recommendation or a re-inspection cycle,” Salazar explained.
Afterward, she can import survey data to make any necessary updates to the inspection databases in the software, update the GIS data to reflect survey material, diameter, depth, and more. She can determine I/I rates – outside of the software – and bring in the GIS data.
“The decision tree works very well at getting preliminary recommendations,” Salazar noted. With this as a starting point, she can then QC these recommendations against the CCTV records and PACP coding that may vary from operator to operator.
Salazar then compiles all data and creates customizable reports to deliver the recommendations to RJN Group’s asset management clients.
In summary, she said: ”QC’ing survey data from our field crews and subcontractors is much easier and quicker than before. [The software} also catches errors, that were more difficult to catch with previous QC methods. It has allowed us to handle a significantly larger amount of CCTV data in a smaller timeframe. It has allowed the COF, LOF, and risk analysis to be taken a step further."