Exploiting Digitalization to Determine Optimal Intervention Programs for Water Distribution Networks

Modern societies are heavily reliant on the service provided by water distribution networks. Despite the critical importance of this infrastructure, water utilities are facing major financial challenges worldwide and are raising water fees in response. The willingness of stakeholders to bear this growing financial burden is low. Without clear and convincing argumentation, future water fee hikes will become increasingly difficult to obtain. The infrastructure asset management process is a well-suited tool for providing such argumentation. This process is a systematic approach to ensure that infrastructure continues to provide an adequate level of service during all lifecycle phases (i.e. construction, operation, maintenance, expansion, and demolition). Over the last several decades, digitalization has permeated all activities related to infrastructure asset management and led to tangible benefits such as the improved design, efficient operation and timely maintenance of water distribution networks. However, compared to other sectors of the economy, digitalization in the infrastructure sector is lagging. Concrete examples are needed to nudge infrastructure managers towards further embracing digitalization in all aspects of the infrastructure asset management process. This thesis aims to fulfil this need by providing specific examples of how digitalization dissolves data silos within utilities and between them and enables the adoption of efficient algorithms to tangibly improve asset management of water distribution networks. The contents of the thesis is divided into three main parts. The main themes of the three parts are pipe failure prediction with artificial neural networks (ANNs), optimal intervention planning for water distribution networks, and optimizing the execution of operational maintenance programs. Part I consists of Chapter 2. Part II consists of Chapters 3-6. Part III consists of Chapter 7. This work contributes to the advancement of infrastructure asset management of water distribution networks in several ways. Firstly, a study examines how a pipe failure prediction methodology based on ANNs can be used to make short-term failure predictions on all pipe materials by combining soft deterioration data from an expert elicited survey with hard deterioration data from recorded failures. Secondly, a methodology is presented for determining optimal short-term intervention programs on large, real-world water distribution networks that considers all network components requiring preventive intervention planning. Several aspects of the novel methodology are explored in additional chapters. These include the use of cost-benefit analysis in quantitatively comparing interventions on different object types, the influence of economies of scale on pipe replacement estimates, and the simplifications used to model large, real-world networks. Finally, a study is provided exploring the influence of maintenance density and routing algorithm on the efficiency gains that can be achieved by integrating municipal maintenance tasks such as hydrant and valve inspections and meter replacements into combined work packages. The most substantial contribution of this thesis is found in Part II on optimal intervention planning. The increasing demands of stakeholders for decision transparency, resource efficiency and sustainability will continue to serve as catalysts for the development and professionalization of infrastructure asset management. The untapped potential of digitalization represents a clear path forward to achieving these ambitious goals. Ultimately, digitalization will not only transform decision-making but will also fundamentally reshape the organizational structures within utilities as well as the water sector itself.
ISBN:978-3-907234-37-2