Jinliang Gao

The detection and spatial assessment of buried water pipeline leaks are critical for preventing resource loss and geohazards such as soil erosion and subsidence. This paper presents an initial experimental demonstration of a microwave imaging system designed for mapping subsurface anomalies induced by buried water pipeline leaks. The proposed system integrates a custom-designed broadband open-sleeve dipole antenna with a vector network analyzer, power amplifier, and a precision linear motion stage that emulates synthetic aperture radar scanning for controlled measurement validation. The antenna achieves a 66.7% impedance bandwidth (1∼2 GHz) and a stable gain of approximately 8 dBi. A phase-compensated back-projection algorithm is employed to generate two-dimensional ground-projected scattering maps. Controlled experiments involving wet-sand regions, surface potholes, and subsurface cavity surrogates yield averaged within-image response differences of 2.1∼5.2 dB between anomaly-associated regions and ground background regions. Under the specified acquisition conditions, the theoretical azimuth and range resolutions are 0.28 m and 0.15 m, respectively. These results provide preliminary evidence of imaging feasibility under the tested surrogate-target conditions. Further validation with actual leakage and representative soil and surface-cover conditions is required to assess applicability to infrastructure monitoring.

Graphical abstract summarising the DWEBC workflow: a WDN is converted to a weighted graph; EPANET hydraulic outputs and demand data are used to compute DWEBC; and high-ranking pipes support resilience screening, maintenance prioritisation, and digital-twin decision-making. Water distribution networks (WDNs) support urban resilience by delivering potable water under highly variable demands and hydraulic conditions. Traditional graph-theoretic centrality measures identify critical pipes based solely on topology, overlooking the spatial heterogeneity of nodal demands that drives service loss priorities. This study proposes demand-weighted edge betweenness centrality (DWEBC), a hydraulically informed extension of EBC in which pipe length, head loss, and target-node demand are embedded into the edge-weight function, thereby aligning shortest-path computations with the hydraulic relevance of the paths. Three weighting schemes – global constant, time-varying nodal, and time-varying nodal and pipe-specific scaling – are formulated. The algorithm is demonstrated on a small laboratory-scale Kaina network, Exnet, d-town, and d-town benchmark networks and validated on Net3, where DWEBC, in particular the time-varying nodal and pipe-specific variant, distinctly ranks pipes that serve major demand corridors. This offers a rapid screening tool for maintenance prioritisation, emergency and capital planning, and resilience assessment. Computational performance outruns several approaches by orders of magnitude, enabling routine re-analysis under shifting demand scenarios. It can be easily scaled, and it can make use of real-time flow telemetry to enable dynamic and hybrid frameworks, such as Digital Twin, to enable real-time and realistic network performance analysis and control.

Schematic of the study framework showing flow decomposition and discharge testing for customer-side leakage detection in DMAs. Customer-side leakage, a component of background leakage in water distribution systems, is commonly characterized through inlet flow signal decomposition and mitigated through pressure management. However, decomposition-based methods often lack physical grounding, and the direct identification and spatial localization of such subtle leakages remain insufficiently explored. This study proposes an integrated framework that combines signal decomposition with controlled field testing to enable precise detection of customer-side leakage within district metered areas. High-resolution smart meter data support refined hydraulic simulation and flow signal analysis. Controlled discharge experiments are conducted to induce representative leakage conditions, from which characteristic leakage signatures can be extracted from inlet flow series using variational mode decomposition with experimental support. The decomposed components are compared with consumption signals from all individual household meters via dynamic time warping to quantify similarity and locate leakage sources. The proposed framework is applied to a real district metered area in Danzhou, China, where minor customer-side leakages at household meter connections are successfully identified and verified through field inspection. The results demonstrate that integrating data-driven mode decomposition with experimental calibration provides a robust and transferable approach for proactive customer-side leakage management.

Dead‐end pipes in water distribution networks are prone to water quality deterioration due to prolonged stagnation, and fluctuating hydraulic conditions can remobilize accumulated contaminants into the main pipe, threatening downstream water quality. However, the mechanisms and concentration dynamics of pollutant migration in such pipes remain insufficiently understood. This study elucidated solute migration mechanisms and developed an exponential solute decay model for dead‐end pipes through the integration of full‐scale experiments and computational fluid dynamics (CFD) simulations. Velocity and concentration distributions across multiple cross‐sections were analyzed, and the mass exchange coefficient was expressed as a function of geometric and hydraulic parameters. Results show that shear forces at the junction between the main and dead‐end pipes induce a persistent clockwise vortex on the vertical cross‐section, continuously transporting solutes into the main pipe. The interior of dead‐end pipes is not fully stagnant but contains weak recirculating flow, with the longitudinal velocity profile exhibiting a distinct three‐stage pattern. Although forward velocities in the dead‐end pipe are less than 10% of those in the main pipe, advective transport still plays a major role in mass transfer within the velocity disturbance region and the exponential decay region of the dead‐end pipe. The proposed decay model accurately captures temporal solute concentration variations, with a relative error below 0.6%. This model provides a robust basis for water quality assessment at dead‐end junctions and offers practical guidance for predicting water quality responses to sudden pollutant intrusions.

The figure shows the trunk preserving demand weighted edge betweenness centrality procedure. Starting with DWEBC computation, then identifying the trunk network, growing DMAs on the residual network, and concluding with evaluating the resulting DMAs using suggested pressure and head loss metrics to select a preferred configuration. District metered areas (DMAs) play a crucial role in managing leakages and pressure, and monitoring operations within water distribution networks (WDNs). However, purely topological design of DMAs overlooks hydraulics and the operational role of highly critical pipes, undermining redundancy and resilience. This study proposes a trunk-preserving hydraulically informed partitioning framework that (i) utilises demand weighted edge betweenness centrality (DWEBC) to delineate a trunk network (TN) and (ii) grows capacitated, size-balanced DMAs on the residual network using an inverse of the DWEBC as edge weight. The approach employs farthest-first k-centre seeding, graph-Voronoi growth, Kernighan–Lin/Fiduccia–Mattheyses connectivity repair, uniformity balancing, and minimum DMA size enforcement. The DMAs are then evaluated using a pressure uniformity index (PUI), head-loss gradient (HLG), demand satisfaction ratio (DSR), and boundary control complexity (BCC) metrics, after which collector edges interfacing TN and DMAs are flagged. The approach is deterministic, bounded in runtime, and implemented in Python using EPANET/WNTR. Results on the benchmark networks demonstrate easy identification of TN, formation of balanced DMA in terms of size and pressure, and interpretable interfaces for metering and control. The framework generalises to real-world WDNs and can seed multi-objective refinement if Pareto exploration of PUI–HLG–DSR–BCC is desired.

This paper addresses the critical aspect of water conservation in public buildings within the context of sustainable urban water resources management. While conventional approaches rely on pricing controls and water‐saving appliances, this research emphasizes the key consideration of psychological factors influencing users' willingness to conserve water. Through a survey involving 1,206 volunteers, an expanded theory of planned behavior model is constructed and analyzed to evaluate the impact of water‐saving attitudes, subjective norms, self‐efficacy, and perceived behavioral control on individuals' water‐saving behavior. Intervention experiments conducted in three types of public buildings equipped with smart water meters unveil the nuanced dynamics of real‐time water‐saving behavior and its responsiveness to external interventions. Findings underscore the collective influence of subjective norms, water‐saving attitudes, self‐efficacy, and perceived behavioral control on individuals' intentions and behaviors. Noteworthy is the observed time lag and diminishing impact of external interventions, where economic, feedback, and subjective norms interventions prove more effective. This study not only contributes a theoretical framework but also provides practical insights, emphasizing the need for consistent and targeted external interventions. Practitioners, decision‐makers, and stakeholders are urged to recognize the profound impact of users' psychological factors on public water‐saving behavior and strategically employ interventions for sustained positive outcomes.

Urban water distribution systems face growing challenges from energy inefficiencies caused by hydraulic anomalies, such as pipe aging, bursts, demand variability, and suboptimal pump and valve operations. This review systematically evaluates current technologies for energy-efficient management of WDNs under such conditions, structured around both basic and applied technologies. Basic technologies include real-time monitoring, data acquisition, and hydraulic modeling with CFD simulation. Applied technologies focus on demand forecasting, pressure management for energy optimization, and leakage anomaly detection. Case studies demonstrate the practical value of these approaches. Despite recent advances, challenges persist in data interoperability, real-time optimization complexity, scalability, and forecasting uncertainty. Future research should emphasize adaptive AI algorithms, integration of digital twin platforms with control systems, hybrid optimization frameworks, and renewable energy recovery technologies. This review provides a comprehensive foundation for the development of intelligent, energy-efficient, and resilient urban water distribution systems through integrated, data-driven management strategies.

In this study, the efficacy of varying spoiler design parameters on regulating dead-end contaminant diffusion was investigated using particle image velocimetry (PIV) technology. The optimal spoiler design parameters were identified under low Reynolds number turbulence, including variables such as tilt angle, height, width, and installation position. The findings of this study present a novel approach for improving water quality in WDNs by installing spoilers in dead-ends while offering valuable insights and providing guidance for the design and optimization of these spoilers.