Effects of the Number and Spatial Layout of Distributed Storage Facilities on the Structural Resilience of Urban Stormwater Networks_Vol. 4 No. 2 (JES 2026)_Journal of Engineering System (ISSN: 2959-0604)

Home > Journal of Engineering System (ISSN: 2959-0604) > Vol. 4 No. 2 (JES 2026) >

Effects of the Number and Spatial Layout of Distributed Storage Facilities on the Structural Resilience of Urban Stormwater Networks

Download PDF

DOI: https://doi.org/10.62517/jes.202602223

Author(s)

Xujie Zheng

Affiliation(s)

College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, China

Abstract

To address the lack of quantitative criteria for configuring distributed storage ponds under a fixed total storage volume, this study quantified the effects of storage pond number and spatial layout on the structural resilience of urban stormwater drainage networks. Candidate placement nodes were selected using a topological-distance-based K-Medoids method. The fixed total storage volume was then equally allocated among different numbers of storage ponds to generate schemes with varying facility numbers and placement locations. System structural resilience was evaluated using the extended Global Resilience Analysis (GRA) method, and the Spatial Dispersion Index (SDI) was introduced to characterize the spatial dispersion of storage ponds and clarify its influence on structural resilience. The results showed that increasing the number of storage ponds improved both the mean structural resilience and its lower bound. However, structural resilience did not increase continuously with storage pond number; instead, the maximum value of 0.3938 was achieved under a moderate number of storage ponds. SDI was negatively correlated with structural resilience, and high-resilience schemes depended on the prioritized placement of key nodes. These findings indicate that increasing the number of storage ponds and achieving spatially uniform dispersion are not sufficient conditions for optimizing structural resilience. The results provide a quantitative basis for determining the number of storage ponds, controlling their spatial dispersion, and identifying key placement nodes under a fixed total storage volume.

Keywords

Distributed Storage Ponds; Global Resilience Analysis (GRA); K-Medoids Clustering; Spatial Dispersion Index (SDI); Spatial Layout

References

[1] Fontecha, J.E., Guaje, O.O., Duque, D., Akhavan-Tabatabaei, R., Rodríguez, J.P., Medaglia, A.L., 2020. Combined maintenance and routing optimization for large-scale sewage cleaning. Ann. Oper. Res. 286, 441–474. https://doi.org/10.1007/s10479-019-03342-8 [2] Goorden, M.A., Larsen, K.G., Nielsen, J.E., Nielsen, T.D., Qian, W., Rasmussen, M.R., Srba, J., Zhao, G., 2024. Optimal control strategies for stormwater detention ponds. Nonlinear Anal. Hybrid Syst 53, 101504. https://doi.org/10.1016/j.nahs.2024.101504 [3] Hesarkazzazi, S., Bakhshipour, A.E., Hajibabaei, M., Dittmer, U., Haghighi, A., Sitzenfrei, R., 2022. Battle of centralized and decentralized urban stormwater networks: from redundancy perspective. Water Res. 222, 118910. https://doi.org/10.1016/j.watres.2022.118910 [4] Kamali, B., Ziaei, A.N., Beheshti, A., Farmani, R., 2022. An open-source toolbox for investigating functional resilience in sewer networks based on global resilience analysis. Reliab. Eng. Syst. Saf. 218, 108201. https://doi.org/10.1016/j.ress.2021.108201 [5] Liu, W., He, Y., Liu, Z., Luo, H., Liu, T., 2023. A bilevel data-driven method for sewer deposit prediction under uncertainty. Water Res. 231, 119588. https://doi.org/10.1016/j.watres.2023.119588 [6] Luo, X., Liu, P., Cheng, Q., Liu, W., Zhou, C., Zheng, Y., Wang, D., Cheng, L., 2024. Reinforcing resilience for integrated design of green and grey infrastructure with real-time control rules by considering system failures. J. Hydrol. 638, 131498. https://doi.org/10.1016/j.jhydrol.2024.131498 [7] Miller, J.D., Hutchins, M., 2017. The impacts of urbanisation and climate change on urban flooding and urban water quality: a review of the evidence concerning the United Kingdom. J. Hydrol.: Reg. Stud. 12, 345–362. https://doi.org/10.1016/j.ejrh.2017.06.006 [8] Mugume, S.N., Butler, D., 2017. Evaluation of functional resilience in urban drainage and flood management systems using a global analysis approach. Urban Water J. 14, 727–736. https://doi.org/10.1080/1573062X.2016.1253754 [9] Mugume, S.N., Gomez, D.E., Fu, G., Farmani, R., Butler, D., 2015. A global analysis approach for investigating structural resilience in urban drainage systems. Water Res. 81, 15–26. https://doi.org/10.1016/j.watres.2015.05.030 [10] Rodríguez, J.P., McIntyre, N., Díaz-Granados, M., Maksimović, Č., 2012. A database and model to support proactive management of sediment-related sewer blockages. Water Res. 46, 4571–4586. https://doi.org/10.1016/j.watres.2012.06.037 [11] Rodríguez, M., Fu, G., Butler, D., Yuan, Z., Cook, L., 2023. Global resilience analysis of combined sewer systems under continuous hydrologic simulation. J. Environ. Manage. 344, 118607. https://doi.org/10.1016/j.jenvman.2023.118607 [12] Rossman, Lewis A.; Huber, Wayne C., 2016. Storm Water Management Model Reference Manual Volume I – Hydrology (Reference Manual No. EPA/600/R-14/334). U.S. Environmental Protection Agency, Washington, DC. [13] Rossman, Lewis A., Simon, Michelle A., 2022. Storm Water Management Model User’s Manual Version 5.2 (User’s Manual). U.S. Environmental Protection Agency. [14] Sharior, S., McDonald, W., Parolari, A.J., 2019. Improved reliability of stormwater detention basin performance through water quality data-informed real-time control. J. Hydrol. 573, 422–431. https://doi.org/10.1016/j.jhydrol.2019.03.012 [15] Shin, S., Lee, G., Ahmed, U., Lee, Y., Na, J., Han, C., 2018. Risk-based underground pipeline safety management considering corrosion effect. J. Hazard. Mater. 342, 279–289. https://doi.org/10.1016/j.jhazmat.2017.08.029 [16] Van Bijnen, M., Korving, H., Clemens, F., 2012. Impact of sewer condition on urban flooding: an uncertainty analysis based on field observations and monte carlo simulations on full hydrodynamic models. Water Sci. Technol. 65, 2219–2227. https://doi.org/10.2166/wst.2012.134 [17] Wang, M., Li, M., Sweetapple, C., 2023. A new framework for distributed storage tanks placement based on a resilience characteristic metric and reduced modelling. J. Environ. Manage. 342, 118098. https://doi.org/10.1016/j.jenvman.2023.118098 [18] Wang, Y., Zhang, Q., Zhang, J., Lin, K., 2025. Impact of 2D and 3D factors on urban flooding: spatial characteristics and interpretable analysis of drivers. Water Res. 280, 123537. https://doi.org/10.1016/j.watres.2025.123537 [19] Xu, K., Zhang, X., Bin, L., Shen, R., 2024. An improved global resilience assessment method for urban drainage systems: a case study of haidian island, south China. J. Environ. Manage. 360, 121135. https://doi.org/10.1016/j.jenvman.2024.121135 [20] Yan, H., Yang, Q., Wang, S., Tian, W., He, Z., Wang, J., Fu, G., Xia, S., Xin, K., Tao, T., 2025. Enhancement of urban drainage system resilience by artificial intelligence: a comprehensive review. ACS ES&T Eng. 5, 2701–2728. https://doi.org/10.1021/acsestengg.5c00700 [21] Yang, J., Zayed, T., Arimiyaw, D., Nashat, M., Liu, X., Ibrahim, A., 2026. Structural failure in urban sewer pipelines: a comprehensive review from analysis to intervention techniques. Tunnelling Underground Space Technol. 167, 107027. https://doi.org/10.1016/j.tust.2025.107027 [22] Zhou, Q., Leng, G., Su, J., Ren, Y., 2019. Comparison of urbanization and climate change impacts on urban flood volumes: importance of urban planning and drainage adaptation. Sci. Total Environ. 658, 24–33. https://doi.org/10.1016/j.scitotenv.2018.12.184