Condition assessment for water distribution pipelines using inverse transient analysis and the reconstructive method of characteristics

Modern civilisations rely on water distribution systems to deliver water resources to domestic and industrial consumers. During the lifespan of pipeline assets, they naturally deteriorate due to a combination factors such as ground movement, fatigue, high stresses, and external or internal corrosion. The gradual deterioration of pipelines may lead to the failure of pipelines which may have severe consequences in terms of water resource loss, disruption to industry, traffic and the wider community, repair costs and compensation claims. Developing an efficient and reliable pipeline condition assessment approach is essential to decision-making involving inspection, rehabilitation and replacement. Many existing methods can only investigate pipeline condition over a limited range, which makes them slow and expensive. Fluid transient-based methods can cover several kilometres of pipeline using a few seconds of transient-based test data, due to the fast wave propagation speed. In addition, a transient event can be generated and measured at existing access points along pipelines (for example, air valves or fire hydrants), so cutting the pipeline open and/or draining out the water from the pipeline is not required. Overall, fluid transient-based methods are cost-effective and non-invasive, which make them a promising tool for the future. To achieve the goal of continuous condition assessment for water distribution pipelines, this research focuses on the Inverse Transient Analysis (ITA) method and the previously developed Reconstructive Method of Characteristics (RMOC). The research proposes a faster and improved ITA approach by incorporating a new Head Based Method of Characteristics (HBMOC) and a flexible grid, which enhances the computational efficiency and avoids the need for incorporation of interpolation schemes such as those used in the traditional MOC approach. This efficient ITA approach is then developed into the multi-stage parameter-constraining inverse transient analysis (ITAMP) [MP subscript] by iteratively limiting the search-space, to overcome problem of lack of identifiability when inverse problems involve hundreds of decision variables. The previously developed RMOC for pipeline condition assessment requires a dead-end boundary and an access point immediately upstream of the dead-end boundary, which is difficult to achieve in the field. The RMOC is significantly generalised in this thesis by relaxing this requirement. The new generalised RMOC utilises two pressure transducers placed at any two interior points along a pipeline to achieve pipeline condition assessment. The number and location of pressure transducers required to achieve optimum identifiability are also investigated. It has been demonstrated by the generalised RMOC that if the pipeline condition between the two pressure transducers is unknown, pressure measurements by two transducers are not able to uniquely identify the wave speed distribution along a pipeline using transient-based methods. To improve identifiability, given that the first two sensors are N reaches apart (i.e. N pipe segments in the pipeline model), the third sensor should not be placed at nodes that are separated from any of the first two sensors by an integer multiple of N reaches. The generalised RMOC also provides insight into why general ITA methods struggle to find good solutions as it illustrates that an infinite number of plausible solutions are possible for the almost same pressure trace if the wave speed values between transducers are allowed to vary and a third sensor is placed at an integer multiple location. The verification of ITAMP [MP subscript] and generalised RMOC by a field and a laboratory experiment, respectively, demonstrates that methods developed in this research can serve as a valuable screening tool for pipeline condition assessment in the real world.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201