h. steed1 11arcadis us, inc., tx. *email: …€¦ · · 2017-05-21on hard to access interceptor...

Using Robust Asset Management Technology to Tackle Inaccessibility in Dallas H. Steed1, S. Mandhle11Arcadis US, Inc., TX. *Email: [email protected]

ABSTRACT

In the absence of inspection data due to accessibility issues, statistical probability models were used to leverage the condition of inspected pipes to forecast condition of inaccessible pipes within the Dallas Water Utilities interceptor system. The combination of forecasted condition scores with risk based asset management allowed for the prioritization of selected interceptors for rehabilitation and replacement projects. Arcadis provided DWU with a complete program that allows for input criteria to be amended to reflect current needs of the system, and create budgetary planning information with the goal of reducing overall risk within the network.

KEYWORDS: NASSCO, GompitZ, RRPS, CIP

INTRODUCTION

How do you proactively characterize risk within a system that you can’t inspect? We faced that situation in Dallas. The standard approach, closed-circuit television (CCTV) and manhole inspections, for a Dallas Water Utilities (DWU) project was utilized initially with limited success on hard to access interceptor sewers. More expensive and sophisticated technology (laser and sonar) was next utilized with mixed results, which still left over 20% of the large diameter interceptor pipe in the system off limits to inspection. The costs for access improvements would be prohibitively significant even with the highest risk pipe. These challenges spurred the team to find a solution that would produce reasonable remaining useful life for pipe yet to be inspected. Ultimately, predictive condition modeling, in combination with a customizable capital improvement planning tool and access improvements for the highest risk pipes, provided the needed results.

Many utilities have the equipment, staffing and programs to inspect, evaluate and prioritize repair and replacement programs for their wastewater collection systems with the exception of larger diameter interceptors. Large diameter interceptors are a small percentage of the total wastewater system and require specialized equipment and staffing. Inspecting and evaluating these large interceptors also comes with numerous challenges including access (along creeks and waterways), physical design challenges (offset manholes, curves, distances between access points), and flows (depth, velocities, obstructions). Dallas Water Utilities undertook a program to address these challenges, evaluate technologies and develop a sustainable program for their interceptor system. This program included a phased plan to inspect, evaluate, prioritize and develop a capital program.

DWU operates and maintains over 4,000-miles of wastewater pipelines, of which 231.2-miles are wastewater interceptors larger than 24-inches. DWU is equipped and regularly inspects the

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majority of their system; however the larger size interceptors require specialized equipment that has limited regular inspections and evaluation. These interceptors are mostly concrete pipelines 15 to 84 years in age with the majority being installed over 50 years ago.

METHODOLOGY

Inspections

In September 2010 the multi-phased project began with a desktop review of GIS information, maintenance records and the wastewater master plan to refine the system risk profile and prioritize areas for inspection. This desktop risk refinement identified substantial GIS updates and data modifications that significantly changed the system risk profile and prioritization. Considering inspection budgets and prioritization evaluations, an initial 91 miles of interceptors were identified for Phase 1 field inspection and evaluation.

DWU’s consultant team, conducted scouting activities to locate manholes and evaluate access challenges. The triage approach would utilize the lowest cost inspection technology (zoom camera) to find the “good” pipe and if necessary recommend a CCTV or Laser/Sonar/CCTV follow-up inspection. The zoom camera inspection would also recommend field maintenance activities necessary for inspection with other technologies (cleaning, manhole rehabilitation, access improvements). Below, Table 1 summarizes the anticipated use of technology for the inspection phase of the project.

Table 1: Inspection Technology Plan

Inspection Method Scoped Considerations

Zoom Camera 66.0 miles

Lowest inspection cost, can be deployed to difficult locations, manhole inspection and pipeline inspection, horizontal and vertical curves limit views, no concerns for stuck/lost equipment, cannot be used with offset manholes, may not view entire pipe segment

CCTV 18.1 miles

Medium inspection costs, skid and float deployment, consider hazards for deployment in pipe segment, proven technology and condition scoring, requires nearby truck access

Laser/Sonar/CCTV 6.5 miles

Highest inspection costs, detailed measurements of pipe corrosion and sedimentation, CCTV information and condition scoring, robust robot can travel upstream and downstream from single deployment, requires large truck access and larger manhole openings

Standardized Inspection Scoring



Following the physical interceptor inspection, the condition assessment phase began. The purpose of this phase of the work is to accurately determine the condition of the existing pipe per nationally accepted standards. Then based on the standard, and good engineering judgment, the likelihood of failure or remaining useful life can be estimated. The condition assessment and condition grading of the defects and overall pipe ratings is performed in accordance with the National Association of Sanitary Sewer Companies (NASSCO) Pipeline Assessment Certification Program (PACP) standard. For the success of the DWU inspection and condition assessment program, it is essential that a standard approach is followed to define the existing condition of the inspected assets. This standard approach allows the whole team from inspectors to planning staff to design engineers to effectively communicate the pipe condition.

NASSCO developed the PACP in 2001 in conjunction with the Water Research Centre (WRc) of the United Kingdom to standardize the defect coding and condition grading of sewer pipe inspection in the United States. The standard also includes guidelines regarding proper positioning of the CCTV camera in the pipe along the pipe axis, speed of advancement of the camera, and standard database format, among other things. CCTV operators are trained by NASSCO approved trainers and are required to be re-certified on a recurring interval.

A condition grade is a pre-defined value between 1 and 5 for each defect observed with implications as shown below in Table 2 for structural defects. The PACP includes both Structural and Operations & Maintenance condition grades.

Table 2: Structural Condition Grade Implications

Condition Grade

Condition Defect Description

1 Excellent Minor defects

2 Good Defects that have not begun to demonstrate

3 Fair Moderate defects that will continue to deteriorate

4 Poor Severe defects that will become grade 5 within the foreseeable future

5 Immediate Attention

Defects requiring immediate attention

Structural defects are those that directly impair the structural condition of the pipeline such as joint separations, joint offsets, cracks, fractures, broken and collapsed pipe, and eroded inverts. Concrete pipe corrosion has unique defects which include the following in ascending order of significance: roughness increased, surface spalling, exposed aggregate, missing aggregate, and steel reinforcing visible / corroded. Structural defects are those that may only be addressed through rehabilitation, replacement, or spot repairs. On the other hand, operational defects



include a range of conditions that can either directly affect the performance of the sewer or are indicators of potential structural defects. Operational conditions include debris, sedimentation, grease, intrusions (root or service laterals), pipe sags, hydraulic / capacity problems, and infiltration. Operational defects are those that generally can be addressed by maintenance.

Risk

In order to use prioritize pipe for rehabilitation and replacement projects, there needed to be a system to identify the most critical pipes. The introduction of a risk system based on the likelihood of failure and the consequence of failure allowed for the desired prioritization. The following sections discuss the components of risk in greater detail.

LoF

Likelihood of failure (LoF) includes the consideration of two different failure modes, physical and performance. For this project, we have defined the physical condition to be equivalent to the structural condition. From this point forward, the term structural will be used to describe the physical condition of the pipe. Structural and performance criteria are broken out into separate components, as shown in Figure 1 .

Figure 1: Likelihood of Failure Criteria

The LoF score is comprised of both structural condition and performance condition scores. The sum of the two scores is equal to the LoF score, as shown in the following equation.

Performance Score + Structural Score = Likelihood of Failure Score

This approach results in LoF scores ranging from a minimum of 2 to a maximum of 10. In order to get a score of 2, both the performance and the structural scores would have to be 1, or in other

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Structural Condition

NASSCO PACP Structural Scores from CCTV for Inspected Pipes

OR

GompitZ Predicted NASSCO PACP Structural Scores for Uninspected Pipes

Performance Condition

Capacity

(Based on Hydraulic Modeling)

O&M Issues Score (NASSCO PACP)



words, it would have to be in near perfect condition. A score of 10 would indicate that the interceptor is failing in both areas. Physical condition is based solely on the structural assessment, with scores ranging from 1 (very good) to 5 (very poor). The pipe that was able to be inspected using CCTV was assigned a value based on the condition seen in the actual pipe, based on NASSCO PACP condition scoring standards. For the segments that were unable to be inspected, predictive condition modeling was used to produce scores.

Statistical Modeling

Given the extensive portion of the DWU interceptor system which was not inspected it, became necessary to "forecast" or model the condition of the uninspected pipe based on past pipe inspections. InfraPLAN was retained to apply the GompitZ model and to provide DWU with an estimate of the physical condition for a pipe of interest at any year by using the inspection information from the sample of previously inspected pipe.

GompitZ considers the current condition state of a pipe at a particular time and then predicts when a pipe will likely transition to the next highest condition state. The model represents the transition between condition states using Markov Chain and Matrices of probabilities. A Markov chain is a random model that identifies a sequence of possible events and the likelihood of each event occurring is only dependent upon the state reached in the previous event. Essentially, it suggests that the probability that one event occurs is only influenced by the previous state rather than the whole sequence in its entirety.

Further, GompitZ calls for the Markov chains to be organized within time dependent matrices. This set up guarantees that a pipe will either stay in the same condition or to move to the next. GompitZ is a combination of a Gompertz function and a Probit model. A Gompertz function displays slow growth at the beginning and end of a time period while a Probit model only allows for the dependent variable to take two values. Applying these statistical tools to the given GIS dataset allowed for predictive modeling to provide a forecast of the condition for the remainder of the Dallas system. However, before the model was able to produce results, the input data needed to be analyzed to ensure accuracy.

The first step to this process was to review and ensure that the pipe data, such as material and diameter, were drawn from inspection records. However, there were some discrepancies and missing entries that required attention. Inspection identification numbers were incorrectly assigned, duplicate inspections existed, multiple inspections occurred for the same pipe segments, and there were discrepancies between the pipe and inspection data. In order to address the issues, Arcadis reviewed their own records to identify any errors and then consulted with DWU to come to a resolution. The main attributes that needed review were pipe length, material, and year of installation.

Pipe length data was given by three different sources: GIS length, length of pipe from the inspection report and the length surveyed from the inspection report. Pipe material data was provided by the inspection report and when inconsistencies occurred, Arcadis and DWU collaborated to select the correct option. DWU also provided the year of installation and Arcadis clarified data for entries that were missing. Other data provided by the inspection report that



were considered for the GompitZ model include: inspection date, condition grade and environmental factors (pipe capacity, slope/turbulence).

Each pipe also has a value corresponding to its respective condition score, flow value, velocity, and slope. The condition scores, as mentioned earlier, were provided by the inspections and the values were determined using the standard PACP methodology. Velocity for each pipe was determined using Manning’s Equation which has several inputs such as diameter and slope. Using Manning’s equation the Froude number was calculated to characterize the flow in any given pipe. Locations where the Froude number changed from a value greater than one to a value less than one experienced a change from supercritical flow to subcritical flow, or rather a hydraulic jump. Pipes that had flow undergoing hydraulic jumps were flagged and incorporated into the model. Slopes were determined using GIS data and manual calculations when certain data was unavailable. Upon completion of this step, statistics were able to be produced to better understand the data.

Results from the statistical analysis from InfraPLAN were presented as graphs, charts, and tables in order to completely assess the validity of the data. At first glance, the data seemed to be accurate, but upon closer look it became apparent that several pieces of data did not fit within expectations. Analysis of this data for concrete pipes raised doubt due to the observation that there is old pipe that is scored as good condition pipe. This finding led to further investigation of the inspection logs and CCTV videos for the pipes of interest and the concerns proved valid as many of the pipes were in much worse condition than initially scored during the first round of review. Revisions were made during another round of review to the scores in the database to reflect the true condition of the pipes.

Additional findings were made while observing more of the statistical presentations for PVC pipes. After analysis of the data, it was apparent that the trend is unrealistic as the condition appears to improve over time. Areas of high concern were the younger pipes that had high condition scores. After investigation of these pipes, the scores were once again revised to reflect the true condition of the pipes. It also became apparent that most of the pipes were rated as a 3 and very few of the pipes were coded as a 2 or 4. These findings are not surprising as the defects that coincide with 2 or 4 are more difficult to identify during the inspections, so it is far more likely to see the other scores represented more frequently. Once the data was completely revised, it became necessary to consider all the potential causes for the deterioration of a pipe.

There are many factors that could affect the condition of a pipe over time and in order to have a model that includes those factors, it is necessary to fully understand them. These factors are called covariates, or a variable that has potential to predict the outcome of a study. During the modeling stage there were several covariates that were initially considered to identify any correlation between the pipe condition and that particular covariate. Below are several of the covariates that were studied:

• Pipe Material: Within the Dallas system there are several different pipe materials so each material was considered individually in order to determine if any correlation between the pipe condition and type of material exists. The system includes materials such as



concrete, PVC, VCT, brick, fiberglass, HDPE, RTP and wrapped steel pipe. Some of the materials are not represented in large quantities within the system and for modeling purposes it was necessary to create groups. The materials and the abbreviations used in the analysis of the project is listed below in Table 3. The materials were grouped into 4 main categories and Figure 2 illustrates the breakdown.

Table 3: Pipe Material Abbreviation Key

Abbreviation Material

CONC Concrete

PC Precast Concrete

PCCP Pre-stressed Concrete Cylinder Pipe

RC Reinforced Concrete

PVC Polyvinyl Chloride

ELSE Any other material not previously listed (e.g., steel, clay)

MULT/UNK Multiple and/or Unknown

Figure 2: DWU Pipe Material Grouping

• Discharge of Force Main: Force mains are pressurized pipes that are typically used for moving water when gravity is not the best options. The pipes of interest for this study were those that had connecting force mains 16-inch diameter and larger, given the force



mains are typically conveying older wastewater without much oxygen available until it reaches a possibly turbulent discharge point and connection to the interceptor system. The initial results pf the GompitZ modeling showed a low correlation for this covariant and thus was not included.

• Near Junction of Large Diameter Pipes: As corrosion can be most evident just near the junction structures, consideration was given to pipes upstream and downstream of the junction structures. Specifically, a junction is defined as a location where there is at least one downstream pipe to two upstream pipes which are both large diameter. The study of this covariate included analysis of pipes downstream (DS), upstream (US), upstream and downstream, and not near junctions. From the given dataset there was no trend apparent for any of the scenarios and so this covariate was not included into the predictive model. However, as more data is collected in the future, it may be necessary to reconsider and assess the inclusion of junction data into the model.

• Near Hydraulic Jump: Using the flow data and slope information in the GIS database areas were located where supercritical flow transition to subcritical flow. This is characteristic of a hydraulic jump. Locations of hydraulic jumps were studied and explored for possible correlation to the condition of the pipes. The hydraulic jump data proved to be significant and seemed to contribute as an indicator to the condition. As more data is provided regarding slope and flow it would be prudent to reassess the continued importance of this covariate.

• Evidence of Inflow /Infiltration: Pipes were identified with observed inflow and infiltration (I/I) defects from the condition assessment. It was challenging to see any significant trend that would suggest an influence on pipe condition due to I&I. As previously stated, once more data is collected it is very possible that this factor could have much more significance, but with the limited data it did not make sense to include I&I in the model. Based on the results, there is no confirmation that I/I is the sole contributor to the increased average condition score because the pipes were also older which could imply that age effected the rating.

Once the errors in the data were reconciled and the covariates were considered, GompitZ provided results. The model's output resulted in the probability of a pipe being rated each of the PACP scores for any given year. Further, the model not only projected these probabilities for every pipe in the system but also for each year until 2100. Below, Table 4 shows an excerpt from the dataset that shows the probability of two pipes being any of the ratings for a five year span. Additionally, there is an aggregated state which is essentially an expected value and is determined by using a weighted average method. The aggregate state is the PACP score that best represents a pipe at an age or year of interest. The formula for the aggregated state is listed below, followed by the excerpt from the table.

Aggregated Value = p1(1) + p2(2) + p3(3) + p4(4) + p5(5)

Table 4: Probability Example for Two Pipes



After the modeling and inspections conducted during this project, expected useful life has proven to be much longer than the initial values used. In short, it is apparent that the current assessment of the DWU system indicates pipe is in far better condition than was predicted using the initial desktop study. Based on its probabilistic method, the model predicts that the average structural score of the concrete pipe for the entire Dallas system is 2.98. In Table 5, the average score for each of the next 10 years if no improvements are made to the system are shown.

Table 5: GompitZ Predicted Average System Structural Scores 2016 - 2025

Year

Average System Structural Score

(Including GompitZ)

2016 2.98

2017 3.00

2018 3.02

2019 3.03

2020 3.05

2021 3.06

2022 3.08

2023 3.09

2024 3.11

2025 3.12



The NASSCO PACP inspection scores were completed in December 2015 and after receiving the GompitZ projected 2016 scores for the uninspected sections of the pipes, it was possible to estimate the scores for the system as a whole as shown in Table 6.

Table 6: Structural Condition Results Summary

Structural Condition Segments Miles % Miles

1 1077 53.28 23%

2 602 32.03 14%

3 2438 115.80 50%

4 301 17.72 8%

5 180 12.36 5%

Total 4598 231.19 100%

To further refine the likelihood of failure, performance condition is also analyzed, as there is more than one failure mode for a pipe. Performance condition criteria includes the ability to meet current capacity demands, the ability to meet future capacity demands, and the O&M score that was recorded in the field. The capacity scoring is based on the criteria that DWU has used in the past, which uses both current (2004 model) and future (2050 projected) capacity information in order to determine the capacity score for the pipe.

The O&M scores were previously explained and essentially, the scores were based on maintenance issues (defects) observed at the time of inspection which negatively impact the performance of the pipe, namely water level. Since only a fraction of the pipes were able to be physically inspected, O&M scores are not available for all segments. In the case when no PACP data was available, the pipes were assigned the average O&M score for the entire system which is 1.5. In Table 7 it can be seen that over 60% of the system does not have O&M data, as O&M data is only available for pipes inspected during this project.



Table 7: System O&M Scores

System O&M Score Segments Miles % Miles

1 811 51.10 22.10%

1.5 3180 148.70 64.32%

2 501 25.83 11.17%

3 68 3.42 1.48%

4 34 2.04 0.88%

5 4 0.09 0.04%

Total 4598 231.19 100.00%

In the above table, it is very noticeable how many of the interceptors within system did not have O&M scores available. To look further into trends of actual inspected pipe with O&M scores, Table 8 was created to examine the breakdown of the field-recorded scores.

Table 8: Inspection O&M Scores

Inspection O&M Score Segments Miles % Miles

1 811 51.1 61.95%

2 501 25.83 31.32%

3 68 3.42 4.15%

4 34 2.04 2.47%

5 4 0.09 0.11%

Total 1418 82.48 100.00%

Approximately 82 miles of the interceptor system had O&M scores from the field inspections. Of those 82 miles, more than 60% had an O&M score of 1, indicating that the vast majority of the pipes had either minute O&M defects or none at all. Less than 3% of the inspected pipes received scores above a three, indicating that very little of the system had serious O&M defects that might progress into failure modes. Additionally, the 2.13 miles of pipe with these high O&M scores were coded with defects indicating high water levels, which is a capacity concern rather



than a maintenance issue. It is important to keep in mind that many of the lower level defects (i.e., scores from 1-3) could be issues that can be resolved through maintenance operations such as: grease or sediment accumulation, ragging, encrustation, minor obstructions and the presence of rocks.

The other component of the performance condition is the hydraulic modeling data. Hydraulic modeling information was extracted from flow data from 2004; earlier risk models also used the data to represent the system. The 2004 data was then used to project 2050 flows. Based on 2004 and projected 2050 hydraulic data, an overall hydraulic performance score was created. The criteria is listed below in Table 9.

Table 9: Hydraulic Performance Criteria

Criteria Score

1 2 3 4 5

Current Capacity (2004 Model)

q/Qfull =0.65

q/Qfull =0.85

q/Qfull =1.00

q/Qfull >1; Surcharge predicted at manholes within 3' of ground surface

q/Qfull >1; Predicted overflows

Future Capacity (2004 Model for 2050)

q/Qfull =1.0 N/A N/A

q/Qfull >1; Surcharge predicted at manholes within 3' of ground surface

q/Qfull >1; Predicted overflows

Each pipe was evaluated based on 2004 data showing the current (at the time) capacity and the projected 2050 capacity. A conservative scoring approach was used, which adopted the highest score in either the current or future capacity was used as the overall hydraulic performance score. Unfortunately, not all pipes were evaluated in the 2004 hydraulic modelling. For pipes that do not have data from the model, the average hydraulic score for the entire system, a score of 1.9, was assigned. Table 10 shows that a third of the system earned a performance score of 1 and indicates that a large portion was assigned the average score.



Table 10: Hydraulic Performance Scores based on 2004 Model

Hydraulic Performance Segments Miles % Miles

1 1480 74.05 32.03%

1.9 2074 98.04 42.41%

2 388 19.06 8.25%

3 487 26.51 11.47%

4 19 2.19 0.95%

5 150 11.33 4.90%

Total 4598 231.19 100.00%

It should be noted that due to the lack of system wide hydraulic modelling information and the age of the available information, these scores are provisional. These scores are provided to demonstrate how likelihood of failure is influenced by more than structural condition alone. Updated hydraulics are vital to establishing a more reliable measure of performance and should be incorporated into the condition scoring data as soon as it is available. This will not only update current scores, which are over ten years old at this time, but also, hopefully, provide data where there is currently none. Over forty percent of the system is lacking hydraulic modelling data completely, and, thus, was assigned a score of 1.9.

After the components of likelihood of failure, performance and structural condition, were evaluated and scored, they were added together to produce the likelihood of failure, an indicator with a minimum score of 2 and a maximum score of 10. A score of 2 would imply that the asset received the minimum score of 1 for both performance and structural condition, indicating that the asset was in near perfect condition. A score of 10 would identify an asset with a five in both performance and structural condition, which would be a pipe for which failure is imminent or has already occurred. Table 11 provides a summary of the likelihood of failure scores for the system.



Table 11: Summary of LoF Results

LOF Segments Feet Miles % of System

2 69 18,580 3.52 1.52%

3 951 242,693 45.96 19.88%

4 729 214,339 40.59 17.56%

5 2020 482,204 91.33 39.50%

6 389 108,008 20.46 8.85%

7 263 80,544 15.25 6.60%

8 90 37,356 7.07 3.06%

9 56 27,531 5.21 2.26%

10 31 9,412 1.78 0.77%

Total 4598 1,220,66 231.19 100.00%

The likelihood of failure scores here range from 2 to 10. Scores with non-integer values were rounded up. As shown in the table above, there was decent gradation of scores, with the majority of the scores falling in between 3 and 5. A distribution of the scores can be seen in Figure 3.

Figure 3: Distribution of System LoF Scores

The closer the likelihood of failure is to 2, the less likely the pipe is to fail. These scores have a favorable distribution in that the majority of the scores are concentrated at a score of 5. It is important to remember that these scores include the performance data that is old and incomplete.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2 3 4 5 6 7 8 9 10



This should be updated and the scoring exercise repeated to get a more accurate depiction of the system. With these current numbers, the system appears to be in fair condition. These scores will be combined with the consequence of failure, explained in the following section, to identify pipes and general locations with the highest risk to target for projects.

CoF

CoF scoring was developed with an emphasis on creating granularity and the size was the primary factor in determining the score. If there are critical infrastructure or sensitive areas intersected by the pipe then it will affect the score. Table 12 provides a comprehensive list of all the criteria which were incorporated into the scoring.

Table 12: CoF Scoring Criteria

CoF 1 CoF 2 CoF 3 CoF 4 CoF 5

Diameter <=12

Diameter >12 and <24

Diameter >=24 and <=72

Diameter >72 and <=96

Diameter >96

Major Shopping 50ft

Hospital 100ft Under Buildings

Arterial 60ft Schools 100ft Rail Road or Dart Rail 20ft

Industrial 50ft Canals Rivers 100ft

Major Thoroughfare 60ft

Highway 60ft

Environmentally Sensitive Area 20ft

The initial score is taken as the highest score that the pipe achieves based on any one of its characteristics. Each two additional occurrences of that maximum score will add an additional point to the base score to a maximum of 5. The equation of the CoF score is listed below.

CoF = Maximum Score + (Additional Two Occurrences of Max Score / 2)

In developing a new scoring methodology, the approach was to increase the gradation of scores throughout the system by increasing the size divisions and, effectively the scoring range, from three categories to five. This will also allow this CoF approach to be applied to smaller diameter pipe (i.e., non-interceptor). Previously for DWU, CoF had been scored using a 1-3 scale, with diameter as the primary determining factor of the final score. Secondary consideration was also given to interceptors with smaller diameters which intersected critical infrastructure or sensitive areas.



RESULTS

CoF Results

The CoF score can generally have a minimum score of 1 and a maximum score of 5; however, as all the pipes evaluated for this project were interceptors with a diameter 24 inches or greater, the minimum score possible for the purposes of this report is a 3. The maximum score remains a 5. A summary of the system CoF scores is presented below in Table 13.

Table 13: CoF Scores for DWU WW Interceptors

COF Score

Segments Miles % Miles

3 1689 83.44 36.09%

4 2727 130.54 56.46%

5 182 17.22 7.45%

Total 4598 231.2 100.00%

The consequence of failure for the system is generally high, with the majority of the pipes receiving a score of 4. This is not surprising as all the pipes assessed are interceptors. If any one of these fails, it will have a big impact purely from size alone. If you add other factors, such as intersecting critical infrastructure or sensitive areas, this increases even further. In the future, this CoF scoring could be applied to the entire system, as it has categories that would be applicable to smaller diameter pipe. If the entire system were included in the analysis, the numbers would have even more gradation.

Asset Management

DWU’s interceptors support vital services to customers, and failure represents a risk to the organization from social, environmental or financial ramifications. Through risk analysis, DWU is able to compare the relative risks embedded in the system to make better resource allocation decisions. An evaluation is performed on the LoF (i.e. physical and performance condition considered together) and consequence of failure (CoF) of an interceptor. A risk score is calculated at DWU using the following formula:

Risk = Likelihood of Failure x Consequence of Failure

In this project, Arcadis focused on three areas while developing this approach to risk scoring. The main focus was to achieve gradation and granularity, or a better spread of discrete scores, in order for DWU to allocate resources efficiently to those pipes which need it most. Arcadis sought to accomplish this through CoF criteria such that assets could have scores at non-integer values ranging from 1-5. Secondly, Arcadis wanted to develop a holistic approach to risk which would give more consideration to several categories of consequence, including social, economic,



and environmental, while also stratifying the LoF such that criteria are considered for the performance score. Finally, Arcadis wanted to deliver a tool that provided flexibility to the changing priorities over the years.

It is vital that the risk scoring scale truly represents the system even if it still categorized a majority of the system as high risk. Gradation is important for discerning between selecting one potential project from another, however it is crucial that high risk pipe is not overlooked. The current risk scoring system displays the high risk within the system while still allowing for a certain amount of gradation to assist in the selection and creation of projects by increasing the granularity of the CoF scores though incorporating a way for multiple criteria ranked at the same level of consequence to add additional points to the CoF. The CoF and LoF were multiplied together to attain the risk for each pipe segment. The segments were categorized by risk score and total miles of each risk category were categorized into five ranges according to the risk. The breakoff points for risk score categories are listed below in Table 14.

Table 14: Risk Categories and Corresponding Score Ranges

Risk Min. Score Max. Score

Highest 40 50

High 30 39

Medium 20 29

Low 10 19

Lowest 2 9

Using these risk category ranges, the risk scores were translated into their corresponding categories to give a better picture of the system as a whole. A summary of this exercise is presented in Table 15.

Table 15: DWU Interceptor Sewer Risk Summary

Risk Miles Percent of Total (mi)

Highest 0.57 0.24%

High 8.87 3.84%

Medium 105.65 45.70%

Low 87.77 37.96

Lowest 28.33 12.25%

Total 231.2 100.00%



As seen above, nearly half of the collection mains fall within the lowest and low risk categories, with an additional 46% falling within the medium risk category. Less than five percent of the collection mains are considered high or highest risk. This is good news, as, although quite a large portion of the system is considered medium risk, the highest risk pipes are few and easy to identify for projects.

Rehabilitation and Replacement Planning System

As an aid to the condition monitoring Arcadis developed a Rehabilitation and Replacement Planning System (RRPS) tool. All of the risk scores, based on the likelihood at the time of inspection, were inserted into the RRPS database in order to be utilized by the tool to identify potential projects and funding scenarios. The Arcadis Rehabilitation and Replacement Planning System (RRPS) is a software tool designed for distributed assets managed within a Geographic Information System (GIS). The system applies service level, CoF, LoF and replacement cost criteria in conjunction with budget alternatives to plan for the rehabilitation or replacement (R&R) of linear assets. Upon selecting the most desirable long-term budget scenario, assets can then be grouped into logical CIP projects to begin the design and construction process. The results of the selected long-term scenario and project grouping can be used to update the enterprise GIS to provide visibility of this information to the entire organization.

The CIP modeling process begins by loading the approriate assets inventory into the RRPS project database. In this project, the inventory is composed of the large diameter interceptors that are owned and maintained by DWU. After loading the pipe inventory, the asset cohorts should be defined. Cohorts are used to categorize the linear assets into groups having similar physical characteristics that would affect their expected useful life. Each cohort includes degradation curve information for the current pipe and for the replacement pipe.

Each cohort record requires the following minimum information:

• Material(s) • Minimum Diameter (>Min Diameter) • Maximum Diameter (<=Max Diameter) • Minimum Year (>Min Year) • Maximum Year (<=Max Year) • Initial Pipe Equation Type • Initial Pipe Equation Constant/Y-Intercept • Initial Pipe Equation Exponent/Slope • Replacement Pipe Equation Type • Replacement Pipe Equation Constant/Y-Intercept • Replacement Pipe Equation Exponent/Slope

The deterioration curves for current and replacement pipe are specified by the equation type of linear or exponential, the Y-intercept, and the slope/exponent values.



Exponential: Condition = Y-intercept * e (Exponent * Age) Linear: Condition = (Slope * Age) - Y intercept

The project database is configured to use exponential curves base on a condition value of 1 at installation (age 0) and condition value 5 at end of effective useful life (EUL). Therefore the Y-intercept is 1 and the exponent is defined by the equation: Exponent = ln (Condition / Y-intercept) / Age. For an EUL of 100 years the exponent is ln (5/1) / 100 = 0.01609

The project database has been configured with two cohorts, one for concrete and another for non-concrete material within the interceptor inventory. Concrete is defined to have an 80 year EUL and non-concrete uses a 100 year EUL. For most of the concrete interceptor assets, the cohort EUL is just a placeholder, because the results from the GompitZ analysis will override the cohort degradation curve with a pipe specific curve. The concrete cohort degradation curve is only used for pipes of this material that were not analyzed with the GompitZ model. All cohorts are defined to use replacement material having a 100 year EUL. The replacement degradation is used to predict the effect on system wide condition after each pipe is replaced.

Additionally, there is an R&R Cost table, shown below, that identifies pipe costs by diameter ranges. Each row represents the cost per linear foot for the range of diameters specified (> Min Dia and <= Max Dia). The project database is configured with the cost for inspection and three types of R&R costs (slip lining, cured-in-place pipe, and full replacement). Additionally, the cost for easements are defined for seven areas of the City: Northwest, North Central, Northeast, Central, Southwest South Central and southeast.

The Criticality Thresholds table stores the range of pipe structural condition and performance values within which a service may be provided. R&R will be considered if a pipe reaches either the condition or performance limit for its CoF. The project database is configured with a different range of structural condition values for each distinct criticality (integer values from 1 through 5). Each pipe in the model must therefore be assigned a criticality which represents the CoF. The project database is configured to start replacement at lower (better) condition for



higher CoF pipes and wait for higher (worse) condition for pipes of lower CoF. Different performance scores can be defined, but this project database uses only the highest performance value of 5 for all CoF pipes.

Once all of the processes above are accomplished, the RRPS tool presents the assets graphically or in a tabular form so that projects can be aggregated in a logical manner. Graphic representation of the data allows the user to view the assets based on risk and visually group assets that are contiguous. The reasoning behind this is that these projects would be more sense to be performed if they are contiguous rather than scattered.

The R&R planning process utilized four software tools: Arcadis RRPS, GompitZ, Esri ArcGIS Desktop and Microsoft Access. The RRPS tool is used to develop long-term R&R budget scenarios and quantify changes to the system-wide risk and condition of the modeled assets. GompitZ is a statistical model used to define the structural degradation of each interceptor segment over time. Esri ArcGIS is used to manage the GIS data and define groups of interceptor assets for near-term projects. Microsoft Access is used for database management, system configuration and results reporting. RRPS uses an Access 2003 database configured as an Esri Personal Geodatabase (PGDB). All of these tools seamlessly work together and update to any edits or changes so that the robust features are always up to date.

SUMMARY AND CONCLUSIONS

The 231 miles of the Dallas interceptor sewers were evaluated for this project based on both physical inspection using NASSCO PACP guideline and GompitZ predictive modelling for those pipes which were unable to be inspected. After structural scores were assigned to each pipe, they were then evaluated based on O&M defects and hydraulic capacity to produce a performance score. These two components came together to create the likelihood of failure.

The consequence of failure is a measure of the severity of the effect on the system and the public if a particular asset were to fail. With discussion from Dallas, criteria were developed that were used to rank the segments based on their consequence of failure, ensuring that pipes which met more than one criteria were given more weight than those who might have only met one. The consequence of failure score and the likelihood of failure scores were then multiplied together to determine the overall risk score for the segment. The possible risk scores range from 2 to 50, but actual risk scores ranged from 6 to 45.

The risk categories include the following: Highest Risk (40-50), High Risk (30-39), Medium Risk (20-29), Low Risk (10-19), and Lowest Risk (2-9). Once the segments were distributed amongst the five ranges, it was easy to see, both visually and graphically, the current state of risk the system was experiencing as a whole. Figure 4 illustrates the distribution of the miles of pipe based on risk category. It is clear the majority of the system is in the medium to low risk range, with the medium risk range slightly greater than the low risk range by around twenty miles. It is important to keep in mind that these scores include a significant amount of modeled information when compared to actual inspection information; however, it is a good start to get a picture of



overall risk of the system. The most promising news is that less than one percent of all the interceptors in the system were at the highest risk.

Figure 4: Miles of Pipe Classified by Risk Category

Based on the evaluation of risk for 2016, there is only a small portion of the system which is flagged for high or highest risk. This will simplify the project selection process, as it will be easier to narrow down potential projects based on where the highest concentration of high risk pipe is. The largest risk category is medium. These medium-risk interceptors will eventually progress to high risk. Considering the high number of segments, this could cause a large shift in the distribution of risk. This underscores the importance of monitoring the condition of the system and making risk mitigation and reduction, though rehabilitation or replacement, a priority now in order to reduce unplanned reactive costs in the future.

Finally, the risk system in conjunction with the RRPS planning tool allowed for the prioritization of pipes with inspection data. This prioritization assisted in the creation of rehabilitation and replacement projects that align with the DWU annual budget and with the desire to decrease risk within the system. Additionally, the prioritization of the pipes with GompitZ data allowed for the identification of pipes that required inspection. These suggested inspections would also be required to be considered for budgetary and planning purposes. Ultimately, DWU is left with a program that utilizes robust tools that can be altered to adhere to the current needs of their system and a lack of inspection data will not prevent a thorough evaluation of their network.

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h. steed1 11arcadis us, inc., tx. *email: …€¦ · · 2017-05-21on hard to access interceptor...

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