A rapidly changing digital landscape is shifting government-owned infrastructure utility organizations toward digital transformation. This literature review aims to consider how the characteristics of infrastructure organizations, in particular water organizations, might influence their digital transformation, understand the issues involved in that transformation, and identify the factors that could support digitization pathways for these organizations. The research found that changing technologies, social behaviors, and expectations around digital transformation will continue to push water organizations toward digital transformation. Changing regulatory requirements and a greater focus on increasing efficiency and improving customer relationships will also drive this transformation. A corporatewide focus on digital governance, culture, skills, and knowledge, coupled with “single point of truth” data management, will enable operations, customer and community relations, and smart systems to achieve economic and performance efficiencies, improved customer satisfaction, and better regulatory compliance. Dynamic systems and network modeling may enable government-owned infrastructure organizations to understand their complex nature, internal and external relationships, interdependencies, and current digital maturity. The digital transformation of these organizations will require careful, long-term strategic and organizational planning and commitment.

Hydraulic transients (water hammer waves) can be used to excite a pressurized pipeline, yielding the frequency response diagram (FRD) of the system. The FRD of a pipeline system is useful for condition assessment and fault detection, because it is closely related to the physical properties of the pipeline. Most previous FRD-based leak detection techniques use the sinusoidal leak-induced pattern recorded on the FRD, either shown on the resonant responses or the antiresonant responses. In contrast, the technique reported in the current paper only uses the responses at the first three resonant frequencies to determine the location and size of a leak. The bandwidth of the excitation only needs to be five times that of the fundamental frequency of the tested pipeline, which is much less than the requirement in conventional FRD-based techniques. Sensitivity analysis and numerical simulations are performed to assess the robustness and applicable range of the proposed leak location technique. The proposed...

Water main breaks in city areas can cause significant economic loss, interrupt traffic, and result in negative publicity for utilities. The South Australian Water Corporation (SA Water) has taken a proactive approach to reduce water main breaks in the Adelaide, Australia, central business district (CBD) by detecting developing pipe cracks/leaks using distributed acoustic sensors (accelerometers) with customized analytics and internet of things (IoT) technologies. This paper reports the findings and the outcomes of this initiative 24 months after the first commissioning of the continuous leak detection system. The acoustic data analytics team (comprising the authors) has successfully interpreted acoustic data from numerous cracked pipes in the Adelaide CBD, with approximately 55% of all developing leaks associated with cracked pipes being able to be detected before uncontrolled failure (i.e., before unplanned interruption of customers and/or third party economic damage). Furthermore, the localization and repair of specific cracks (sometimes through sections of corroded cast iron pipe walls) can help justify extending the service life of the water main as points of weakness (faults) are specifically rectified. However, only 45% of the developing cracks have been able to be repaired, after detection, under controlled conditions (i.e., before the planned interruption of customers) due to practical limitations affecting operational responses. The results have confirmed the additional insight that can be obtained by continuously collecting acoustic data in terms of distinguishing environmental and crack noises as well as circumferential from longitudinal cracks.

Jinzhe Gong, Martin F. Lambert, Angus R. Simpson, and Aaron C. Zecchi