150 Part III: Maintenance optimisation of multi-state components F. Taillandier, S. M. E. and A. Bennabi (2020). “A decision-support framework to manage a sewer system considering uncertainties”. In: Urban Water Journal 17.4, pp. 344–355. doi: 10.1080/1573062X.2020.1781908. Fang, X., W. Guo, Q. Li, J. Zhu, Z. Chen, J. Yu, B. Zhou, and H. Yang (2020). “Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences”. In: IEEE Access 8, pp. 39574–39586. doi: 10.1109/ACCESS.2020.2975887. Fenner, R. (2000). “Approaches to sewer maintenance: a review”. In: Urban Water 2.4. Sewer Systems and Processes, pp. 343–356. issn: 1462-0758. doi: https://doi.org/ 10.1016/S1462-0758(00)00065-0. Fenner, R. A., L. Sweeting, and M. J. Marriott (2000). “A new approach for directing proactive sewer maintenance”. In: Proceedings of the Institution of Civil EngineersWater and Maritime Engineering. Vol. 142. 2. Thomas Telford Ltd, pp. 67–77. doi: 10.1680/wame.2000.142.2.67. Fontecha, J. E., R. Akhavan-Tabatabaei, D. Duque, A. L. Medaglia, M. N. Torres, and J. P. Rodríguez (Mar. 2016). “On the preventive management of sediment-related sewer blockages: a combined maintenance and routing optimization approach”. In: Water Science and Technology 74.2, pp. 302–308. issn: 0273-1223. doi: 10.2166/wst.2016.160. Fuchs-Hanusch, D., M. Günther, M. Möderl, and D. Muschalla (June 2015). “Cause and e!ect oriented sewer degradation evaluation to support scheduled inspection planning”. In: Water Science and Technology 72.7, pp. 1176–1183. issn: 0273-1223. doi: 10.2166/wst.2015.320. Gueye, T., Y. Wang, and R. T. Mushtaq (2023). “Concrete deterioration detection in sewers using machine learning algorithms: an experiment-based study”. In: International Journal of Information Technology 15.4, pp. 1949–1959. doi: 10.1007/s41870-02301231-9. Hallak, A., D. Di Castro, and S. Mannor (2015). “Contextual Markov Decision Processes”. In: arXiv preprint arXiv:1502.02259. doi: 10.48550/arXiv.1502.02259. Hamid Zaman Ahmed Bouferguene, M. A.-H. and C. Lorentz (2017). “Improving the productivity of drainage operations activities through schedule optimisation”. In: Urban Water Journal 14.3, pp. 298–306. doi: 10.1080/1573062X.2015.1112409. Hernández, N., N. Caradot, H. Sonnenberg, P. Rouault, and A. Torres (2021). “Optimizing SVM models as predicting tools for sewer pipes conditions in the two main cities in Colombia for di!erent sewer asset management purposes”. In: Structure and Infrastructure Engineering 17.2, pp. 156–169. doi: 10.1080/15732479.2020.1733029. Inanloo, B., B. Tansel, K. Shams, X. Jin, and A. Gan (2016). “A decision aid GIS-based risk assessment and vulnerability analysis approach for transportation and pipeline networks”. In: Safety Science 84, pp. 57–66. issn: 0925-7535. doi: 10.1016/j.ssci. 2015.11.018. Jeung, M., J. Jang, K. Yoon, and S.-S. Baek (2023). “Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning”. In: Journal of Hydrology 624, p. 129973. issn: 0022-1694. doi: https://doi.org/10.1016/ j.jhydrol.2023.129973. João A. Zeferino, A. P. A. and M. C. Cunha (2010). “Multi-objective model for regional wastewater systems planning”. In: Civil Engineering and Environmental Systems 27.2, pp. 95–106. doi: 10.1080/09540250802658988. Kerkkamp, D., Z. Bukhsh, Y. Zhang, and N. Jansen (2022). “Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks”. English. In: Proceeding of the 14th International Conference on Agents and Artificial Intelligence. Vol. 2, pp. 574–585. doi: 10.5220/0000155600003116. Khurelbaatar, G., B. Al Marzuqi, M. Van A!erden, R. A. Müller, and J. Friesen (2021). “Data reduced method for cost comparison of wastewater management scenarios–case
RkJQdWJsaXNoZXIy MjY0ODMw