Abstract
This paper examines the role of pipe deterioration prediction approaches for optimizing maintenance, repair, and rehabilitation of buried water supply, wastewater collection, and drainage networks. It is appreciated that there are other ancillary assets within water supply and wastewater collection and drainage networks, but these were not considered in this paper. Currently there are a range of asset condition assessment frameworks, mainly based on asset defect location, identification, and characterization. These are infrequently applied in practice, mainly due to the restricted availability of asset defect inspection data. This paper reviews current deterioration modeling approaches and highlights the crucial need for broader, richer data sets (including both asset and surrounding environment data) to inform the development and application of such approaches. This paper describes what could be considered as an expanded ideal data set for deterioration modeling at a network and individual asset scale and indicates emerging new inspection technologies that should be capable of meeting the enhanced data needs.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is supported by UK Research and Innovation (UKRI) Engineering and Physical Sciences Research Council (EPSRC) Programme grant EP/S016813/1 (Pipebots: www.pipebots.ac.uk).
References
Achim, D., F. Ghotb, and K. McManus. 2007. “Prediction of water pipe asset life using neural networks.” J. Infrastruct. Syst. 13 (1): 26–30. https://doi.org/10.1061/(ASCE)1076-0342(2007)13:1(26).
Ahmadi, M., F. Cherqui, J.-C. De Massiac, and P. Le Gauffre. 2014. “From sewer inspection programmes to rehabilitation needs: Research and results related to data quality and availability with the support of numerical experiment.” Eur. J. Environ. Civ. Eng. 18 (10): 1145–1156. https://doi.org/10.1080/19648189.2014.893212.
Al-Barqawi, H., and T. Zayed. 2006. “Condition rating model for underground infrastructure sustainable water mains.” J. Perform. Constr. Facil. 20 (2): 126–135. https://doi.org/10.1061/(ASCE)0887-3828(2006)20:2(126).
Al-Barqawi, H., and T. Zayed. 2008. “Infrastructure management: Integrated AHP/ANN model to evaluate municipal water mains’ performance.” J. Infrastruct. Syst. 14 (4): 305–318. https://doi.org/10.1061/(ASCE)1076-0342(2008)14:4(305).
Alvisi, S., and M. Franchini. 2014. “A heuristic procedure for the automatic creation of district metered areas in water distribution systems.” Urban Water J. 11 (2): 137–159. https://doi.org/10.1080/1573062X.2013.768681.
Amaitik, N. M., and S. M. Amaitik. 2008. “Development of PCCP wire breaks prediction model using artificial neural networks.” In Pipelines 2008: Pipeline asset management: Maximizing performance of our pipeline infrastructure, 1–11. Reston, VA: ASCE. https://doi.org/10.1061/40994(321)128.
Ana, E., and W. Bauwens. 2010. “Modeling the structural deterioration of urban drainage pipes: The state-of-the-art in statistical methods.” Urban Water J. 7 (1): 47–59. https://doi.org/10.1080/15730620903447597.
Ana, E., W. Bauwens, M. Pessemier, C. Thoeye, S. Smolders, I. Boonen, and G. De Gueldre. 2009. “An investigation of the factors influencing sewer structural deterioration.” Urban Water J. 6 (4): 303–312. https://doi.org/10.1080/15730620902810902.
Aydogdu, M., and M. Firat. 2015. “Estimation of failure rate in water distribution network using fuzzy clustering and LS-SVM methods.” Water Resour. Manage. 29 (5): 1575–1590. https://doi.org/10.1007/s11269-014-0895-5.
Babovic, V., J.-P. Drecourt, M. Keijzer, and P. F. Hansen. 2002. “A data mining approach to modelling of water supply assets.” Urban Water 4 (4): 401–414. https://doi.org/10.1016/S1462-0758(02)00034-1.
Bakry, I., H. Alzraiee, K. Kaddoura, M. El Masry, and T. Zayed. 2016. “Condition prediction for chemical grouting rehabilitation of sewer networks.” J. Perform. Constr. Facil. 30 (6): 04016042. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000893.
Balekelayi, N., and S. Tesfamariam. 2019. “Statistical inference of sewer pipe deterioration using Bayesian geoadditive regression model.” J. Infrastruct. Syst. 25 (3): 04019021. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000500.
Berardi, L., O. Giustolisi, Z. Kapelan, and D. Savic. 2008. “Development of pipe deterioration models for water distribution systems using EPR.” J. Hydroinf. 10 (2): 113–126. https://doi.org/10.2166/hydro.2008.012.
Boxall, J., A. Saul, and P. Skipworth. 2004. “Modeling for hydraulic capacity.” J. Am. Water Works Assoc. 96 (4): 161–169. https://doi.org/10.1002/j.1551-8833.2004.tb10607.x.
Brevis, W., L. Susmel, and J. Boxall. 2015. “Investigating in-service failures of water pipes from a multiaxial notch fatigue point of view: A conceptual study.” Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci. 229 (7): 1240–1259. https://doi.org/10.1177/0954406214553020.
Burn, S., P. Davis, and S. Gould. 2009. “Risk analysis for pipeline assets—The use of models for failure prediction in plastics pipelines.” In Service life prediction of polymeric materials, 183–204. New York: Springer.
Caffoor, I. 2019. Robotics and Autonomous Systems (RAS) for buried pipe infrastructure and water operations. Sheffield, UK: Univ. of Sheffield.
Caradot, N., M. Riechel, M. Fesneau, N. Hernandez, A. Torres, H. Sonnenberg, E. Eckert, N. Lengemann, J. Waschnewski, and P. Rouault. 2018. “Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany.” J. Hydroinf. 20 (5): 1131–1147. https://doi.org/10.2166/hydro.2018.217.
Caradot, N., H. Sonnenberg, I. Kropp, A. Ringe, S. Denhez, A. Hartmann, and P. Rouault. 2017. “The relevance of sewer deterioration modelling to support asset management strategies.” Urban Water J. 14 (10): 1007–1015. https://doi.org/10.1080/1573062X.2017.1325497.
Carvalho, G., C. Amado, R. S. Brito, S. T. Coelho, and J. P. Leitão. 2018. “Analysing the importance of variables for sewer failure prediction.” Urban Water J. 15 (4): 338–345. https://doi.org/10.1080/1573062X.2018.1459748.
CEN (European Committee for Standardization). 2008. Drain and sewer systems outside of buildings. EN 752. Brussels, Belgium: CEN.
CEN (European Committee for Standardization). 2011. Investigation and assessment of drain and sewer systems outside buildings—Part 2: Visual inspection coding system. EN 13508-2. Brussels, Belgium: CEN.
Christodoulou, S., P. Aslani, and A. Vanrenterghem. 2003. “A risk analysis framework for evaluating structural degradation of water mains in urban settings, using neurofuzzy systems and statistical modeling techniques.” In Proc., World Water & Environmental Resources Congress 2003, 1–9. Reston, VA: ASCE. https://doi.org/10.1061/40685(2003)134.
Chughtai, F., and T. Zayed. 2008. “Infrastructure condition prediction models for sustainable sewer pipelines.” J. Perform. Constr. Facil. 22 (5): 333–341. https://doi.org/10.1061/(ASCE)0887-3828(2008)22:5(333).
Chughtai, F., and T. Zayed. 2011. “Integrating WRc and CERIU condition assessment models and classification protocols for sewer pipelines.” J. Infrastruct. Syst. 17 (3): 129–136. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000052.
Clair, A. M., and S. Sinha. 2012. “State-of-the-technology review on water pipe condition, deterioration and failure rate prediction models!” Urban Water J. 9 (2): 85–112. https://doi.org/10.1080/1573062X.2011.644566.
Clarke, B., et al. 2017. “A decision support system to proactively manage subsurface utilities.” In Proc., Int. Symp. for Next Generation Infrastructure (ISNGI), 99–108. London: International Symposium for Next Generation Infrastructure.
Creighton, J. H. 2012. A first course in probability models and statistical inference. New York: Springer.
Cremer, F., H.-G. van Deel, J. Lehne, and K. Scholz. 2002. “Arbeitschilfen Abwasser Planung, Bau und Betrieb von abwassertechnischen Anlagen in Liegenschaften des Bundes.” KA Wasserwirtschaft, Abwasser 49 (8): 1070–1077.
Davies, J., B. Clarke, J. Whiter, and R. Cunningham. 2001. “Factors influencing the structural deterioration and collapse of rigid sewer pipes.” Urban Water 3 (1–2): 73–89. https://doi.org/10.1016/S1462-0758(01)00017-6.
Davis, P., S. Burn, M. Cardy, S. Gould, P. Sadler, and G. Tjandraatmadja. 2007a. Long term performance prediction for PE pipes. Denver: American Water Works Research Foundation.
Davis, P., S. Burn, M. Moglia, and S. Gould. 2007b. “A physical probabilistic model to predict failure rates in buried PVC pipelines.” Reliability Eng. Syst. Saf. 92 (9): 1258–1266. https://doi.org/10.1016/j.ress.2006.08.001.
Davis, P., and D. Marlow. 2008. “Quantifying economic lifetime for asset management of large diameter pipelines.” Am. Water Works Assoc. 100 (7): 110–119. https://doi.org/10.1002/j.1551-8833.2008.tb09680.x.
Davis, P., D. D. Silva, D. Marlow, M. Moglia, S. Gould, and S. Burn. 2008. “Failure prediction and optimal scheduling of replacements in asbestos cement water pipes.” J. Water Supply: Res. Technol.—AQUA 57 (4): 239–252. https://doi.org/10.2166/aqua.2008.035.
Deb, A. K. 2002. Prioritizing water main replacement and rehabilitation. Denver: American Water Works Association.
Dehghan, A., K. J. McManus, and E. F. Gad. 2008a. “Statistical analysis of structural failures of water pipes.” Proc. Inst. Civ. Eng. Water Manage. 161 (4): 207–214. https://doi.org/10.1680/wama.2008.161.4.207.
Dehghan, A., K. J. McManus, and E. F. Gad. 2008b. “Probabilistic failure prediction for deteriorating pipelines: Nonparametric approach.” J. Perform. Constr. Facil. 22 (1): 45–53. https://doi.org/10.1061/(ASCE)0887-3828(2008)22:1(45).
De Silva, D., M. Moglia, P. Davis, and S. Burn. 2006. “Condition assessment and probabilistic analysis to estimate failure rates in buried pipelines.” J. Water Supply Res. Technol. AQUA 55 (3): 179–191. https://doi.org/10.2166/aqua.2006.0004.
Dirksen, J., F. Clemens, H. Korving, F. Cherqui, P. Le Gauffre, T. Ertl, H. Plihal, K. Müller, and C. Snaterse. 2013. “The consistency of visual sewer inspection data.” Struct. Infrastruct. Eng. 9 (3): 214–228. https://doi.org/10.1080/15732479.2010.541265.
Duchesne, S., G. Beardsell, J. P. Villeneuve, B. Toumbou, and K. Bouchard. 2013. “A survival analysis model for sewer pipe structural deterioration.” Comput.-Aided Civ. Infrastruct. Eng. 28 (2): 146–160. https://doi.org/10.1111/j.1467-8667.2012.00773.x.
DWA (Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall e.V.). 1999. Visual inspection—Inspection, renovation, replacement and rehabilitation of sewers. ATV-M 143-2. Hennef, Germany: DWA.
DWA (Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall e.V.). 2012. Leitfaden zur strategischen Sanierungsplanung von Entwässerungssystemen außerhalb von Gebäuden [Guideline on strategical rehabilitation planning of urban drainage systems outside of buildings]. Hennef, Germany: DWA.
DWA (Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall e.V.). 2016. Zustandserfassung und -beurteilung von Entwässerungssystemen außerhalb von Gebäuden—Teil 7: Beurteilung der Umweltrelevanz des baulichen/betrieblichen Zustands [Condition assessment and classification of urban drainage systems outside of buildings]. Hennef, Germany: DWA.
Egger, C., A. Scheidegger, P. Reichert, and M. Maurer. 2013. “Sewer deterioration modeling with condition data lacking historical records.” Water Res. 47 (17): 6762–6779. https://doi.org/10.1016/j.watres.2013.09.010.
El Chanati, H., M. S. El-Abbasy, F. Mosleh, A. Senouci, M. Abouhamad, I. Gkountis, T. Zayed, and H. Al-Derham. 2016. “Multi-criteria decision making models for water pipelines.” J. Perform. Constr. Facil. 30 (4): 04015090. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000842.
Fares, H., and T. Zayed. 2010. “Hierarchical fuzzy expert system for risk of failure of water mains.” J. Pipeline Syst. Eng. Pract. 1 (1): 53–62. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000037.
Farshad, M. 2004. “Two new criteria for the service life prediction of plastics pipes.” Polym. Test. 23 (8): 967–972. https://doi.org/10.1016/j.polymertesting.2004.04.010.
Folkman, S. 2018. Water main break rates in the USA and Canada: A comprehensive study. Logan, UT: Utah State Univ., Buried Structures Laboratory.
Fox, S., W. Shepherd, R. Collins, and J. Boxall. 2016. “Experimental quantification of contaminant ingress into a buried leaking pipe during transient events.” J. Hydraul. Eng. 142 (1): 04015036. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001040.
Fuentes, R., T. Chapman, M. Cook, J. Scanlan, Z. Li, and R. C. Richardson. 2017. “Briefing: UK-RAS white paper in robotics and autonomous systems for resilient infrastructure.” Proc. Inst. Civ. Eng.-Smart Infrastruct. Constr. 170 (3): 72–79. https://doi.org/10.1680/jsmic.17.00013.
Furlong, C., S. De Silva, L. Guthrie, and R. Considine. 2016. “Developing a water infrastructure planning framework for the complex modern planning environment.” Utilities Policy 38: 1–10.
Gedam, A., S. Mangulkar, and B. Gandhi. 2016. “Prediction of sewer pipe main condition using the linear regression approach.” J. Geosci. Environ. Protect. 4 (5): 100–105. https://doi.org/10.4236/gep.2016.45010.
Geem, Z. W., C.-L. Tseng, J. Kim, and C. Bae. 2007. “Trenchless water pipe condition assessment using artificial neural network.” In Pipelines 2007: Advances and experiences with trenchless pipeline projects, 1–9. Reston, VA: ASCE. https://doi.org/10.1061/40934(252)26.
Giustolisi, O., D. Laucelli, and A. F. Colombo. 2009. “Deterministic versus stochastic design of water distribution networks.” J. Water Resour. Plann. Manage. 135 (2): 117–127. https://doi.org/10.1061/(ASCE)0733-9496(2009)135:2(117).
Hahn, G. J., and S. S. Shapiro. 1994. Statistical models in engineering. New York: Wiley.
Harvey, R. R., and E. A. McBean. 2014. “Comparing the utility of decision trees and support vector machines when planning inspections of linear sewer infrastructure.” J. Hydroinf. 16 (6): 1265–1279. https://doi.org/10.2166/hydro.2014.007.
Haykin, S. 2010. Neural networks and learning machines. 3rd ed. Chennai, India: Pearson Education India.
Hernández, N., N. Caradot, H. Sonnenberg, P. Rouault, and A. Torres. 2018. “Support tools to predict the critical structural condition of uninspected pipes for case studies of Germany and Colombia.” Water Pract. Technol. 13 (4): 794–802. https://doi.org/10.2166/wpt.2018.085.
Hukka, J. J., and T. S. Katko. 2015. “Resilient asset management and governance for deteriorating water services infrastructure.” Procedia Econ. Finance 21: 112–119. https://doi.org/10.1016/S2212-5671(15)00157-4.
IKT (Institute for Underground Infrastructure). 2014. “Bildreferenzkatalog—Private Abwasserleitungen” [Image reference catalog—Private sewers]. [In German.] Accessed September 22, 2021. https://www.ikt.de/blog/neuer-bildreferenzkatalog-zu-schaeden-an-privaten-abwasserleitungen/.
Ismail, A., and A. El-Shamy. 2009. “Engineering behaviour of soil materials on the corrosion of mild steel.” Appl. Clay Sci. 42 (3–4): 356–362. https://doi.org/10.1016/j.clay.2008.03.003.
Jones, D. F., S. K. Mirrazavi, and M. Tamiz. 2002. “Multi-objective meta-heuristics: An overview of the current state-of-the-art.” Eur. J. Oper. Res. 137 (1): 1–9. https://doi.org/10.1016/S0377-2217(01)00123-0.
Jun, H. J., J. K. Park, and C. H. Bae. 2020. “Factors affecting steel water-transmission pipe failure and pipe-failure mechanisms.” J. Environ. Eng. 146 (6): 04020034. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001692.
Kabir, G., N. B. C. Balek, and S. Tesfamariam. 2018. “Sewer structural condition prediction integrating Bayesian model averaging with logistic regression.” J. Perform. Constr. Facil. 32 (3): 04018019. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001162.
Kabir, G., S. Tesfamariam, A. Francisque, and R. Sadiq. 2015. “Evaluating risk of water mains failure using a Bayesian belief network model.” Eur. J. Oper. Res. 240 (1): 220–234. https://doi.org/10.1016/j.ejor.2014.06.033.
Kakoudakis, K., K. Behzadian, R. Farmani, and D. Butler. 2017. “Pipeline failure prediction in water distribution networks using evolutionary polynomial regression combined with K-means clustering.” Urban Water J. 14 (7): 737–742. https://doi.org/10.1080/1573062X.2016.1253755.
Khan, Z., T. Zayed, and O. Moselhi. 2010. “Structural condition assessment of sewer pipelines.” J. Perform. Constr. Facil. 24 (2): 170–179. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000081.
Khazraeializadeh, S., L. F. Gay, and A. Bayat. 2014. “Comparative analysis of sewer physical condition grading protocols for the City of Edmonton.” Can. J. Civ. Eng. 41 (9): 811–818. https://doi.org/10.1139/cjce-2014-0077.
Kim, J., C. Bae, H. Woo, J. Kim, and S. Hong. 2007. “Assessment of residual tensile strength on cast iron pipes.” In Pipelines 2007: Advances and experiences with trenchless pipeline projects, 1–7. Reston, VA: ASCE. https://doi.org/10.1061/40934(252)62.
Kleiner, Y., A. Nafi, and B. Rajani. 2010. “Planning renewal of water mains while considering deterioration, economies of scale and adjacent infrastructure.” Water Sci. Technol. Water Supply 10 (6): 897–906. https://doi.org/10.2166/ws.2010.571.
Kleiner, Y., and B. Rajani. 1999. “Using limited data to assess future needs.” J. Am. Water Works Assoc. 91 (7): 47–61. https://doi.org/10.1002/j.1551-8833.1999.tb08664.x.
Kleiner, Y., and B. Rajani. 2001. “Comprehensive review of structural deterioration of water mains: Statistical models.” Urban Water 3 (3): 131–150. https://doi.org/10.1016/S1462-0758(01)00033-4.
Kleiner, Y., and B. Rajani. 2002. “Forecasting variations and trends in water-main breaks.” J. Infrastruct. Syst. 8 (4): 122–131. https://doi.org/10.1061/(ASCE)1076-0342(2002)8:4(122).
Kleiner, Y., and B. Rajani. 2008. “Prioritising individual water mains for renewal.” In Proc., World Environmental and Water Resources Congress 2008: Ahupua’A, 1–10. Reston, VA: ASCE. https://doi.org/10.1061/40976(316)498.
Kleiner, Y., B. Rajani, and R. Sadiq. 2005. Risk management of large-diameter water transmission mains. Denver: American Water Works Association.
Kleiner, Y., R. Sadiq, and B. Rajani. 2004. “Modeling failure risk in buried pipes using fuzzy Markov deterioration process.” In Pipeline Engineering and Construction: What’s on the Horizon? 1–12. Reston, VA: ASCE. https://doi.org/10.1061/40745(146)7.
Kley, G., I. Kropp, T. Schmidt, and N. Caradot. 2013. SEMA D1. 1—Review of available technologies and methodologies for sewer condition evaluation. Berlin: Kompetenzzentrum Wasser Berlin.
Koo, D.-H., and S. T. Ariaratnam. 2006. “Innovative method for assessment of underground sewer pipe condition.” Autom. Constr. 15 (4): 479–488. https://doi.org/10.1016/j.autcon.2005.06.007.
Kutyłowska, M., and H. Hotloś. 2014. “Failure analysis of water supply system in the Polish city of Głogów.” Eng. Fail. Anal. 41: 23–29. https://doi.org/10.1016/j.engfailanal.2013.07.019.
Laakso, T., T. Kokkonen, I. Mellin, and R. Vahala. 2018. “Sewer condition prediction and analysis of explanatory factors.” Water 10 (9): 1239. https://doi.org/10.3390/w10091239.
Landau, L. J. 2012. Concepts for neural networks: A survey. New York: Springer.
Lawless, J. F. 2011. Statistical models and methods for lifetime data. New York: Wiley.
Le Gat, Y. 2008. “Modelling the deterioration process of drainage pipelines.” Urban Water J. 5 (2): 97–106. https://doi.org/10.1080/15730620801939398.
Le Gat, Y., and P. Eisenbeis. 2000. “Using maintenance records to forecast failures in water networks.” Urban Water 2 (3): 173–181. https://doi.org/10.1016/S1462-0758(00)00057-1.
Le Gauffre, P., C. Joannis, E. Vasconcelos, D. Breysse, C. Gibello, and J.-J. Desmulliez. 2007. “Performance indicators and multicriteria decision support for sewer asset management.” J. Infrastruct. Syst. 13 (2): 105–114. https://doi.org/10.1061/(ASCE)1076-0342(2007)13:2(105).
Li, D., A. Cong, and S. Guo. 2019. “Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification.” Autom. Constr. 101 (May): 199–208. https://doi.org/10.1016/j.autcon.2019.01.017.
Li, Z., B. Zhang, Y. Wang, F. Chen, R. Taib, V. Whiffin, and Y. Wang. 2014. “Water pipe condition assessment: A hierarchical beta process approach for sparse incident data.” Mach. Learn. 95 (1): 11–26. https://doi.org/10.1007/s10994-013-5386-z.
Liu, Z., and Y. Kleiner. 2013. “State of the art review of inspection technologies for condition assessment of water pipes.” Measurement 46 (1): 1–15. https://doi.org/10.1016/j.measurement.2012.05.032.
Loganathan, G. V., S. Park, and H. Sherali. 2002. “Threshold break rate for pipeline replacement in water distribution systems.” J. Water Resour. Plann. Manage. 128 (4): 271–279. https://doi.org/10.1061/(ASCE)0733-9496(2002)128:4(271).
Lu, J. P., P. Davis, and L. Burn. 2003. “Lifetime prediction for ABS pipes subjected to combined pressure and deflection loading.” Polym. Eng. Sci. 43 (2): 444–462. https://doi.org/10.1002/pen.10036.
Lubini, A. T., and M. Fuamba. 2011. “Modeling of the deterioration timeline of sewer systems.” Can. J. Civ. Eng. 38 (12): 1381–1390. https://doi.org/10.1139/l11-103.
Makana, L. O., N. Metje, I. Jefferson, M. Sackey, and C. D. Rogers. 2020. “Cost estimation of utility strikes: Towards proactive management of street works.” Infrast. Asset Manage. 7 (2): 64–76.
Makar, J. 1999. “Diagnostic techniques for sewer systems.” J. Infrastruct. Syst. 5 (2): 69–78. https://doi.org/10.1061/(ASCE)1076-0342(1999)5:2(69).
Makropoulos, C., and D. Butler. 2005. “A neurofuzzy spatial decision support system for pipe replacement prioritisation.” Urban Water J. 2 (3): 141–150. https://doi.org/10.1080/15730620500236674.
Malek Mohammadi, M., M. Najafi, S. Kermanshachi, V. Kaushal, and R. Serajiantehrani. 2020. “Factors influencing the condition of sewer pipes: State-of-the-art review.” J. Pipeline Syst. Eng. Pract. 11 (4): 03120002. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000483.
Marlow, D., P. Davis, D. Beale, S. Burn, and A. Urquhart. 2009. Remaining asset life: A state of the art review. Denver: Water Environment Research Foundation.
Mashford, J., D. Marlow, D. Tran, and R. May. 2011. “Prediction of sewer condition grade using support vector machines.” J. Comput. Civ. Eng. 25 (4): 283–290. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000089.
McDonald, S., J. Zhao, and K. Yehuda. 2001. Guidelines for condition assessment and rehabilitation of large sewers. Ottawa: Institute for Research in Construction National Research Council Canada.
Micevski, T., G. Kuczera, and P. Coombes. 2002. “Markov model for storm water pipe deterioration.” J. Infrastruct. Syst. 8 (2): 49–56. https://doi.org/10.1061/(ASCE)1076-0342(2002)8:2(49).
Moglia, M., P. Davis, and S. Burn. 2008. “Strong exploration of a cast iron pipe failure model.” Reliab. Eng. Syst. Saf. 93 (6): 885–896. https://doi.org/10.1016/j.ress.2007.03.033.
Mohammadi, M. M., M. Najafi, A. Tabesh, J. Riley, and J. Gruber. 2019. “Condition prediction of sanitary sewer pipes.” In Pipelines 2019: Condition assessment, construction, and rehabilitation, 117–126. Reston, VA: ASCE.
Mounce, S. R., W. J. Shepherd, J. B. Boxall, K. V. Horoshenkov, and J. H. Boyle. 2021. “Autonomous robotics for water and sewer networks.” IAHR Hydrolink 2021 (2): 55–62.
Najafi, M., and G. Kulandaivel. 2005. “Pipeline condition prediction using neural network models.” In Pipelines 2005: Optimizing pipeline design, perations, and maintenance in today’s economy, 767–781. Reston, VA: ASCE. https://doi.org/10.1061/40800(180)61.
Najjaran, H., B. Rajani, and R. Sadiq. 2004. “A fuzzy expert system for deterioration modeling of buried metallic pipes.” In Proc., IEEE Annual Meeting of the Fuzzy Information, 2004, 373–378. New York: IEEE.
Najjaran, H., R. Sadiq, and B. Rajani. 2006. “Fuzzy expert system to assess corrosion of cast/ductile iron pipes from backfill properties.” Comput.-Aided Civ. Infrastruct. Eng. 21 (1): 67–77. https://doi.org/10.1111/j.1467-8667.2005.00417.x.
NASSCO (National Association of Sewer Service Companies). 2016. Pipeline assessment certification program (PACP). Frederick, MD: NASSCO.
National Archives. 2008. “The water supply and sewerage services (customer service standards) regulations.” Accessed September 28, 2021. https://www.legislation.gov.uk/uksi/2008/594/schedules.
National Research Council Canada. 2003. Deterioration and inspection of water distribution systems, a best practice by the national guide to sustainable municipal infrastructure. Ottawa: National Research Council of Canada.
Nielsen, A. H., J. Vollertsen, H. S. Jensen, T. Wium-Andersen, and T. Hvitved-Jacobsen. 2008. “Influence of pipe material and surfaces on sulfide related odor and corrosion in sewers.” Water Res. 42 (15): 4206–4214. https://doi.org/10.1016/j.watres.2008.07.013.
Oliveira, D., W. Guo, L. Soibelman, and J. Garrett. 2007. “Spatial data management and analysis in sewer systems’ condition assessment: An overview.” Comput. Civ. Eng. 2007: 391–398. https://doi.org/10.1061/40937(261)48.
Ormsby, C. 2009. “A framework for estimating the total cost of buried municipal infrastructure renewal projects.” M.S. thesis, Dept. of Civil Engineering and Applied Mechanics, McGill Univ.
Osman, H., and K. Bainbridge. 2011. “Comparison of statistical deterioration models for water distribution networks.” J. Perform. Constr. Facil. 25 (3): 259–266. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000157.
Park, S. W., and G. Loganathan. 2002. “Methodology for economically optimal replacement of pipes in water distribution systems: 1. Theory.” KSCE J. Civ. Eng. 6 (4): 539–543. https://doi.org/10.1007/BF02842007.
Parrott, C., T. J. Dodd, J. Boxall, and K. Horoshenkov. 2020. “Simulation of the behavior of biologically-inspired swarm robots for the autonomous inspection of buried pipes.” Tunnelling Underground Space Technol. 101 (Jul): 103356. https://doi.org/10.1016/j.tust.2020.103356.
Pelletier, G., A. Mailhot, and J.-P. Villeneuve. 2003. “Modeling water pipe breaks—Three case studies.” J. Water Resour. Plann. Manage. 129 (2): 115–123. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:2(115).
Poulton, M., Y. Le Gat, and B. Brémond. 2009. “The impact of pipe segment length on break predictions in water distribution systems.” In Strategic asset management of water supply and wastewater infrastructures, 419–430. London: IWA Publishing.
Rajani, B. 2000. Investigation of grey cast iron water mains to develop a methodology for estimating service life. Denver: American Water Works Association.
Rajani, B., and Y. Kleiner. 2001. “Comprehensive review of structural deterioration of water mains: Physically based models.” Urban Water 3 (3): 151–164. https://doi.org/10.1016/S1462-0758(01)00032-2.
Rajani, B., and J. Makar. 2000. “A methodology to estimate remaining service life of grey cast iron water mains.” Can. J. Civ. Eng. 27 (6): 1259–1272. https://doi.org/10.1139/l00-073.
Rajani, B., and S. Tesfamariam. 2007. “Estimating time to failure of cast-iron water mains.” Proc. Inst. Civ. Eng.-Water Manage. 160 (2): 83–88. https://doi.org/10.1680/wama.2007.160.2.83.
Rajani, B., C. Zhan, and S. J. C. G. J. Kuraoka. 1996. “Pipe soil interaction analysis of jointed water mains.” Can. Geotech. J. 33 (3): 393–404.
Rajeev, P., J. Kodikara, D. Robert, P. Zeman, and B. Rajani. 2014. “Factors contributing to large diameter water pipe failure.” Water Asset Manage. Int. 10 (3): 9–14.
Reed, C., A. J. Robinson, and D. Smart. 2006. Potential techniques for the assessment of joints in water distribution pipelines. Denver: American Water Works Association.
Rezaei, H., B. Ryan, and I. Stoianov. 2015. “Pipe failure analysis and impact of dynamic hydraulic conditions in water supply networks.” Procedia Eng. 119: 253–262. https://doi.org/10.1016/j.proeng.2015.08.883.
RIONED. 2017. “Gegevenswoordenboek stedelijk water” [Data dictionary urban water]. Accessed September 20, 2022. https://www.riool.net/gegevenswoordenboek-stedelijk-water.
Rogers, C., et al. 2017. “Assessing the underworld—Understanding the context for engineering the next generation infrastructure.” In Proc., Int. Symp. for Next Generation Infrastructure (ISNGI), 341–350. London: International Symposium for Next Generation Infrastructure.
Rokstad, M., and R. Ugarelli. 2016. “Improving the benefits of sewer condition deterioration modelling through information content analysis.” Water Sci. Technol. 74 (10): 2270–2279. https://doi.org/10.2166/wst.2016.419.
Sadiq, R., B. Rajani, and Y. Kleiner. 2004. “Fuzzy-based method to evaluate soil corrosivity for prediction of water main deterioration.” J. Infrastruct. Syst. 10 (4): 149–156. https://doi.org/10.1061/(ASCE)1076-0342(2004)10:4(149).
Salman, B., and O. Salem. 2012. “Modeling failure of wastewater collection lines using various section-level regression models.” J. Infrastruct. Syst. 18 (2): 146–154. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000075.
Savic, D. A. 2009. “The use of data-driven methodologies for prediction of water and wastewater asset failures.” In Risk management of water supply and sanitation systems, 181–190. New York: Springer.
Scheidegger, A., J. P. Leitão, and L. Scholten. 2015. “Statistical failure models for water distribution pipes—A review from a unified perspective.” Water Res. 83 (15): 237–247. https://doi.org/10.1016/j.watres.2015.06.027.
Seica, M. V., and J. A. Packer. 2004. “Finite element evaluation of the remaining mechanical strength of deteriorated cast iron pipes.” J. Eng. Mater. Technol. 126 (1): 95–102. https://doi.org/10.1115/1.1631027.
Seica, M. V., and J. A. Packer. 2006. “Simplified numerical method to evaluate the mechanical strength of cast iron water pipes.” J. Infrastruct. Syst. 12 (1): 60–67. https://doi.org/10.1061/(ASCE)1076-0342(2006)12:1(60).
Shirzad, A., M. Tabesh, and R. Farmani. 2014. “A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks.” KSCE J. Civ. Eng. 18 (4): 941–948. https://doi.org/10.1007/s12205-014-0537-8.
Sivanandam, S., S. Sumathi, and S. Deepa. 2007. Introduction to fuzzy logic using MATLAB. Berlin: Springer.
Sousa, V., J. P. Matos, and N. Matias. 2014. “Evaluation of artificial intelligence tool performance and uncertainty for predicting sewer structural condition.” Autom. Constr. 44: 84–91. https://doi.org/10.1016/j.autcon.2014.04.004.
Tesfamariam, S., B. Rajani, and R. Sadiq. 2006. “Possibilistic approach for consideration of uncertainties to estimate structural capacity of ageing cast iron water mains.” Can. J. Civ. Eng. 33 (8): 1050–1064. https://doi.org/10.1139/l06-042.
Thienen, P., B. Bergmans, and R. Diemel. 2018. “Advances in development and testing of a system of autonomous inspection robots for drinking water distribution systems.” In Proc., WDSA/CCWI Joint Conf. Kingston, ON, Canada: Queen’s Univ.
Tran, D., A. Ng, B. J. C. Perera, S. Burn, and P. Davis. 2006. “Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes.” Urban Water J. 3 (3): 175–184. https://doi.org/10.1080/15730620600961684.
Tran, D., B. C. Perera, and A. Ng. 2009. “Comparison of structural deterioration models for stormwater drainage pipes.” Comput.-Aided Civ. Infrastruct. Eng. 24 (2): 145–156. https://doi.org/10.1111/j.1467-8667.2008.00577.x.
Tran, D. H., A. Ng, and B. J. C. Perera. 2007. “Neural networks deterioration models for serviceability condition of buried stormwater pipes.” Eng. Appl. Artif. Intell. 20 (8): 1144–1151. https://doi.org/10.1016/j.engappai.2007.02.005.
Tscheikner-Gratl, F., N. Caradot, F. Cherqui, J. P. Leitão, M. Ahmadi, J. G. Langeveld, Y. Le Gat, L. Scholten, B. Roghani, and J. P. Rodríguez. 2019. “Sewer asset management—State of the art and research needs.” Urban Water J. 16 (9): 662–675. https://doi.org/10.1080/1573062X.2020.1713382.
Ugarelli, R. M., I. Selseth, Y. Le Gat, J. Rostum, and A. H. Krogh. 2013. “Wastewater pipes in Oslo: From condition monitoring to rehabilitation planning.” Water Practice Technol. 8 (3–4): 487–494.
USEPA. 2002. Deteriorating buried infrastructure management challenges and strategies. Washington, DC: American Water Works Service, Engineering Dept., USEPA.
Vanrenterghem-Raven, A. 2007. “Risk factors of structural degradation of an urban water distribution system.” J. Infrastruct. Syst. 13 (1): 55–64. https://doi.org/10.1061/(ASCE)1076-0342(2007)13:1(55).
van Riel, W., J. Langeveld, P. Herder, and F. Clemens. 2014. “Intuition and information in decision-making for sewer asset management.” Urban Water J. 11 (6): 506–518. https://doi.org/10.1080/1573062X.2014.904903.
van Riel, W., E. van Bueren, J. Langeveld, P. Herder, and F. Clemens. 2016. “Decision-making for sewer asset management: Theory and practice.” Urban Water J. 13 (1): 57–68. https://doi.org/10.1080/1573062X.2015.1011667.
VSA-DSS (Verband Schweizer Abwasser- und Gewässerschutzfachleute). 2014. Datenstruktur siedlungsentwässerung [Data structure urban water]. Glattbrugg, Switzerland: VSA.
Wang, C.-W., Z.-G. Niu, H. Jia, and H.-W. Zhang. 2010. “An assessment model of water pipe condition using Bayesian inference.” J. Zhejiang Univ.-SCIENCE A 11 (7): 495–504. https://doi.org/10.1631/jzus.A0900628.
Wang, Y., T. Zayed, and O. Moselhi. 2009. “Prediction models for annual break rates of water mains.” J. Perform. Constr. Facil. 23 (1): 47–54. https://doi.org/10.1061/(ASCE)0887-3828(2009)23:1(47).
Wasson, C. S. 2015. System engineering analysis, design, and development: Concepts, principles, and practices. Hoboken, NJ: Wiley.
Watson, T., C. Christian, A. Mason, M. Smith, and R. Meyer. 2004. “Bayesian-based pipe failure model.” J. Hydroinf. 6 (4): 259–264. https://doi.org/10.2166/hydro.2004.0019.
Wirahadikusumah, R., D. Abraham, and T. Iseley. 2001. “Challenging issues in modeling deterioration of combined sewers.” J. Infrastruct. Syst. 7 (2): 77–84. https://doi.org/10.1061/(ASCE)1076-0342(2001)7:2(77).
Wood, A., and B. J. Lence. 2009. “Using water main break data to improve asset management for small and medium utilities: District of Maple Ridge, BC.” J. Infrastruct. Syst. 15 (2): 111–119. https://doi.org/10.1061/(ASCE)1076-0342(2009)15:2(111).
WRc (Water Research Centre). 2013. Manual of sewer condition classification. 5th revised ed. Swindon, UK: WRc.
WRc (Water Research Centre). 2021. “Sewerage risk manual.” Accessed September 22, 2021. http://srm.wrcplc.co.uk/home.aspx.
Younis, R., M. Knight, Y. Kleiner, J. Matthews, and J. Jung. 2015. “Drinking water pipelines defect coding system.” In Pipelines 2015, 1110–1124. Reston, VA: ASCE. https://doi.org/10.1061/9780784479360.102.
Zhou, Y., K. Vairavamoorthy, and F. Grimshaw. 2009. “Development of a fuzzy based pipe condition assessment model using PROMETHEE.” In Proc., World Environmental and Water Resources Congress 2009: Great Rivers, 1–10. Reston, VA: ASCE. https://doi.org/10.1061/41036(342)485.
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Journal of Pipeline Systems Engineering and Practice
Volume 13 • Issue 3 • August 2022
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© 2022 American Society of Civil Engineers.
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Received: Oct 5, 2021
Accepted: Feb 19, 2022
Published online: May 17, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 17, 2022
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