Rehabilitation Scenarios for Sustainable Water Mains - Concordia ...

Rehabilitation Scenarios for Sustainable Water Mains Khaled Shahata1; Tarek Zayed2; and Saad Al-Jibouri3 1

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Graduate Student, Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada, PH (514) 848-2424; FAX (514) 848-7965; email: [email protected]. Assistant Professor, Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada, PH (514) 848-2424 ext. 8779; FAX (514) 848-7965; email: [email protected]. Associate Professor, Construction Management & Engineering Group, Department of Civil Engineering, University of Twente, The Netherlands, PH.+31-(0)53-4894887, Fax:+31-(0)534892511, Email: [email protected].

Abstract The ability to regularly deliver safe drinking water is a constant challenge to municipalities. According to the Canadian National Research Council reports, the renewal and rehabilitation of infrastructure across Canada is estimated to be at least $15 billion. Therefore, selecting the best repair and/or rehabilitation scenarios is essential to optimize the quality of the existing water mains and to minimize rehabilitation cost losses. Current research identifies several rehabilitation methods for water mains, which are classified into three main categories: (1) repair (e.g. Open trench, sleeves); (2) renovation (e.g. slip lining, cement lining, epoxy lining, CIPP); and (3) replacement (e.g. pipe bursting, micro-tunneling, directional drilling, auger boring, open cut). Stochastic life cycle cost (SLCC), using Monte Carlo simulation approach, is utilized to compare the developed scenarios so that the optimal scenario can be accommodated for different types of water main pipes (e.g. Cast Iron, Ductile Iron, Concrete, and PVC). Data, related to the cash flow of each scenario, are collected from contractors and municipalities in Canada. Current research framework will assist municipality engineers to select the optimum rehabilitation scenario for each type of water main. In addition, it will assist them to properly manage their assets, which guarantee better quality of life for the society. Keywords: Stochastic - Life Cycle Cost - Water Mains – Rehabilitation – Monte Carlo – Simulation. Introduction Water supply and sewer systems, in Canada, have reached a point where maintenance and renewal is essential. According to a survey conducted by the Canadian National Research Council (NRC), the present estimated cost across Canada for replacement and rehabilitation of water mains is at least $15 billion [NRC, 2004]. Some municipalities use deterministic life cycle cost (DLCC) approach which doesn’t usually cover the uncertainty in service life, interest rate, or new construction/ rehabilitation cost and might lead to inaccurate decision. The American Association of Water Work (AWWA) manuals have published many documents on repair, renovation, and replacement techniques for rehabilitation of water mains [AWWA, 2001]. Most life cycle cost (LCC) models for civil infrastructure assume deterministic behavior of its service life [Hass et al., 1994, and Hudson et al., 1997]. Farngopol et al. (2001) reported that additional research is required to develop better LCC models and tools to quantify the risks, cost, and benefits associated with some civil infrastructures.

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Salem et al. (2003) has presented a risk-based LCC of rehabilitation and construction alternatives for pavements. In addition, the NRC has developed a LCC model using fuzzy approach for cathodic protection of water mains [Rajani et al., 2004]. Kleiner et al. (2001) used dynamic programming approach to calculate LCC for relining and replacement, using open cut, alternatives of water mains. The selection of cost effective rehabilitation and/or new installation method is crucial to determine the repairing, renovating or replacing time for water mains. Moreover, it is required to develop a useful and easy tool to help decision maker in reaching the optimum rehabilitation or reconstruction decision. Research Objective The objective of this research is to develop a stochastic life cycle cost (SLCC) model that selects the appropriate new installation/ rehabilitation scenario of water mains. The SLCC model will be developed using Monte Carlo simulation approach. SLCC Model The SLCC model achieves the following steps as shown in Figure 1: 1) determine the breakage rate analysis; 2) identify the LCC procedure; 3) run Monte Carlo simulation; 4) perform sensitivity analysis; 5) select the best rehabilitation and replacement scenarios, and 6) generate a summary report. The following sub-sections describe the main elements of SLCC model.

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Figure 1 Life Cycle Cost Model (Shahata, 2006) SLCC Procedure The major components of SLCC procedure are summarized in Figure 2. It can be outlined as follows: (1) Define the main problem items (i.e. scope, evaluation criteria (e.g. acceptable risk level), rehabilitation alternatives, cost elements); (2) Decide the LCC approach (i.e. probabilistic vs. deterministic); (3) Assign economic parameters (e.g. discounted rate, analysis period); (4) Develop cash flow profile for each alternative (i.e. rehabilitation activities and their time interval based on deterioration and breakage rate analysis, estimate the rehabilitation technique cost); (5) Compute

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Equivalent Annual Uniform Cost (EAUC) for each alternative; (6) Perform strategies (i.e. sensitivity analysis, uncertainty analysis); (7) Analyze results and generate final report.

Figure 2 Life Cycle Cost Procedure (Shahata, 2006) Run Monte Carlo Simulation After developing the SLCC model, simulation approach is employed as follows: • Identify parameters that carry uncertainty and determine their values. • Construct probability distributions for the identified parameters using random sampling of variables from the defined fitted distributions. • Design a simulation model using “@ Risk” software package in order to generate the probability distribution of the EAUC for different alternatives. Figure 3 shows the calculation of EAUC using Monte Carlo simulation; this enables decision makers to make informed decisions regarding the rehabilitation alternatives that can be applied and the

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level of risk that they plan to accept. Where: (EAUC) equivalent annual uniform cost; (n) service life, (PVcost t) present value of all cost elements; (i) discounted rate. All these elements are expressed in a probability distribution functions.

Figure 3 Calculating EAUC using Monte Carlo Simulation (Shahata, 2006) Perform sensitivity analysis A sensitivity analysis is carried out to provide a better understanding of the developed model. It is introduced to examine the effect of changing input parameters on the overall results. Input parameters will be changed individually while the other parameters are upheld constant. The sensitivity of the model to changes in the following parameters is tested: (1) discounted rate; (2) deterioration rate; and (3) unit cost of all major components. Suggested Scenarios The Alternatives used for rehabilitation project(s) are shown in Table 1, based on these alternatives some scenarios were suggested. However, scenarios are developed in 6 main categories as shown in Table 2. The total numbers of the suggested scenarios are sixty scenarios. Example of cash flow for “repair only”, “renovation only”, and “replacement only” scenarios are shown in Figure 4. The scenarios are built based on mains service life and breaks intervals. Such that when a break occurs, a rehabilitation method from the available alternatives is used in which this procedure can be applied up to a maximum of five breaks. Table 1 Alternatives for rehabilitation project(s) Operation Description Symbol 1 Repair Sleeves SVS Open trench OT 2 Renovation Cement or epoxy lining C/EL Slip lining SL Curried in Place Pipe CIPP 3 Replacement Pipe Bursting PB Open Cut OC Horizontal Directional Drilling HDD Microtunneling MT

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Table 2 Suggested scenarios for rehabilitation project(s) Category Operation No. of Scenarios 1 Repair only 2 2 Renovation only 3 3 Replacement only 4 4 Repair & renovation 24 5 Repair & replacement 18 6 Renovation & replacement 9 Total 60

Figure 4 Example of suggested Scenarios (Shahata, 2006) The required data consist of operation & maintenance cost, replacement cost, deterioration rate (service life of pipe), and discounted rate. Costs are entered into the SLCC model as a triangular probability distribution with minimum, most likely, and maximum costs. To use this model, assumptions have to be made as follows: • Parameters for Trenching and Excavation Cost: The soil condition used is sandy gravel soil with 1:1 side slope. Pipe depth is between 2.0-3.0m. • Parameters for pipe materials: cast iron, ductile iron, PVC, asbestos cement, and concrete • Parameters for Valve, Fitting, and Hydrant Cost: Frequency of installation is medium.

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Time of breaks and type of repair data are collected from Canadian municipalities to predict the service life of mains. Data are analyzed and a probability distribution functions are used with mean (µ), and standard deviation ( ) to model the timing of each break. Discounted rate data are collected from bank of Canada and entered to the SLCC model as a normal probability distribution function. Application example This section compares the rehabilitation costs for a cast iron pipe with diameter range from 150 to 600mm (6”-24”). The “Replacement only” scenarios are utilized to clarify the developed model and framework in current research. They are summarized in the following analysis where the EUAC values are in ($/km/year). A histogram plot of the EAUC “replacement only” scenarios is shown in Figure 5. It shows the ranges of minimum, mean, and maximum cost of each scenario. Based on the relatively large size bars, variation can be easily observed. The minimum EAUC is for pipe bursting alternative with 43000 $/km/yr. Based on the chi-squared fit statistic, each of the following probability functions can best fit the output data for “replacement only” scenario: Log Logistic; Lognormal; Inv Gauss; and Pearson. Figure 6 shows the cumulative probability distribution of the EAUC for “pipe bursting”, “open cut”, “HDD”, and “MT”. It shows that “pipe bursting” scenario always yields a lower EAUC than all other scenarios. However, Figure 7 shows the sensitivity graph for pipe bursting scenario, which describes how changing inputs affect the outputs. For example, the input service life has a negative effect on the output alternative. A sensitivity coefficient value of 1 indicates a complete positive correlation between two variables. Though, a value of -1 indicates a complete inverse correlation between two variables and the value of 0 indicates that there is no correlation between variables. The sensitivity for 150600mm diameter range of cast iron mains shows that deterioration rate for service life has the highest effect on the EUAC with a negative sensitivity coefficient of (-0.321). Which means that as service life increases EAUC decreases. The replacement technique costs and the discounted rate have significant positive sensitivity coefficient effect on the EUAC with a range of 0.230 to 0.159. These sensitivity coefficients indicated that service life is the most uncertain parameter that needs to be studied in order to reduce life cycle cost of water mains. Other sensitivity coefficient values indicate a partial correlation; the output is affected by changes in the selected input, but may be affected by other variables as well. Output results of the SLCC model, with 95% confidence level, showed that “Open Trench” is the best scenario for all diameter ranges (“repair only” scenarios). They also show that “Slip-Lining” is the best scenario for all diameter ranges (“renovation only” scenarios) and “Pipe bursting” is the best scenario for small diameter 150 to 600mm (6” to 24”) (“replacement only” scenarios). In addition, “Open Cut” is the best for large diameter 750 mm (> 30”) in the “replacement only” scenarios. Conclusions A stochastic model has been developed to perform SLCC analysis for several water main rehabilitation alternatives. Data are collected from municipalities across Canada. The model has been tested for several scenarios and proved to be robust. A combination of repair, renovation, and replacement techniques are integrated in the model to develop different scenarios for rehabilitation of water mains. Several conclusions were found based on 60 selected scenarios: 1. “Open Trench” is the best for all diameters ranges (“repair only” scenarios). 2. “Slip-Lining” is the best for all diameters ranges (“renovation only” scenarios).

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95% percentile

Open CUT (OC)

HDD

$102 $53

$39

$68

$72 $48

$29

$67 pipe bursting (PB)

$170

Mean

$104

5% percentile

$43

$180 $160 $140 $120 $100 $80 $60 $40 $20 $0

$25

EAUC ( 1000 $/km/year)

3. “Pipe bursting” is the best for small diameters 150 to 600mm (6”to24”) (“replacement only” scenario). 4. “Open Cut” is the best for large diameters 750 mm (> 30”) in the “replacement only” scenarios.

MT

scenario Name

Figure 5 The EAUC for the “replacement only” scenarios (Shahata, 2006)

Figure 6 Cumulative probability distribution for “replacement only” scenarios (Shahata, 2006)

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Figure 7 Sensitivity graphs for pipe bursting scenario (Shahata, 2006) References American Water Works Association (AWWA) (2001). “Manual of Water Supply Practices (M 28) Rehabilitation of Water Mains,” 2nd Edition, Denver, CO. Frangopol, D. M., Kong, J. S., and Gharaibeh, E. S. (2001). “Reliability based life-cycle management of highway bridges.” Journal of Computing in Civil Eng., 15(1): 27-34 Hass, R., Hudson, W., and Zaniewski, A. (1994). Modern pavement management. Krieger Publishing, Malabar, Florida. Hudson, W., Hass, R., and Uddin, W. (1997). Infrastructure management. McGraw-Hill, New York. Kleiner, Y., Adams, B. J., and Rogers, J. S., 2001 "Water Distribution Network Renewal Planning" Journal of Computing in Civil Engineering, January, 15(1): 15-26. National Research Council (NRC) (2004). “Assessing Canada’s Infrastructure Needs: A Review of Key Studies” Infrastructure Canada research report, September. Rajani, B.; Kleiner, Y. (2004). “Alternative strategies for pipeline maintenance/ renewal "AWWA Annual Conference, Orlando, Florida, June13-17, pp. 1-16 Salem, O., AbouRizk, S., and Ariaratnam, S. (2003). “Risk-based life-cycle costing of infrastructure rehabilitation and construction alternatives.” J. Infrastructure Systems, 9(1): 6-15. Shahata, k. (2006). "Stochastic Life Cycle Cost Modeling Approach for Water Mains," Master Thesis submitted to Concordia University, Montreal, Canada, April.

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