Science of the Total Environment 505 (2015) 1011–1017

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

A risk-based approach to sanitary sewer pipe asset management Kelly Baah, Brajesh Dubey ⁎, Richard Harvey, Edward McBean School of Engineering, University of Guelph, 50 Stone Road East, Guelph, Ontario N1G 2 W1, Canada

H I G H L I G H T S • • • •

Deteriorating sanitary sewer pipe assets are increasingly a growing issue globally. A model was developed that prioritizes maintenance and rehabilitation in a cost effective and proactive manner. A novel consequence of pipe failure impact factor is included in the consequence of failure model calculation. The methodology can be applied universally as a sanitary sewer pipe asset management tool.

a r t i c l e

i n f o

Article history: Received 26 July 2014 Received in revised form 1 October 2014 Accepted 12 October 2014 Available online 8 November 2014 Editor: Damia Barcelo Keywords: Infrastructure risk assessment Sewer pipes Decision matrix ArcGIS Modelling

a b s t r a c t Wastewater collection systems are an important component of proper management of wastewater to prevent environmental and human health implications from mismanagement of anthropogenic waste. Due to aging and inadequate asset management practices, the wastewater collection assets of many cities around the globe are in a state of rapid decline and in need of urgent attention. Risk management is a tool which can help prioritize resources to better manage and rehabilitate wastewater collection systems. In this study, a risk matrix and a weighted sum multi-criteria decision-matrix are used to assess the consequence and risk of sewer pipe failure for a mid-sized city, using ArcGIS. The methodology shows that six percent of the uninspected sewer pipe assets of the case study have a high consequence of failure while four percent of the assets have a high risk of failure and hence provide priorities for inspection. A map incorporating risk of sewer pipe failure and consequence is developed to facilitate future planning, rehabilitation and maintenance programs. The consequence of failure assessment also includes a novel failure impact factor which captures the effect of structurally defective stormwater pipes on the failure assessment. The methodology recommended in this study can serve as a basis for future planning and decision making and has the potential to be universally applied by municipal sewer pipe asset managers globally to effectively manage the sanitary sewer pipe infrastructure within their jurisdiction. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Over the years, sophisticated sewage collection systems have been developed to transport wastewater generated within cities to wastewater treatment plants. Sanitary sewer pipes constitute the primary means for transporting wastewater and are an essential component of the services provided by municipalities (Zhao et al., 2001; Hahn and Palmer, 2002; Duchesne et al., 2013). When the delivery is effective in moving wastewater from residential, commercial and industrial sources, wastewater collection pipes ensure a clean environment and limit the incidents of ground and surface water contamination arising from wastewater. However, studies including, but not limited to, Gallay et al. (2006), Borchardt et al. (2007), Hunt et al. (2010), Vroblesky et al. (2011), Verlicchi et al. (2012), Bradbury et al. (2013), Meffe and de Bustamante (2014), and Short et al. (2014) have shown ⁎ Corresponding author. Tel.: +1 519 824 4120x52506. E-mail address: [email protected] (B. Dubey).

http://dx.doi.org/10.1016/j.scitotenv.2014.10.040 0048-9697/© 2014 Elsevier B.V. All rights reserved.

that defective sanitary sewer pipes may result in adverse human health impacts through the contamination of drinking water sources arising from exfiltration of sewage. For example, Bishop et al. (1998) collated a series of published incidents of sewer related groundwater contamination in England and Wales between the 1920s and 1990s and found a total of 17 sewer-related groundwater contamination incidents resulting in 3000 cases of gastro-enteritis infection and 50 recorded typhoid infections. Hunt et al. (2010) similarly assessed sewer source contamination of drinking water wells in various communities in Wisconsin (USA) and found that 18 of 33 wells sampled, tested positive for human enteric viruses. Other analytes detected included phenathrene, tetrachloethylene, DEET, diethoxyoctylphenol pyrene, 4-cumyphenol Isophorone, 4-cumyophenol, anthracene and others. Wolf et al. (2012) investigated the occurrence of pharmaceuticals and artificial sweeteners in Rastatt, Germany and found mean concentrations between 702 ng/l and 14 ng/l for 4 out of 8 samples analyzed over a 5 year period for pharmaceuticals (amidotrizoic acid and anticonvulsant carbamazepine) and the artificial sweetener acesulfame. Further,

1012

K. Baah et al. / Science of the Total Environment 505 (2015) 1011–1017

Kuroda et al. (2012) found nano-scale concentrations of pharmaceuticals and personal care products (PPCPs) in both confined and unconfined aquifers (up to 500 m deep) from a total of 50 samples collected from 19 wards in Tokyo, Japan. These case studies demonstrate the potential for leaky sewers to contaminate drinking water sources at large depths and lateral distances. Hence, the need for adequate and proper management of sewer pipe assets cannot be overemphasized. The objective of this paper is therefore to perform a risk assessment that prioritizes future inspection of uninspected pipe assets in a way that optimizes cost and forestalls adverse impact to the natural and built environment. 2. Background Despite the significance of the services intended to be provided by sanitary sewer systems, the general approaches adopted by most municipalities in the last few decades, have been less proactive than the trend observed in recent years. According to Fenner (2000), the reactive approach to sanitary sewer pipe maintenance currently being utilized is generally more expensive than proactive techniques. Furthermore, budget cuts and insufficient resource allocation create the need for municipalities to develop cost effective methods of maintaining and rehabilitating their sanitary sewer infrastructure (Ariaratnam and Macleod, 2002). In order to prioritize sewer rehabilitation and maintenance, the completion of a risk assessment study which identifies critical areas within the distribution system is imperative (Halfawy et al., 2008). Since risk is a function of both probability and consequence (Rogers and Grigg, 2009), assessing the risk of sewer pipe failure involves assessing both the probability and consequence of failure (CoF) of the individual pipe asset within the network. A risk assessment study can serve as an asset management tool which provides a rationale for prioritizing the rehabilitation of existing bad pipes as well as future inspection of pipes whose condition states are unknown (Syachrani et al., 2011). The sanitary sewer pipe risk assessment process can be categorized as containing two components; (1) a sewer deterioration model which predicts the condition state of individual pipe assets within the network and (2) a CoF model which estimates the magnitude of impact should a pipe segment break. According to Ana and Bauwens (2010), there are three classes of sewer deterioration models: physical models, artificial intelligence-based models, and statistical models. Regression models, a type of statistical model, are known to be better suited for identifying the basic relationships between the individual variables that contribute to the condition grade of the pipe, whereas artificial neural networks (intelligence-based) are more appropriate for a ‘black box’ approach and have better prediction capabilities (Tu, 1996). Some examples of sewer deterioration models include, fuzzy Markov deterioration models, decision tree-based deterioration models, multiple logistic regression and probabilistic neural network models (Syachrani et al., 2013; Mashford et al., 2010; Tran et al., 2009, 2010; Younis and Knight, 2010; Khan et al., 2009; Chughtai and Zayed, 2008; Ariaratnam et al., 2001). The second component of the risk of sewer pipe failure assessment is the assessment of the CoF. Several authors have presented and implemented different approaches to estimate the CoF associated with each individual pipe asset in a wastewater collection system. The most common methodologies include the use of a weighted-sum multi-criteria decision matrix system or a fuzzy logic approach. Both methodologies involve assigning performance values/scores to a list of socio-economic and environmental impact consequences, based on expert knowledge and advice. Kleiner et al. (2004) modeled the risk of failure of sewer assets using fuzzy Markov deterioration process; the study involved the selection of a qualitative scale describing the CoF ranging from extremely low, to extremely high consequences. The only input variable considered in

the Kleiner et al. study was the age of the sewer pipe asset. Other factors that affect the degree of impact of sewer pipe failures were not discussed. Kleiner et al. proposed the use of a fuzzy rule-based risk approach to determine the consequence of pipe failure. Salman and Salem (2011) also assessed the risk of sewer pipe failure using a weighted-sum risk matrix system and a fuzzy inference methodology based on expert advice. A total of 17 impact factors were incorporated into the CoF assessment in Salman and Salem (2011). Factors not considered include proximity to stormwater pipes and utility lines such as gas pipes and power lines. Hahn and Palmer (2002) developed an expert-based system for prioritizing sewer inspection, using a Bayesian belief network model. The study involved interviewing both public and private sector experts to obtain expert opinion on the mechanisms of sewer pipe failure and their likely impacts. The impacts/consequences of pipe failure were categorized into socio-economic and reconstruction impacts. The socio-economic impacts identified include human health impacts, environmental impacts, and commercial and traffic impacts. Other studies that have included a risk assessment approach to sewer asset management include Halfawy et al. (2008), Hintz et al. (2007) and Martin et al. (2007). Irrespective of the approach adopted by the aforementioned authors, the risk of sewer pipe failure is best determined by assessing the CoF using a weighted scoring summation approach. The CoF may be assessed in terms of its monetary implications or may be assigned a value that allows ranking of the most critical assets within the network. For the present study, the CoF assessment is performed using a GISbased weighted scoring matrix system. Based on the data available, eleven factors contributing to high or low impact of failure were identified and were incorporated into the model calculations accordingly. A novel impact factor included in the CoF model is the proximity of deteriorated stormwater pipes to sanitary sewers. This is significant because recent studies have shown that stormwater outfalls may be contaminated by nearby, structurally defective sanitary sewer pipes even during periods of dry weather flow (Sercu et al., 2011; Sauer et al., 2011; Doshi, 2014). 3. Methodology The calculation of risk of pipe failure involves estimating the condition grade of the sewer pipe assets (probability) using a sewer deterioration model and determining the CoF using a weighted-sum scoring matrix system. The data utilized for the CoF assessment was obtained from a midsized community located in southern Ontario whereas the condition grade of all uninspected pipe assets was determined by use of a random forest data mining tool (Harvey and McBean, 2014). 3.1. Study area The study area is a midsized city which relies on 7522 sanitary sewer pipes to transport its wastewater. The existing wastewater collection infrastructure serves a population of approximately 120,000 inhabitants. The city has a separate storm water management system, not a combined sewer system. Gravity pipes make up 99% of the sanitary sewer system, whereas siphons and pressurized force-mains make up about 0.6% and 0.4%, respectively. An engineering consultancy was retained from 2008 to 2011 to visually inspect a portion of the city's sewer pipe assets using closed-circuit television (CCTV) technology. Structural and operational defects within each inspected pipe were identified using the Water Research Center Manual of Sewer Condition Classification (WRc MSCC). Severity scores assigned to each defect using the Water Research Council Sewerage Rehabilitation Manual (WRc SRM) were then used to assign an internal condition grade (ICG) to each of the 3129 sanitary sewer pipe assets that were inspected over that time period (the remaining 60% of the sanitary sewer pipes have yet to be inspected).

K. Baah et al. / Science of the Total Environment 505 (2015) 1011–1017 Table 1 Performance values and predetermined weights for the CoF impact factors. Impact factor

Impact factor sub-criteria (Sij)

Performance Weight values (PV) (Wj)

Roadway type

Intersecting ON road class 2 Intersecting ON road class 4 Intersecting ON road class 5 Yes No Diameter ≤300 mm Diameter N300 m and ≤600 m Diameter N600 m and ≤900 m Diameter N900 m Depth ≤3 m Depth N3 m and ≤10 m 3: Depth N10 m Yes No Pipe distance ≤120 m Pipe distance N120 m Pipe distance ≤200 m Pipe distance N200 m 3: Distance b5 m Distance ≥5 m and ≤10 Distance N10 m Pipe distance ≤15 m Pipe distance N15 m Pipe distance ≤20 m Pipe distance N20 m Distance ≤10 Distance N10

3 2.4 1 3 0 1 1.5

Intersecting a railway track Pipe size

Pipe burial depth

Located downtown Proximity to hospital Proximity to school Distance to building

Proximity to river Proximity to park or recreational areas Proximity to bad stormwater pipe

0.2

0.16

2.25 3 1 1.5 3 3 0 3 0 3 0 3 1.5 0 3 0 3 0 3 0

3 (Moderate)

4 (High)

5 (Very high)

1 (Low)

Low

Low

Fair

Fair

Fair

2 (Fair)

Low

Low

Moderate

Moderate

Moderate

3 (Moderate)

Moderate

Moderate

Moderate

Moderate

Moderate

4 (High)

Moderate

Moderate

Moderate

Very high

Very high

5 (Very High)

Moderate

Moderate

Moderate

Very high

Very high

The above script assigns a performance value of “1” to all pipe assets with a depth ≤3 m, “1.5” for depth N3 m and ≤10 m and “3” for pipes with depth above 10 m. The performance value for each pipe segment is then multiplied by its pre-assigned weight (in this case 0.16) to obtain a score. Table 1 provides the performance values and weights for the impact factors and sub-criteria considered in the study. After the CoF for each pipe segment is determined using Eq. (1), a Jenks natural break classification system is used to further classify the results into five impact factor rating categories namely; (1) very low, (2) low, (3) medium, (4) high and (5) very high CoF (Jenks, 1977). Jenks natural breaks classification was selected because it minimizes the variance of members in the same class and increases the variance among members of a different class (Jenks, 1977).

0.2 0.2 0.2

0.2 0.16 0.2

ð1Þ

j¼1

where Sij = performance score for pipe i in terms of impact factor j; and Wj = weight of impact factor j. The impact factors selected for the CoF calculation were based on data available within an existing capital asset inventory maintained within ESRI ArcGIS by the city. The performance values selected for each impact factor and their sub-criteria were based on a comprehensive literature review of Zhao et al. (2001), Kleiner et al. (2004) and Salman and Salem (2011). Many of the impact factors are commonly recorded when assets are first installed (e.g. pipe size) or can be readily extracted from existing capital asset map layers (e.g. intersecting a railway track). For each selected impact factor, an ArcGIS shape file was imported into the ArcMap workspace for the analysis. Distance-related calculations were estimated using the Euclidean distance and zonal statistics tools located within ESRI's Arc toolbox. Impact factors that were a function of pipe attributes such as pipe depth and size were calculated using a python “and or if then” logic syntax. An example python script used to determine the impact factor score for the burial depth of pipe i is shown below: def Reclass(DEPTH): if DEPTH b =3: return 1 elif (DEPTH N3 and DEPTH b =10): return 1.5 else: return 3 PVdepth = Reclass(!DEPTH!)

2 (Low to moderate)

0.2

The CoF for each sewer pipe (Pipei) was calculated using Eq. (1) and using the list of socio-economic and environmental impact factors as listed in Table 1. 11   X Si j  W j

Consequence of failure 1 (Low)

0.16

3.2. Consequence of Failure (CoF) assessment

Cof ¼ Consequence of failureðPipei Þ ¼

Table 2 Illustration of a risk matrix (adopted from Salman and Salem, 2011) (different colors of cell reflect different levels of failures).

Probability of failure (or condition state)

0.2

1013

3.3. Limitations of the consequence of failure model The limitation of the CoF model is the inherent subjectivity introduced by the decision maker/risk assessor, when crisp values are assigned to intangible environmental and socio-economic wastewater pipe failure consequences. For example, the cost of rehabilitating a defective sewer pipe generally increases with burial depth. If the decision maker considers pipes with depth ≥ 10 as having a high CoF because of the relatively high cost of fixing such a pipe, some information may be lost by assigning crisp cut-off values during the CoF calculations. The CoF model will reflect the preference of the decision maker and will inevitably include trade-offs based on the crisp cut-off values selected by the decision maker. However, a sensitivity analysis can be easily used to assess the loss of information described in the above example e.g. a change in the crisp cut-off value from 10 m to 9 m had no impact on the overall CoF results in this case study investigation. 3.4. Pipe condition grade prediction model Pipe conditions of the uninspected sewer pipe assets were estimated by Harvey and McBean (2014) using a novel approach to predict individual pipe condition using existing information leveraged from existing

Table 3 Sewer pipe failure impact factor ratings obtained from Jenks natural breaks classification. Jenks natural break cut-off values

Impact factor rating

Description

0.32–0.64 0.65–0.98 0.99–1.32 1.33–1.82 1.83–2.90

1 2 3 4 5

Low Low-to-moderate Moderate High Very high

1014

K. Baah et al. / Science of the Total Environment 505 (2015) 1011–1017

Fig. 1. Distribution of consequence of failure.

class-imbalanced inspection datasets. The model was developed using the random forests data mining tool on a training dataset consisting of 138 bad pipes and 1117 good pipes and was optimized such that it achieved

a low false negative rate of 18%. An overall accuracy of 72% and a true positive rate of 82% were also achieved on the basis of the optimization process (see Harvey and McBean, 2014).

Fig. 2. Sewer pipe consequence of failure map.

K. Baah et al. / Science of the Total Environment 505 (2015) 1011–1017

1015

Fig. 3. Sewer pipe consequence of failure table in ArcGIS.

3.5. Determining the risk of failure Once the CoF and internal condition grade of the uninspected pipe assets have both been determined, the risk of failure for each pipe segment is calculated by combining the consequence of failure and its corresponding predicted internal condition grade using a risk matrix system (Salman and Salem, 2011). Since the sewer pipe asset management team for the study area, considers pipes with condition grades 4 and 5 as near collapse and in need of urgent attention, the risk matrix combined pipes in this category (ICG 4 and 5) with pipes that have high and very high CoF. Table 2 shows an example of how pipes with high risk of failure were determined using the risk matrix system.

4. Results & discussion 4.1. Consequence of failure assessment By use of Eq. (1) and the information provided in Table 1, the CoF for each uninspected sewer pipe (4656 pipes) was determined. The results obtained from the aforementioned procedure, ranged between 0.32 (lowest CoF value) and 2.90 (highest CoF value). As described in the Methodology, Jenks natural breaks classification was further used to classify the CoF values into five impact factor categories. Table 3 shows details of the five impact factor classes obtained based on the natural breaks classification. An overview of the results shows that the majority (94%) of the uninspected sanitary pipes have very low to moderate CoF, whereas 6% of the sanitary pipe assets have high to very high CoF. The frequency distribution of the CoF is shown in Fig. 1. The CoF assessment included proximity to bad stormwater pipes which is a significant impact factor that hasn't been considered in

previous research papers. The effect of bad stormwater pipes that have close proximity to sanitary pipes is important, because of the potential for stormwater outfalls to be contaminated by exfiltration from the nearby sewer pipes (Sauer et al., 2011; Doshi, submitted for publication). Impact factors such as depth to bedrock and proximity to buried utility lines (e.g. gas pipes and power transmission lines) could not be considered in this study due to lack of available data. It is noteworthy that the procedure for determining the CoF is imprecise and can have varying results depending on the jurisdiction and how the decision maker/risk assessor perceives the magnitude of impact associated with the shortlisted impact factors. The weakness of this methodology is therefore the fuzziness introduced by assigning crisp numeric values to intangible environmental and socio-economic consequences that are difficult to measure. A consequence of sewer pipe failure map (Fig. 2) was generated from the resulting CoF calculations. The map shows a snapshot of pipes that have high and very high CoF and can be used by the municipality as an essential decision making tool for the sewer pipe assets. A snapshot of the results of the CoF calculations in ArcGIS is shown in Fig. 3 below. From Fig. 3, sewer pipe rehabilitation and maintenance can be prioritized on the basis of the CoF score derived for each pipe asset. Pipes with a higher score should be given a higher priority since they are more likely to have a higher environmental and socio-economic impact when compared with pipes that have a lower score. 4.2. Sewer pipe risk of failure assessment After estimating the consequence of pipe failure, pipes that have a high risk of failure (i.e. high CoF and a high probability of having an ICG 4 or 5) are identified using the risk matrix provided in Table 2.

1016

K. Baah et al. / Science of the Total Environment 505 (2015) 1011–1017

Downtown/ central business district

Fig. 4. Sewer pipe risk of failure map.

The results of the risk matrix show that 3.5% (164 out of the 4656) of the uninspected sewer pipe assets have a high risk of failure. The sewer pipe risk of failure map (Fig. 4) generated from the resulting risk matrix shows that the majority of the pipes that have a high risk of failure tend to be located in the older parts of the city. 5. Conclusion In this study, the risk of sanitary sewer pipe failure has been applied to a mid-sized city in Ontario using a risk matrix system and a weighted scoring summation methodology in ArcGIS. Out of 4656 uninspected pipe assets, 3.5% have a high failure risk whereas 6% showed high to very high CoF. The methodology adapted in this paper is general and can serve as a basis for future planning and decision making. The methodology utilized in this study is universal and can be applied by municipal sewer pipe asset managers globally to effectively manage the sanitary sewer pipe infrastructure within their jurisdiction. Future studies should include impact factors such as proximity to buried utility lines in order to improve the results obtained for the CoF assessment. Acknowledgments The authors would like to thank Teresa Lewitzky and Adam Bonnycastle from the Data Resource center at the University of Guelph, for their assistance in applying ArcGIS.

References Ana EV, Bauwens W. Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods. Urban Water J 2010;7(1):47–59. Ariaratnam ST, Macleod CW. Financial outlay modeling for a local sewer rehabilitation strategy. J Constr Eng Manag 2002:486–95. Ariaratnam S, El-Assaly A, Yang Y. Assessment of infrastructure inspection needs using logistic models. J Infrastruct 2001;7(4):160. Bishop PK, Misstear BD, White M, Harding NJ. Impacts of sewers on groundwater quality. J Chart Inst Water Environ Manag 1998(12):216–23. Borchardt MA, Bradbury KR, Gotkowitz MB, Cherry JA, Parker BL. Human enteric viruses in groundwater from a confined bedrock aquifer. Environ Sci Technol 2007;41(18): 6606–12. Bradbury KR, Borchardt MA, Gotkowitz M, Spencer SK, Zhu J, Hunt RJ. Source and transport of human enteric viruses in deep municipal water supply wells. Environ Sci Technol 2013;47(9):4096–103. Chughtai F, Zayed T. Infrastructure condition prediction models for sustainable sewer pipelines. J Perform Constr 2008:333–41. Doshi J. An investigation of leaky sewers as a source of fecal contamination in the storm water drainage systems in Singapore [Masters Thesis] Cambridge, MA, USA: Massachusetts Institute of Technology; 2014. Duchesne S, Beardsell G, Villeneuve J-P, Toumbou B, Bouchard K. A survival analysis model for sewer pipe structural deterioration. Comput Civ Infrastruct Eng 2013; 28(2):146–60. Fenner R. Approaches to sewer maintenance: a review. Urban Water 2000;2(4):343–56. Gallay A, De Valk H, Cournot M, Ladeuil B, Hemery C, Castor C. A large multi-pathogen waterborne community outbreak linked to faecal contamination of a groundwater system, France. Clin Microbiol Infect 2006;12(6):561–70. Hahn M, Palmer R. Expert system for prioritizing the inspection of sewers: knowledge base formulation and evaluation. J Water 2002:121–9. Halfawy M, Dridi L, Baker S. Integrated decision support system for optimal renewal planning of sewer networks. J Comput Civ 2008;22(6):360–72.

K. Baah et al. / Science of the Total Environment 505 (2015) 1011–1017 Harvey R, McBean E. Predicting the structural condition of individual sanitary sewer pipes with random forests. Can J Civ Eng 2014:294–303. Hintz AM, Barnes D, Millar DC. Establishing a collection system baseline condition assessment program one step at a time. Proc., Pipelines: advances and experiences with trenchless pipeline projects, ASCE, Reston, VA; 2007. Hunt RJ, Borchardt MA, Richards KD, Spencer SK. Assessment of sewer source contamination of drinking water wells using tracers and human enteric viruses. Environ Sci Technol 2010;44(20):7956–63. Jenks GF. Optimal data classification for choropleth maps. Occasional paper no. 2. Lawrence, Kansas: Univ. of Kansas, Dept. of Geography; 1977. Khan Z, Zayed T, Moselhi O. Structural condition assessment of sewer pipelines. J Perform Constr 2009:170–9. Kleiner Y, Sadiq R, Rajani B. Modeling failure risk in buried pipes using fuzzy Markov deterioration process. ASCE Pipeline Eng Constr 2004:1–12. Kuroda K, Murakami M, Oguma K, Muramatsu Y, Takada H, Takizawa S. Assessment of groundwater pollution in Tokyo using PPCPs as sewage markers. Environ Sci Technol 2012;46(3):1455–64. Martin T, Johnson D, Anschell S. Using historical repair data to create customized predictive failure curves for sewer pipe risk modeling. Proc Lead Edge Conf Strateg 2007: 1–11. Mashford J, Marlow D, Tran D, May R. Prediction of sewer condition grade using support vector machines. ASCE J Comput 2010:283–90. Meffe R, de Bustamante I. Emerging organic contaminants in surface water and groundwater: a first overview of the situation in Italy. Sci Total Environ 2014;481C:280–95. Rogers P, Grigg N. Failure assessment modeling to prioritize water pipe renewal: two case studies. ASCE J Infrastruct Syst 2009;15(3):162–71. Salman B, Salem O. Risk assessment of wastewater collection lines using failure models and criticality ratings. ASCE J Pipeline Syst Eng 2011:68–76. Sauer EP, Vandewalle JL, Bootsma MJ, McLellan SL. Detection of the human specific Bacteroides genetic marker provides evidence of widespread sewage contamination of stormwater in the urban environment. Water Res 2011;45(14):4081–91.

1017

Sercu B, Van De Werfhorst LC, Murray JLS, Holden P. Sewage exfiltration as a source of storm drain contamination during dry weather in urban watersheds. Environ Sci Technol 2011;45(17):7151–7. Short MD, Daikeler A, Peters GM, Mann K, Ashbolt NJ, Stuetz RM. Municipal gravity sewers: an unrecognised source of nitrous oxide. Sci Total Environ 2014;468-469:211–8. Syachrani S, Jeong H, Chung C. Dynamic deterioration models for sewer pipe network. ASCE J Pipeline Syst 2011:123–31. Syachrani S, Jeong D, Chung C. Decision tree based deterioration model for buried wastewater pipelines. ASCE J Perform 2013:633–45. Tran HD, Perera BJC, Ng AWM. Predicting structural deterioration condition of individual storm-water pipes using probabilistic neural networks and multiple logistic regression models. J Water Resour Plan Manag 2009;135(6):553–7. Tran H, Perera B, Markov Ng A. Neural network models for prediction of structural deterioration of storm-water pipe assets. ASCE J Infrastruct Syst 2010:167–71. Tu JV. Advantages and disadvantages of using artificial neural networks versus logisitic regression for predicting medical outcomes. J Clin Epidemiol 1996;49(11):1225–31. Verlicchi P, Al Aukidy M, Galletti A, Petrovic M, Barceló D. Hospital effluent: investigation of the concentrations and distribution of pharmaceuticals and environmental risk assessment. Sci Total Environ 2012;430:109–18. Vroblesky DA, Petkewich MD, Lowery MA, Landmeyer JE. Sewers as a source and sink of chlorinated-solvent groundwater contamination, Marine Corps Recruit Depot, Parris Island, South Carolina; 2011. p. 63–9 [4]. Wolf L, Zwiener C, Zemann M. Tracking artificial sweeteners and pharmaceuticals introduced into urban groundwater by leaking sewer networks. Sci Total Environ 2012; 430:8–19. Younis R, Knight MA. A probability model for investigating the trend of structural deterioration of wastewater pipelines. Tunn Undergr Space Technol Inc Trenchless Technol Res 2010;25(6):670–80. Zhao J, McDonald S, Kleiner Y. Guidelines for condition assessment and rehabilitation of large sewers; 2001. p. 78.

A risk-based approach to sanitary sewer pipe asset management.

Wastewater collection systems are an important component of proper management of wastewater to prevent environmental and human health implications fro...
1MB Sizes 4 Downloads 7 Views