Managing Paved Assets: YCDSB Pavement Management System for Parking Lots & Play Areas

Transcription

1 Managing Paved Assets: YCDSB Pavement Management System for Parking Lots & Play Areas Wael Bekheet, Ph.D., P.Eng. Faculty of Engineering, Alexandria University 1 El-Horreya Ave., El- Hadarra, Alexandria, Egypt, Phone: +2 (03) Fax: +2 (03) Harry Sturm, M.Sc., P.Eng. harry.sturm@stantec.com Stantec Consulting 7070 Mississauga Road, Suite 160 Mississauga, Ontario L5N 7G2 Ph: (905) Fx: (905) Amir Abd El Halim, M.Sc., P.Eng. aabdelhalim@stantec.com Stantec Consulting 49 Frederick Street Kitchener, Ontario N2H 6M7 Ph: (519) Fx: (519) Joe McLoughlin Manager Maintenance Services York Catholic District School Board Catholic Education Centre 320 Bloomington Road West Aurora ON L4G 3G8 P: Ext: 2387 F: Cory M. Gastis Maintenance Controller York Catholic District School Board Catholic Education Centre 320 Bloomington Road West Aurora ON L4G 3G8 P: Ext: 2306 F: Formerly; Stantec Consulting, Kitchener, Ontario, Canada

2 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 1 ABSTRACT: Many private, public or commercial enterprises have a large percentage of their assets capital and maintenance budgets directed towards paved assets. They need to maintain these assets at an acceptable level of service in terms of both the functional condition and level of safety. York Catholic District School Board (YCDSB) manages approximately 100 facilities. Recently, they initiated a project to inventory the condition of their paved assets and to develop a paved assets management system. The implementation of the system was a particular challenge, since these assets represented individual pavement sections with no historic performance data, and are significantly different from a traditional road functional and geometric perspective. Once the assets were assessed, all the collected data was loaded into a management system database where the analysis was performed to identify the current and future network needs. The implementation involved development of a set of analysis models including performance indices, decision trees, and performance prediction models. The implementation of the YCDSB paved asset management system was successful, and showed the potential use of existing analysis tools in innovative applications within the framework of an asset management system. INTRODUCTION Frequently, agencies which have a number of asset types tend to overlook pavements even though a large percentage of their assets may be invested in pavements and significant capital and maintenance budgets are directed towards these paved assets. School boards are one example where there is a need to maintain these assets at an acceptable level of service in terms of both functional condition and safety level. Traditional pavement management tools are typically geared towards road networks, and given the special nature of these paved assets, using these tools to manage such assets represents a particular challenge. The York Catholic District School Board (YCDSB) is one such agency, which manages approximately 100 facilities including schools and administration buildings. In 2006, the YCDSB initiated a project to assess the current condition of the paved assets and other soft surfaces at all its facilities, and to develop a pavement management system for the paved assets. Soft surfaces were areas such as soccer fields and areas were children congregated during lunch hours or recess. The condition assessments were carried out using a visual distress survey based on a modified version of the US Corps of Engineers Pavement Condition Index (PCI). The objective of the condition assessment was to inventory and assess the condition of the assets including paved surfaces, curbs, sidewalks, and soft surfaces. The implementation of the pavement management system to analyze the paved assets was a challenge, as these assets represented individual pavement sections with no historic performance data. In addition, the paved assets were significantly different from a traditional road function and geometric perspective. They included drop-off lanes for buses or loading/unloading zones, parking areas that are primarily used by passenger automobiles, truck zones for garbage bins, and paved play areas that are primarily subjected to foot traffic with limited traffic loading. For this project it was agreed that Stantec s Highway Pavement Management Application (HPMA) (1) would be used to store and analyze all the data. This system which is typically used to manage large highway pavement networks was successfully tailored, without any actual

3 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 2 software modifications, to manage a discontinuous pavement network of parking lots and play areas. Once the system/network was assessed, all the collected data was loaded into a management system database where the analysis was performed to identify the current and future needs. The implementation involved the development of a set of analysis models including deduct values for a modified PCI, decision trees, performance models and cost models. Each of these models required special customization to address the specific nature of the assets included in the analysis. In addition, a number of meetings with the YCDSB project team were carried out over during implementation to document information such as construction history, performance of various pavement treatments, and unit costs of various maintenance, rehabilitation, and reconstruction (M,R&R) activities. NETWORK DESCRIPTION Implementation of the pavement management system to analyze the paved assets for YCDSB was a particular challenge, as there was no historic performance data. In addition, these paved assets were significantly different from a traditional road function and geometric perspective. As noted above, the paved facilities included bus loading/unloading zones, parking areas, truck zones for garbage bins, and paved play areas. Due to the varying pavement usage, the pavement areas were divided into individual pavement sections based on the functional classification. These discrete sections were then used for inspection, data collection and inclusion in the pavement management analysis. The typical school property generally consisted of a number of unique components or sections. Most schools were composed of five major areas or subsections, which were identified as heavy traffic (HT) areas, parking lots (PL), play areas (PA), soft surfaces (SS), and sidewalks (SW), as shown in FIGURE Parking Lot (PL) Soft Surface (SS) Heavy Traffic (HT) Play Area (PA) FIGURE 1 Functional Classification of Typical School

4 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 3 HT sections received frequent heavy vehicle traffic such as school busses, delivery vehicles, or garbage trucks. These sections were generally located to the front of the schools and often included a bus loop. PL sections are parking facilities where school staff or students (high schools) would park their cars while accessing the facility. Some of the PL sections were subject to heavy vehicle loading due to bus parking or the placement of garbage bins. However, the primary function of the section was used to identify section identity not intermittent usage. PA sections are the paved areas used by students for play during lunch or recess. These sections are typically located at the back or on the perimeter of the school. At a few sites, play areas were subjected to heavy vehicle traffic including delivery or maintenance vehicles, garbage trucks and snow removal equipment. SS sections were sodded or grassed areas used during lunch or recess as play areas and often included soccer fields, football fields, and sometimes ball diamonds. SW sections included any sidewalk used by staff or students to access the property or building that was located on school property. The total area of the paved sections (HT, PL, and PA) is approximately 750,000 m 2, which is equivalent to 100 km of a two-lane undivided road. An important part of any pavement management system is database integrity and consistency. Since numerous schools were inventoried and assessed it was important to ensure that all sections are clearly identified and given a unique name or ID number. YCDSB uses a unique 3 digit school number to identify each school. Also, each paved area was assigned a unique code depending on the functional classification (HT, PL, PA, SS, or SW). Using the school identification number and functional classification codes listed above, a unique section ID was developed and is presented below in FIGURE 2. As an example, consider Our Lady of Fatima School with school number 450, heavy traffic area HT, and functional code 100. The unique school ID would be 450HT100. If there was a second heavy traffic area, the unique ID for the second HT section would be 450HT H T School ID Functional Classification Section Number FIGURE 2 Unique Section Identifier In order to more accurately predict or estimate Maintenance and Rehabilitation (M&R) options and to carry out multi-year budget analyses, the area of each section must be determined. During the condition assessments, field measurements were taken using a survey wheel and measuring tape. However, there were a few irregularly shaped areas, for which the areas were estimated using as built AutoCAD drawings along with satellite imagery. CONDITION ASSESSMENT The objective of the condition assessment was to inventory and assess the condition of the existing infrastructure assets such as a pavements, curbs, or sidewalks. The condition assessment included the paved assets, the condition survey, and the customization of the Pavement Condition Index (PCI) to quantify the pavement condition. As a part of the condition assessments, various data attributes were collected for each school and included address, school number, time of inspection, weather conditions and the

5 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 4 presence of play structure(s) on the site. This information was documented on a Site Details form. In addition to the Site Details form, distress collection forms were completed for sidewalks and hard or soft surfaces and digital photos were taken of typical distresses and overall site conditions. It should be noted that the data collected for sidewalks and soft surfaces was for information only and was not used in the evaluation of the facility needs or for prioritization. Condition Survey Each pavement section was assessed through a visual distress survey, using a distress rating methodology generally consistent with ASTM D (2) distress rating methodology, a widely used criteria for evaluating pavement distresses. The extent and severity of each distress type was measured and logged. All field data was entered on a data collection form which was then later entered into an EXCEL spreadsheet following a QA/QC review. The extent and severity levels for each distress type for asphalt concrete (AC) and Portland Cement Concrete (PCC) pavements considered in the survey are presented in TABLE 1 and TABLE 2, respectively. TABLE 1 Asphalt Concrete Pavements Distress Types ID Distress Type Unit Levels of Severity 1 Linear Cracking m 3 2 Alligator Cracking m Edge Cracking m 3 4 Map Cracking m Pot Holes count 1 6 Raveling % area 3 7 Bleeding % 3 8 Distortion m Rutting m 3 10 Patching m 2 1 TABLE 2 Portland Cement Concrete Pavements Distress Types ID Distress Type Unit Levels of Severity 1 Patching m Scaling % area 3 3 Polishing % area 3 4 Pot Holes/Blow ups count 1 5 Sealant Loss length 1 6 Cracking length 3 7 Corner Cracking count 1 8 Faulting % slabs 3 In addition to pavement distresses, various drainage items and associated distress if any were logged such as the number of catch basins, settlement at catch basins, the relative elevation of the surface to the adjacent grade, and if any blockage was observed at a catch basin.

6 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 5 Development of the Pavement Condition Index (PCI) The Pavement Condition Index (PCI) was used to identify the overall pavement condition for each section. The original PCI model was developed by Shahin (3) and was defined on a scale of 0 to 100, where at 100 the pavement was assumed to be in perfect condition. It is a deductbased model where points are deducted for the extent and severity of the various distresses. The PCI was calculated by assuming that the pavement was in perfect condition (PCI of 100) and subtracting Deduct Values (DV) for each observed distress, depending on the type, severity and extent of the distress. The DVs were used to provide the weightings for indicating the relative importance of the distresses/severity levels in terms of the pavement performance. The magnitude of the value deducted from the PCI depends on distress type and severity. The DV for each distress type / severity level combination was adjusted from the original PCI to reflect the relative importance of distresses in terms of the safety of the school system. As an example, the deduct values for distortion was increased relative to traditional models since this type of distress represents a tripping hazard for students. In addition, any type of distress that would result in the pavement disintegration, such as high severity alligator cracking, was also assigned a higher DV. Overall Condition In total, 390 paved sections were considered in the analysis. The average PCI value for the 390 pavement sections (comprised of the 3 functional classification areas HT, PL, & PA) was found to be 69.5 with a standard deviation of Of the 390 sections, 47% were found to have a PCI value greater than 80, generally identified to be in good to excellent condition and approximately 31% were found to have a PCI value less than 60 generally described to be in poor to very poor condition. FIGURE 3 presents the distribution of PCI for all the 390 pavement sections. PCI Distribution For All 390 Paved Sections Percent (%) 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% <20 >20&<40 >40&<60 >60&<80 >80 PCI FIGURE 3 PCI Distribution for All Paved Sections

7 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 6 PAVEMENT MANAGEMENT ANALYSIS MODELS The analysis models needed for the pavement management analysis were set based on the collected data and YCDSB experience. These models included the M&R activities & cost models, M&R decision trees, and performance prediction models M&R Activities & Costs Based on past YCDSB experience, the Maintenance and Rehabilitation (M&R) activities shown in TABLE 3, and their costs, were defined in the PMS. These activities are then used as part of the analysis decision trees, and performance prediction models were assigned for each of them. TABLE 3: M&R Activities & Costs Description Unit Cost ($/m 2 ) Full Depth Asphalt patching 1.00 Crack sealing 1.13 Light maintenance activities including chip seal, slurry 3.50 seal Thin AC overlay Mill 2 + Thin AC overlay Mill 4 + Thick AC overlay Pulverization and Thin AC overlay Pulverization and Thick AC overlay Unpaved construction (soft surfaces) N/A AC thin reconstruction AC thick reconstruction Decision Trees The PMS uses decision trees to select feasible rehabilitation alternatives from the set of available M& R activities. The decision trees were developed based on past experience to mirror YCDSB practices. FIGURE 4 shows the decision tree implemented in the system.

8 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 7 N PCI<85 Y Do Nothing N PCI<70 Y Crack Sealing N PCI<60 Y Surface Treatment Thin AC Overlay N PCI<50 Y Mill & Overlay/Pulverize & Overlay N Type = HT Y Recons Thin Recons Thick Recons - Thick FIGURE 4 Decision Tree Performance Prediction Models Performance prediction models are a key component of any PMS. They are used to predict future performance of a pavement section, which is used to identify future pavement rehabilitation needs and future condition. Models were developed based on expected service lives, by functional classification, for each M&R activity. The service lives for various M&R activities were defined based on past experience from similar networks and discussions with YCDSB (4, 5). The prediction models used in the system were deterministic models and were defined using a sigmoidal equation, as shown to in Equation [1], which provides greater flexibility in modeling the differential performance of various M&R activities. I t = O e 1 ln t a bc [1] Where I t is the Index value at any year t, and O, a, b, and c are the model parameters. FIGURE 5 shows an example of the performance prediction models implemented in the PMS.

9 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 8 Pavement Performance Prediciton Models Distress Index (PCI) Bus Lanes Parking Lots Play Areas Age (Years) FIGURE 5 Index Prediction Models PAVEMENT MANAGEMENT ANALYSIS The PMS analysis included short term and long term analyses. The main objective of the short term analysis was to identify the immediate network needs and select the candidate list of facilities for M&R in the upcoming fiscal year. The objective of the long term analysis was to identify the network needs and to optimize the forecasted future budget. The pavement management analysis was performed at the section level, where a given parking lot or play area in any given school was analyzed independently. However, based on the typical practice of YCDSB, if a play area in any given school is rehabilitated, the other paved areas within the school are also rehabilitated, since they would be affected by the construction traffic. Therefore, in setting up the analysis, the short-term analysis was performed at the school level since it was a project-level analysis to select actual projects for M&R. On the other hand, the long-term analysis was performed at the section level, since the main objective was to identify the overall needs and the optimization of the system. Short Term Analysis Based on the results of the condition assessments and PCI calculations, a list of candidate pavement sections was formulated based on a Worst - First selection approach and prioritized based on the area from largest to smallest. Since the play areas typically represent the largest pavement areas within any given school and it represents the most critical section from a safety stand point, the worst PA sections were first identified. The candidate schools for rehabilitation were then identified from the school with the worst PA sections, and sorted based on the weighted average (based on area) of the condition of all the paved sections within the school.

10 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 9 Ground Truth Visits Subsequent to development of the prediction models and the short term priority list, a number of schools were visited with board staff to validate the information generated. The site visits were used to confirm that the deduct values used to generate the PCI values resulted in values which also reflected staff perceptions. In addition, the priority schools were reviewed to confirm that the needs were highest at these schools. It was confirmed that the deduct values developed and used resulted in ratings of the facilities which were similar to staff perceptions. Long Term Analysis The purpose of the network level analysis was to determine the current and future M&R needs and to develop priority programs to implement the appropriate treatments. The rehabilitation needs analysis was used to predict section condition and to determine the present and future rehabilitation needs based on this condition. The rehabilitation programming and budgeting analysis used a near-optimization technique to evaluate budget and service levels, as well as to determine multi-year rehabilitation work programs. The needs analysis was performed to determine the YCDSB pavement M&R budget needs over the 10 years analysis period, based on specified target conditions. Two types of target performance needs analyses were performed. The first type of analysis specified a minimum target condition for all sections over a 10-years period, while the second type specified a target average condition for the network over a 10-year period. The optimization analysis included the analysis of three budget scenarios to identify the network condition over the next 10 years. These scenarios are: UN40 Annual budget of 50% of the actual annual budget per year over the next 10 years UN80 Annual budget of the actual annual budget per year over the next 10 years U100 Annual budget of 125% of the actual annual budget per year over the next 10 years FIGURE 6 shows the YCDSB pavements conditions over the next 10 years in terms of the overall PCI average, as compared to the Do Nothing scenario.

11 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page 10 PERFORMANCE SUMMARY PLOT PCI, Avg, MR01, U100 PCI, Avg, MR01, UN80 PCI, Avg, MR01, UN40 PCI Avg Do Nothing Index FIGURE 6 Predicted Performance under Different Budget Scenarios SUMMARY This paper presents a unique application of typical pavement management tools to manage a unique pavement network. Stantec s Highway Pavement Management Application (HPMA), which has typically been used to manage large highway pavement networks, was successfully tailored, without any actual software modifications, to manage a discontinuous pavement network of parking lots and play areas. The implementation of the system was a particular challenge, since these pavement sections represented individual pavement sections with no historic performance data, and were significantly different from a traditional road function and geometric perspective. Once the sections were assessed, all the collected data was loaded into the management system database where the analysis was performed to identify the short term and long term network needs. The implementation involved development of a set of analysis models including performance indices, decision trees, and performance prediction models. The implementation of the YCDSB paved asset management system was successful, and showed the potential use of existing analysis tools in innovative applications within the framework of an asset management system. REFERENCES 1. Stantec Consulting, Highway Pavement Application (HPMA) User Manual, Amherst NY, ASTM Standard for Roads and Parking Lots Pavements (ASTM D )

12 Bekheet, Sturm, Abd El Halim, McLoughlin and Gastis Page Shahin, M.Y. and S.D. Kohn, "Development of a Pavement Condition Rating Procedure for Roads, Streets and Parking Lots", Construction Engineering Research Laboratory, U.S. Army Corps of Engineers, Technical Report M-268, July, Bekheet W, Helali K, VanDerHurst P, Voth M, Amenta J. Development of Roughness Deterioration Models for The National Parks Services Network. Presented at the 85th Annual Meeting of the Transportation Research Board, Washington DC, January Bekheet W, Helali K, VanDerHurst P, Voth M, Amenta J. Development of The National Park Service Parkways & Park Roads Pavement Management System. Presented at the 86th Annual Meeting of the Transportation Research Board, Washington DC, January 2007.