Many resilience surrogate measures have been established to evaluate the performance of water distribution systems (WDSs) over the past three decades. However, most of those measures have neglected the nexus between network layout performance and the essential features of system resilience. In this paper, we proposed two novel surrogate measures by introducing node degree and pipe diameter uniformity within the extended nodal neighborhood in a rational, mathematical structure. Then, a comparison framework of surrogate measures was developed, which included a multi‐pipe failure scenario generator and a dual‐perspective evaluation method. New surrogate measures were benchmarked with well‐known counterparts on four networks with different numbers of sources and loop degrees. Results revealed that new surrogate measures outperformed existing ones clearly with a marginal computational budget increase. Solutions derived from the proposed surrogate measures strengthened system resilience by increasing diameter uniformities from sources to critical nodes. Additionally, new surrogate measures showed fewer performance fluctuations. This study provides insights into how to maintain more resilient WDSs.

Various reliability surrogate measures have emerged over the last three decades to design water distribution systems. However, existing comparative studies cannot assess surrogate measures from the resilience perspective considering the dynamic absorption–recovery process imposed by pipe failures. In this work, we propose a novel benchmark framework based on the global resilience analysis to examine surrogate measures’ performance. Surrogate measures were compared via the stress–strain curve derived from the global resilience analysis under extended period simulation. In particular, we identify the comparable stress range to articulate the differences among surrogate measures and significantly reduce the computational burden. Then, we develop the normalized resilience score (NRS) to evaluate the quality of solutions to network design. Five well-known measures are compared for the multiobjective design of two benchmark networks. Results show that the Network Resilience Index achieves 2.5% to 10.1% better NRSs than the mean NRSs over five surrogate measures, implying that both nodal surplus energy and pipe diameter uniformity greatly impact the network system’s resilience. The uniformity of pipe diameters is more significant than the uniformity of flow rate. Our findings contribute to the design of new and better surrogate measures for network resilience evaluation.

Various reliability surrogate measures (RSMs) have emerged over last three decades to design water distribution systems (WDSs). However, there has yet to be a consensus on their rationalities. In this work, we proposed a novel dual-perspective framework to examine RSMs’ rationalities, including the equal-cost perspective (ECP) by comparing RSMs’ performance at three comparable equal-cost levels and the traditional consistency perspective (TCP) widely used in other comparative studies. RSMs’ performance was evaluated by the mechanical reliability score (MRS) and the normalized resilience score (NRS). The former compared fewer pipe failures under a snapshot scenario, while the latter considered more comprehensive pipe failures using global resilience analysis. We compared five well-known RSMs from the literature, i.e., Network Resilience Index (NRI), Diameter-Sensitive Flow Entropy (DSFE), Redundancy (REDU), Pipe Hydraulic Resilience Index (PHRI) and Combined Entropy-Resilience Index (CERI). We verified their performance for the multi-objective design of two medium-scale, single-source WDSs, i.e., Hanoi and Fossolo. Results reveal that NRI is the most rational measure. The rankings from NRS are similar to those from MRS under the ECP; however, the two rankings vary significantly under the TCP. The ECP is more reasonable than the TCP because it measures network resilience and reliability from practical performance. We recommend using the MRS+ECP framework to compare RSMs since it reflects the critical aspect of global resilience with lower computational overhead. Furthermore, some meta information, e.g., nodal pressure head and demand, has the most significant impact on the rankings of RSMs, inspiring the development of new and better RSMs.