186 Chapter 8. Discussion 8.5 References Auger, S., J.-B. Besnier, M. van Bijnen, F. Cherqui, G. Chuzeville, F. Clemens-Meyer, M. G. Jaatun, J. Langeveld, Y. Le Gat, S. Moin, G. E. Oosterom, W. van Riel, B. Roghani, M. M. Rokstad, J. Røstum, F. Tscheikner-Gratl, and R. Ugarelli (June 2024). “Data management and quality control”. In: Asset Management of Urban Drainage Systems: If anything exciting happens, we’ve done it wrong! IWA Publishing. isbn: 9781789063059. doi: 10.2166/9781789063059_0299. Duchesne, S., G. Beardsell, J.-P. Villeneuve, B. Toumbou, and K. Bouchard (2013). “A survival analysis model for sewer pipe structural deterioration”. In: ComputerAided Civil and Infrastructure Engineering 28.2, pp. 146–160. doi: 10.1111/j.14678667.2012.00773.x. Li, C., P. Zheng, Y. Yin, B. Wang, and L. Wang (2023). “Deep reinforcement learning in smart manufacturing: A review and prospects”. In: CIRP Journal of Manufacturing Science and Technology 40, pp. 75–101. issn: 1755-5817. doi: https://doi.org/10. 1016/j.cirpj.2022.11.003. Linard, A., D. Bucur, and M. Stoelinga (2019). “Fault Trees from Data: E’cient Learning with an Evolutionary Algorithm”. In: International Symposium on Dependable Software Engineering: Theories, Tools, and Applications. Vol. 11951 LNCS, pp. 19–37. doi: 10.1007/978-3-030-35540-1_2. Marugán, A. P. (2023). “Applications of Reinforcement Learning for maintenance of engineering systems: A review”. In: Advances in Engineering Software 183, p. 103487. issn: 0965-9978. doi: 10.1016/j.advengsoft.2023.103487. Real Torres, A. del, D. S. Andreiana, Á. Ojeda Roldán, A. Hernández Bustos, and L. E. Acevedo Galicia (2022). “A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework”. In: Applied Sciences 12.23. issn: 2076-3417. doi: 10.3390/app122312377. Scheidegger, A., T. Hug, J. Rieckermann, and M. Maurer (2011). “Network condition simulator for benchmarking sewer deterioration models”. In: Water Research 45.16, pp. 4983–4994. issn: 0043-1354. doi: 10.1016/j.watres.2011.07.008. Siraskar, R., S. Kumar, S. Patil, A. Bongale, and K. Kotecha (2023). “Reinforcement learning for predictive maintenance: A systematic technical review”. In: Artificial Intelligence Review 56.11, pp. 12885–12947. doi: 10.1007/s10462-023-10468-6. Timashev, S. and A. Bushinskaya (2015). “Markov approach to early diagnostics, reliability assessment, residual life and optimal maintenance of pipeline systems”. In: Structural Safety 56, pp. 68–79. issn: 0167-4730. doi: 10.1016/j.strusafe.2015.05.006.
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