A risk-based soft sensor for failure rate monitoring in water distribution network via adaptive neuro-fuzzy interference systems
Status PubMed-not-MEDLINE Language English Country Great Britain, England Media electronic
Document type Journal Article
PubMed
37500665
PubMed Central
PMC10374646
DOI
10.1038/s41598-023-38620-w
PII: 10.1038/s41598-023-38620-w
Knihovny.cz E-resources
- Publication type
- Journal Article MeSH
Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure.
Association of Talent Under Liberty in Technology Tallinn Estonia
Department of Civil Engineering Ferdowsi University of Mashhad Mashhad Iran
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Abd Ellah RG. Water resources in Egypt and their challenges, lake Nasser case study. Egypt. J. Aquat. Res. 2020;46(1):1–12. doi: 10.1016/j.ejar.2020.03.001. DOI
P. Aghapoor Khameneh, S. M. Miri Lavasani, R. Nabizadeh Nodehi, and R. Arjmandi, Water distribution network failure analysis under uncertainty. Int. J. Environ. Sci. Technol., vol. 17, no. 1, pp. 421–432. (2020), 10.1007/s13762-019-02362-y.
Tang K, Parsons DJ, Jude S. Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system. Reliab. Eng. Syst. Saf. 2019;186:24–36. doi: 10.1016/j.ress.2019.02.001. DOI
Nikoloudi E, Romano M, Memon FA, Kapelan Z. Interactive decision support methodology for near real-time response to failure events in a water distribution network. J. Hydroinf. 2021;23(3):483–499. doi: 10.2166/hydro.2020.101. DOI
Hernandez Hernandez E, Ormsbee L. segment-based assessment of consequences of failure on water distribution systems. J. Water Resour. Plan Manag. 2021 doi: 10.1061/(ASCE)WR.1943-5452.0001340. DOI
Schurer R, Hijnen WAM, Van Der Wal A. The significance of the biomass subfraction of high-MW organic carbon for the microbial growth and maintenance potential of disinfectant-free drinking water produced from surface water. Water Res. 2022;209:117898. doi: 10.1016/j.watres.2021.117898. PubMed DOI
Sangroula U, Han K-H, Koo K-M, Gnawali K, Yum K-T. Optimization of water distribution networks using genetic algorithm based SOP–WDN program. Water (Basel) 2022;14(6):851. doi: 10.3390/w14060851. DOI
O. R. D. US EPA, EPANET 2.2.0: An EPA and water community collaboration. (2020). [Online]. Available: https://www.epa.gov/sciencematters/epanet-220-epa-and-water-community-collaboration
Wéber R, Huzsvár T, Hős C. Vulnerability analysis of water distribution networks to accidental pipe burst. Water Res. 2020;184:116178. doi: 10.1016/j.watres.2020.116178. PubMed DOI
Sun J, Wang R, Wang X, Yang H, Ping J. Spatial cluster analysis of bursting pipes in water supply networks. Procedia Eng. 2014;70:1610–1618. doi: 10.1016/j.proeng.2014.02.178. DOI
Gheisi A, Naser Gh. Multi-aspect performance analysis of water distribution systems under pipe failure. Procedia Eng. 2015;119:158–167. doi: 10.1016/j.proeng.2015.08.867. DOI
Piratla KR, Yerri SR, Yazdekhasti S, Cho J, Koo D, Matthews JC. Empirical analysis of water-main failure consequences. Procedia Eng. 2015;118:727–734. doi: 10.1016/j.proeng.2015.08.507. DOI
Sousa J, Ribeiro L, Muranho J, Marques AS. Locating leaks in water distribution networks with simulated annealing and graph theory. Procedia Eng. 2015;119:63–71. doi: 10.1016/j.proeng.2015.08.854. DOI
Jafari SM, Zahiri AR, Bozorg Hadad O, Mohammad Rezapour Tabari M. A hybrid of six soft models based on ANFIS for pipe failure rate forecasting and uncertainty analysis: A case study of Gorgan city water distribution network. Soft. Comput. 2021;25(11):7459–7478. doi: 10.1007/s00500-021-05706-4. DOI
Zohra HF, Mahmouda B, Luc D. Vulnerability assessment of water supply network. Energy Proc. 2012;18:772–783. doi: 10.1016/j.egypro.2012.05.093. DOI
Tuhovčák L, Tauš M, Míka P. Indirect condition assessment of water mains. Proc. Eng. 2014;70:1669–1678. doi: 10.1016/j.proeng.2014.02.184. DOI
Khramenkov SV, Primin OG. Ensuring the reliability of the water piping of the Moscow water supply system. J. Water Supply Res. Technol. AQUA. 2005;54(2):127–132. doi: 10.2166/aqua.2005.0012. DOI
Maslak V, Nasonkina N, Sakhnovskaya V, Gutarova M, Antonenko S, Nemova D. Evaluation of technical condition of water supply networks on undermined territories. Proc. Eng. 2015;117:980–989. doi: 10.1016/j.proeng.2015.08.206. DOI
Trietsch EA, Vreeburg JHG. Reliability of valves and section isolation. Water Supp. 2005;5(2):47–51. doi: 10.2166/ws.2005.0021. DOI
Kwon HJ, Kwon H-K. Estimations of safety degree of water distribution system. Proc. Eng. 2016;154:398–402. doi: 10.1016/j.proeng.2016.07.500. DOI
Peabody AW, Bianchetti RL, Peabody AW. Peabody’s control of pipeline corrosion. 2. NACE International, The Corrosion Society; 2001.
Sargaonkar A, Kamble S, Rao R. Model study for rehabilitation planning of water supply network. Comput. Environ. Urban Syst. 2013;39:172–181. doi: 10.1016/j.compenvurbsys.2012.08.002. DOI
Marzouk M, Hamid SA, El-Said M. A methodology for prioritizing water mains rehabilitation in Egypt. HBRC J. 2015;11(1):114–128. doi: 10.1016/j.hbrcj.2014.03.002. DOI
S. E. J. M. M. McDonald R. Desnoyers, Failure modes and mechanisms in gray cast iron pipes. In: Underground Infrastructure Research, CRC Press, (2001).
A. B. Paradkar, An evaluation of failure modes for cast iron and ductile iron water pipes. (2013), [Online]. Available: https://rc.library.uta.edu/uta-ir/handle/10106/11660
Larry W. Mays, Reliability analysis of water distribution systems. ASCE; 1989.
Kleiner Y, Rajani B. Comprehensive review of structural deterioration of water mains: Statistical models. Urban Water. 2001;3(3):131–150. doi: 10.1016/S1462-0758(01)00033-4. DOI
Rezaei H, Ryan B, Stoianov I. Pipe failure analysis and impact of dynamic hydraulic conditions in water supply networks. Proc. Eng. 2015;119:253–262. doi: 10.1016/j.proeng.2015.08.883. DOI
Tabesh M, Soltani J, Farmani R, Savic D. Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling. J. Hydroinf. 2009;11(1):1–17. doi: 10.2166/hydro.2009.008. DOI
Seo J, Koo M, Kim K, Koo J. A study on the probability of failure model based on the safety factor for risk assessment in a water supply network. Proc. Eng. 2015;119:206–215. doi: 10.1016/j.proeng.2015.08.877. DOI
Giudicianni C, Herrera M, Di Nardo A, Carravetta A, Ramos HM, Adeyeye K. Zero-net energy management for the monitoring and control of dynamically-partitioned smart water systems. J Clean Prod. 2020;252:119745. doi: 10.1016/j.jclepro.2019.119745. DOI
Wilson D, Filion YR, Moore ID. Identifying factors that influence the factor of safety and probability of failure of large-diameter, cast iron water mains with a mechanistic, stochastic model: A case study in the city of Hamilton. Proc. Eng. 2015;119:130–138. doi: 10.1016/j.proeng.2015.08.863. DOI
RStudio Team, RStudio: integrated development environment for R. Boston, MA, (2015). [Online]. Available: http://www.rstudio.com/
A. Kiyan, M. Gheibi, M. Akrami, R. Moezzi, K. Behzadian, and H. Taghavian, The operation of urban water treatment plants: A Review of smart dashboard frameworks. Environ. Ind. Lett., (2023) 10.15157/EIL.2023.1.1.28-45.
M. Gheibi, B. Chahkandi, K. Behzadian, M. Akrami, and R. Moezzi, Evaluation of ceramic water filters’ performance and analysis of managerial insights by SWOT matrix. Environ. Ind. Lett., (2023), 10.15157/EIL.2023.1.1.1-9.
Rego FC, Rocha MS. Climatic patterns in the mediterranean region. Ecol. Med. 2014;40(1):49–59. doi: 10.3406/ecmed.2014.1269. DOI
Żywiec J, Piegdoń I, Tchórzewska-Cieślak B. Failure analysis of the water supply network in the aspect of climate changes on the example of the central and eastern Europe region. Sustainability. 2019;11(24):6886. doi: 10.3390/su11246886. DOI
M. Nakhaei, M. Akrami, M. Gheibi, P. Daniel Urbina Coronado, M. Hajiaghaei-Keshteli, and J. Mahlknecht, A novel framework for technical performance evaluation of water distribution networks based on the water-energy nexus concept. Energy Conv. Manag, vol. 273, p. 116422, (2022) 10.1016/j.enconman.2022.116422
Microsoft Excel. London: SAGE Publications, Ltd., (2021).
W. Bajjali, Arcgis for environmental and water issues. New York, NY: Springer Science+Business Media, (2017).
Shahsavar MM, Akrami M, Gheibi M, Kavianpour B, Fathollahi-Fard AM, Behzadian K. Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling. Energy Conv. Manag. 2021;248:114794. doi: 10.1016/j.enconman.2021.114794. DOI
Arab M, et al. A soft-sensor for sustainable operation of coagulation and flocculation units. Eng. Appl. Artif. Intell. 2022;115:105315. doi: 10.1016/j.engappai.2022.105315. DOI
Gheibi M, Karrabi M, Eftekhari M. Designing a smart risk analysis method for gas chlorination units of water treatment plants with combination of failure mode effects analysis, shannon entropy, and petri net modeling. Ecotoxicol. Environ. Saf. 2019;171:600–608. doi: 10.1016/j.ecoenv.2019.01.032. PubMed DOI
Akbarian H, Jalali FM, Gheibi M, Hajiaghaei-Keshteli M, Akrami M, Sarmah AK. A sustainable decision support system for soil bioremediation of toluene incorporating UN sustainable development goals. Environ. Pollut. 2022;307:119587. doi: 10.1016/j.envpol.2022.119587. PubMed DOI
Zabihi O, Siamaki M, Gheibi M, Akrami M, Hajiaghaei-Keshteli M. A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations. Int. J. Disaster Risk Reduct. 2023;84:103470. doi: 10.1016/j.ijdrr.2022.103470. DOI
Sadeghioon AM, Metje N, Chapman D, Anthony C. Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms. Urban Water J. 2018;15(4):287–295. doi: 10.1080/1573062X.2018.1424213. DOI
Bakker M, Jung D, Vreeburg J, Van De Roer M, Lansey K, Rietveld L. Detecting pipe bursts using heuristic and CUSUM methods. Proc. Eng. 2014;70:85–92. doi: 10.1016/j.proeng.2014.02.011. DOI
Gamboa-Medina MM, Reis LFR, Guido RC. feature extraction in pressure signals for leak detection in water networks. Proc. Eng. 2014;70:688–697. doi: 10.1016/j.proeng.2014.02.075. DOI
Sala D, Kołakowski P. Detection of leaks in a small-scale water distribution network based on pressure data—experimental verification. Proc. Eng. 2014;70:1460–1469. doi: 10.1016/j.proeng.2014.02.161. DOI
Aldaghi A, Gheibi M, Akrami M, Hajiaghaei-Keshteli M. A smart simulation-optimization framework for solar-powered desalination systems. Groundw. Sustain. Dev. 2022;19:100861. doi: 10.1016/j.gsd.2022.100861. DOI
Hazewinkel M., Encyclopaedia of mathematics. Dordrecht: Springer Netherlands, (1994). 10.1007/978-94-009-5983-5.
H. Omori, Y. Maeda, N. Miyazaki, and A. Yoshioka, Singular Systems of Exponential Functions. In: Noncommutative Differential Geometry and Its Applications to Physics, Y. Maeda, H. Moriyoshi, H. Omori, D. Sternheimer, T. Tate, and S. Watamura, Eds., Dordrecht: Springer Netherlands, (2001), pp 169–186, 10.1007/978-94-010-0704-7_11.
M. Salo, Distributions and the fourier transform. In: Encyclopedia of Applied and Computational Mathematics, B. Engquist, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, (2015), pp. 370–375. 10.1007/978-3-540-70529-1_156.
X. Zhang et al., Gaussian distribution In: Encyclopedia of Machine Learning, C. Sammut and G. I. Webb, Eds., Boston, MA: Springer US, (2011), pp. 425–428. 10.1007/978-0-387-30164-8_323.
P. Suganya, G. Swaminathan, B. Anoop, S. P. S. Prabhakaran, and M. Kavitha, Prediction model for evaluating the raw water quality parameters and its significance in pipe failures of nuclear power plant. In Climate Change and Water Security, S. Kolathayar, A. Mondal, and S. C. Chian, Eds., Singapore: Springer Singapore, (2022), pp. 335–345. 10.1007/978-981-16-5501-2_27.
G. Herrera and P. Morillo, Benchmarking of supervised machine learning algorithms in the early failure prediction of a water pumping system. In: Communication, Smart Technologies and Innovation for Society, Á. Rocha, P. C. López-López, and J. P. Salgado-Guerrero, Eds., Singapore: Springer Singapore, (2022), pp. 535–546. 10.1007/978-981-16-4126-8_48.
Assad A, Bouferguene A. Data mining algorithms for water main condition prediction—comparative analysis. J. Water Resour. Plan Manag. 2022;148(2):4021101. doi: 10.1061/(ASCE)WR.1943-5452.0001512. DOI
Fan X, Wang X, Zhang X, (Bill) Yu X. Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors. Reliab. Eng. Syst. Saf. 2022;219:108185. doi: 10.1016/j.ress.2021.108185. DOI
T. A. Severini, Likelihood methods in statistics, no. 22. In: Oxford statistical science series. Oxford ; New York: Oxford University Press, (2000).
Cassa AM, Van Zyl JE. Predicting the leakage exponents of elastically deforming cracks in pipes. Proc. Eng. 2014;70:302–310. doi: 10.1016/j.proeng.2014.02.034. DOI
Schouwenaars R, Jacobo VH, Ramos E, Ortiz A. Slow crack growth and failure induced by manufacturing defects in HDPE-tubes. Eng. Fail. Anal. 2007;14(6):1124–1134. doi: 10.1016/j.engfailanal.2006.11.066. DOI
Barton NA, Farewell TS, Hallett SH, Acland TF. Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks. Water Res. 2019;164:114926. doi: 10.1016/j.watres.2019.114926. PubMed DOI
S. Rehab-Bekkouche, W. Ghabeche, M. Kaddeche, N. Kiass, and K. Chaoui, Mechanical behaviour of machined polyethylene filaments subjected to aggressive chemical environments. Mechanics, vol. 77, no. 3, pp. 40–46, Jun. 2009, [Online]. Available: https://mechanika.ktu.lt/index.php/Mech/article/view/15233
Alimi L, Chaoui K, Ghabeche W, Chaoui W. Short-term HDPE pipe degradation upon exposure to aggressive environments. Matér. Tech. 2013;101(7):701. doi: 10.1051/mattech/2013083. DOI
Ghabeche W, Alimi L, Chaoui K. Degradation of plastic pipe surfaces in contact with an aggressive acidic environment. Energy Proc. 2015;74:351–364. doi: 10.1016/j.egypro.2015.07.625. DOI
Giraldo-González MM, Rodríguez JP. Comparison of statistical and machine learning models for pipe failure modeling in water distribution networks. Water (Basel) 2020;12(4):1153. doi: 10.3390/w12041153. DOI
Barton NA, Farewell TS, Hallett SH. Using generalized additive models to investigate the environmental effects on pipe failure in clean water networks. NPJ Clean Water. 2020;3(1):31. doi: 10.1038/s41545-020-0077-3. DOI
Boxall JB, O’Hagan A, Pooladsaz S, Saul AJ, Unwin DM. Estimation of burst rates in water distribution mains. Proc. Inst. Civil Eng. Water Manag. 2007;160(2):73–82. doi: 10.1680/wama.2007.160.2.73. DOI
Hekmati N, Rahman MM, Gorjian N, Rameezdeen R, Chow CWK. Relationship between environmental factors and water pipe failure: An open access data study. SN Appl. Sci. 2020;2(11):1806. doi: 10.1007/s42452-020-03581-6. DOI
Robles-Velasco A, Cortés P, Muñuzuri J, Onieva L. Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliab Eng. Syst. 2020 doi: 10.1016/j.ress.2019.106754. DOI