12 Chapter 1. Introduction (a) (b) Figure 1.9: Sewer main system and damage severities. (a) Breda’s sewer network, The Netherlands (View of Breda 2024). (b) Cracks of di!erent severity levels (indicated with k) taken from the (EN 13508:1). models is particularly challenging. The complexity arises from several factors: the inability to conduct run-to-failure experiments due to the decades-long evolution of various deterioration modes; the vast scale and complexity of sewer systems combined with limited knowledge of the various failure mechanisms (Barraud, Bosco, Clemens-Meyer, et al., 2024); and the lack of reliable, high-quality data. This data scarcity is aggravated by the “data-loop problem”, where insu"cient data makes it di"cult to demonstrate its value, which in turn remains di"cult to obtain without adequate data (Cherqui, Clemens-Meyer, Tscheikner-Gratl, et al., 2024). Furthermore, this lack of reliable data introduces various types of biases, a topic discussed in detail in Auger, Besnier, Bijnen, et al., 2024. Current attempts to model the deterioration of sewer mains are classified into physics-based, Machine Learning-based, and probabilistic models (Hawari, Alkadour, Elmasry, et al., 2020; Saddiqi, Zhao, Cotterill, et al., 2023). However, the literature does not conclusively determine which type of model is best for sewer main deterioration modelling (Zeng, Z. Wang, H. Wang, et al., 2023). 1.2.4 Maintenance Optimisation According to ISO 14224:2016 and CEN-EN:13306, maintenance is “the mix of all technical, administrative, and managerial actions, aimed at retaining or restoring an item to a state in which it can perform as required”. Discussed since the 1960s, Maintenance Optimisation (MO) is the systematic improvement of maintenance activities, focusing on answering when and what to maintain (Arts, Boute, Loeys, et al., 2024). MO involves developing and analysing mathematical models to derive maintenance strategies (de Jonge and Scarf, 2020),
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