Binding and distribution regularity of water molecules in high-moisture textured Antarctic krill (Euphausia superba) meat.

High-moisture extrusion technique with the advantage of high efficiency and low energy consumption is a promising strategy for processing Antarctic krill meat. Consequently, this study aimed to prepare high-moisture textured Antarctic krill meat (HMTAKM) with a rich fiber structure at different water contents (53 %, 57 %, and 61 %) and to reveal the binding and distribution regularity of water molecules, which is closely related to the fiber structure of HMTAKM and has been less studied. The hydrogen-bond network results indicated the presence of at least two or more types of water molecules with different hydrogen bonds. Increasing the water content of HMTAKM promoted the formation of hydrogen bonds between the water molecules and protein molecules, leading to the transition of the ß-sheet to the α-helix. These findings offer a novel viable processing technique for Antarctic krill and a new understanding of the fiber formation of high-moisture textured proteins.

Effects of pasteurization temperature and amino acids on the gelation behavior of liquid egg yolk: Emphasizing rheology, gel properties, intermolecular forces and microstructure.

Pipeline blockage caused by liquid egg yolk (LEY) in the pasteurization process has become an urgent problem for egg industry. This study investigated the effects of amino acids (betaine/proline) on rheology of LEY and gel property of egg yolk gel (EYG) at various pasteurization temperatures (68, 72, and 76 °C). Rheological results revealed that 72 °C was the key transition point for increase in LEY thermal aggregation rate. Average particle size of EYG, BEYG and PEYG increased by 63.9 %, 27.3 % and 17.3 % with increasing pasteurization temperature. Amino acids promoted increase in disulfide bonding content and facilitated retention of free and bound water within gels. Moreover, amino acids enhanced crystallinity and order of gel structures. Amino acids can effectively mitigate thermal aggregation of LEY at mild temperatures and promote cross-linking of gel network at high temperatures. This study provides a theoretical foundation for heat resistance of LEY and application of EYG.

Pipe material and natural organic matter impact drinking water biofilm microbial community, pathogen profiles and antibiotic resistome deciphered by metagenomics assembly.

Biofilms in drinking water distribution systems (DWDSs) are a determinant to drinking water biosafety. Yet, how and why pipe material and natural organic matter (NOM) affect biofilm microbial community, pathogen composition and antibiotic resistome remain unclear. We characterized the biofilms' activity, microbial community, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs) and pathogenic ARG hosts in Centers for Disease Control and Prevention (CDC) reactors with different NOM dosages and pipe materials based on metagenomics assembly. Biofilms in cast iron (CI) pipes exhibited higher activity than those in polyethylene (PE) pipes. NOM addition significantly decreased biofilm activity in CI pipes but increased it in PE pipes. Pipe material exerted more profound effects on microbial community structure than NOM. Azospira was significantly enriched in CI pipes and Sphingopyxis was selected in PE pipes, while pathogen (Ralstonia pickettii) increased considerably in NOM-added reactors. Microbial community network in CI pipes showed more edges (CI 13520, PE 7841) and positive correlation proportions (CI 72.35%, PE 61.69%) than those in PE pipes. Stochastic processes drove assembly of both microbial community and antibiotic resistome in DWDS biofilms based on neutral community model. Bacitracin, fosmidomycin and multidrug ARGs were predominant in both PE and CI pipes. Both pipe materials and NOM regulated the biofilm antibiotic resistome. Plasmid was the major MGE co-existing with ARGs, facilitating ARG horizontal transfer. Pathogens (Achromobacter xylosoxidans and Ralstonia pickettii) carried multiple ARGs (qacEdelta1, OXA-22 and aadA) and MGEs (integrase, plasmid and transposase), which deserved more attention. Microbial community contributed more to ARG change than MGEs. Structure equation model (SEM) demonstrated that turbidity and ammonia affected ARGs by directly mediating Shannon diversity and MGEs. These findings might provide a technical guidance for controlling pathogens and ARGs from the point of pipe material and NOM in drinking water.

Reshaping water distribution topology from the carbon emission reduction perspective: An exploratory analysis of water distribution reliability.

As urban economies continue to evolve, the water distribution networks (WDNs) are expanding in scale and becoming more interconnected, leading to increased carbon emissions from operations and maintenance. Consequently, enhancing the stability and safety of WDNs while saving energy has emerged as a primary research focus. This study abandoned the original use of high economic costs for post-maintenance of WDNs. Instead, it reshaped the traditional water distribution topology to form a dynamic, storable, energy-efficient "WDN self-help" model. Drawing inspiration from the "deep tunnel" project in drainage systems, the proposal was to leverage underground spaces to create a deep aqueduct (DA) complementing the traditional WDN, forming a three-dimensional (3D) WDN. Hydraulic and water quality analyses of varying scales of the 3D WDN model demonstrated its superior ability to equalize node pressures, reduce pipeline head losses, and maintain water quality for end-users. Reliability assessments of the 3D WDN revealed enhanced system robustness for medium-to large-scale distributions, while energy consumption analyses indicated a significant increase in water supply energy utilization and significant long-term reductions in carbon footprint. A practical case study was presented to validate the effectiveness of the 3D WDN concept, confirming its ability to reliably distribute water even in the event of a failure. Finally, an estimate of the retrofit cost and the static payback period of the 3D WDN was conducted. This study aims to provide a theoretical reference for the renovation of water supply projects or the optimal design of new WDNs in the context of carbon neutrality.

An all-purpose method for optimal pressure sensor placement in water distribution networks based on graph signal analysis.

Many researchers have addressed the challenge of optimal pressure sensor placement for different purposes, such as leakage detection, model calibration, state estimation, etc. However, pressure data often need to serve multiple purposes, and a method to optimize sensor locations with versatility for various objectives is still lacking. In this paper, a graph-based optimal sensor placement (GOSP) framework is proposed, which aims to provide a robust and all-purpose approach to identify critical points for pressure monitoring. By analysing the spatial variation frequencies of WDN pressures, the relationship between measurements and the global variation of original pressures is established. On this basis, the D-optimality criterion is adopted to formulate the objective of GOSP, which aims to maximize the information on the spatial distribution of pressures that can be obtained from measurements. The new-proposed objective ensures that the sensor locations are compatible with various application scenarios. The proposed method was applied to a real-life distribution network, and was compared with other optimal sensor placement methods oriented towards burst detection and pipe roughness calibration. Based on comparative studies in different scenarios including unknown pressure estimation, burst detection, and model calibration, the effectiveness and robustness of the proposed method have been proved.

Addressing data limitations in leakage detection of water distribution systems: Data creation, data requirement reduction, and knowledge transfer.

Leakage in water distribution systems is a significant problem worldwide, leading to wastage of water resources, compromised water quality and excess energy consumption. Leakage detection is essential to reduce the duration of leaks and data-driven methods are increasingly being used for this purpose. However, these models are data hungry and available observed data, especially leakage data, is limited in most cases. In addition, these data need to be manually processed to label whether leaks occur, which is time-consuming and costly. These are significant obstacles for the development and application of these methods. This article provides a comprehensive review of relevant journal papers, categorizing all data-driven methods into unsupervised anomaly detection, semi-supervised anomaly detection and supervised classification methods based on how the data are utilized for developing these methods. In addition, strategies to address data limitations are summarized from both data and model perspectives, including data creation, reduction of a model's data requirements and knowledge transfer. After detailing these strategies, research gaps are identified. Based on these, future research directions are suggested, highlighting the need for further research in data augmentation, development of semi-supervised classification methods, exploration of multi-classification methods with model updating mechanisms, and development of novel knowledge transfer methods.

Persistent free radicals in natural organic matter activated by iron particles enhanced disinfection byproduct formation.

The widespread presence of iron (Fe) particles and natural organic matter (NOM) in drinking water distribution systems (DWDS) can significantly affect tap water quality, contributing to aesthetic issues and potentially generating harmful disinfection byproducts (DBPs). This study revealed that Fe particles, when combined with humic acid (HA), substantially increased DBP formation during chlorination. Fe particles (particularly preformed Fe particles) significantly increased haloacetic acid (HAA) formation by activating the persistent free radicals (PFRs) in the HA. Compared with the control system without Fe particles, greater than 2 times of HAA increase were observed for the system with Fe pariticles. PFRs accumulated on Fe particle surface could generate hydroxyl radicals, facilitating the decomposition of HA into smaller molecules, which were more reactive with chlorine disinfectants, thus elevated the DBP formation including both known and unknown N-DBPs and Cl-DBPs. The DBP promotion effect of in-situ formed Fe particles was much less than that of preformed Fe particles although both in-situ formed and preformed Fe particles could accumulate PFRs from HA. In-situ formed particles primarily accumulated carbon-centered PFRs, while preformed particles accumulated oxygen-centered PFRs. To mitigate the Fe particle induced water quality risks, it is crucial to control iron pipe corrosion and iron release in DWDS. In addtion, the optimization of treatment processes such as coagulation and filtration to more completely remove NOM and Fe particles could help minimize the DBP formation.

Effects of slurry ice treatment on the physicochemical changes and proteome of large yellow croaker (Larimichthys crocea).

Large yellow croaker (Larimichthys crocea) is susceptible to oxidative denaturation during storage. This work is to investigate the quality alterations by analyzing its physicochemical changes and proteomics throughout preservation under refrigeration, frozen, and slurry ice (SI) conditions. Results revealed that the freshness of large yellow croaker, as evaluated by indicators such as total volatile basic nitrogen, total viable count, and thiobarbituric acid reactive substances, was well maintained while stored in the SI group. Meanwhile, the water distribution in the muscle tissue of group SI exhibited slower fluctuations, thereby preserving the integrity of fish muscle cells. Based on label-free proteomic analysis, a considerable downregulation was observed in the mitogen-activated protein kinase (MAPK) signaling pathway, indicating that SI decelerated this metabolic pathway and effectively delayed the deterioration of muscle. Therefore, the application of SI provides potential for maintaining the quality stability of large yellow croaker.

A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks.

As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs.

Elucidating per- and polyfluoroalkyl substances (PFASs) soil-water partitioning behavior through explainable machine learning models.

In this study, an optimized random forest (RF) model was employed to better understand the soil-water partitioning behavior of per- and polyfluoroalkyl substances (PFASs). The model demonstrated strong predictive performance, achieving an R2 of 0.93 and an RMSE of 0.86. Moreover, it required only 11 easily obtainable features, with molecular weight and soil pH being the predominant factors. Using three-dimensional interaction analyses identified specific conditions associated with varying soil-water partitioning coefficients (Kd). Results showed that soils with high organic carbon (OC) content, cation exchange capacity (CEC), and lower soil pH, especially when combined with PFASs of higher molecular weight, were linked to higher Kd values, indicating stronger adsorption. Conversely, low Kd values (< 2.8 L/kg) typically observed in soils with higher pH (8.0), but lower CEC (8 cmol+/kg), lesser OC content (1 %), and lighter molecular weight (380 g/mol), suggested weaker adsorption capacities and a heightened potential for environmental migration. Furthermore, the model was used to predict Kd values for 142 novel PFASs in diverse soil conditions. Our research provides essential insights into the factors governing PFASs partitioning in soil and highlights the significant role of machine learning models in enhancing the understanding of environmental distribution and migration of PFASs.

Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system.

Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture the complex interrelationships between water quality and DBP occurrence. In this study, we collected drinking water samples from a distribution network throughout a year and measured the related water quality parameters (WQPs) and haloacetic acids (HAAs). 12 machine learning (ML) algorithms were evaluated. Random Forest (RF) achieved the best performance (i.e., R2 of 0.78 and RMSE of 7.74) for predicting HAAs concentration. Instead of using cytotoxicity or genotoxicity separately as the surrogate for evaluating toxicity associated with HAAs, we created a health risk index (HRI) that was calculated as the sum of cytotoxicity and genotoxicity of HAAs following the widely used Tic-Tox approach. Similarly, ML models were developed to predict the HRI, and RF model was found to perform the best, obtaining R2 of 0.69 and RMSE of 0.38. To further explore advanced ML approaches, we developed 3 models using uncertainty-based active learning. Our findings revealed that Categorical Boosting Regression (CAT) model developed through active learning substantially outperformed other models, achieving R2 of 0.87 and 0.82 for predicting concentration and the HRI, respectively. Feature importance analysis with the CAT model revealed that temperature, ions (e.g., chloride and nitrate), and DOC concentration in the distribution network had a significant impact on the occurrence of HAAs. Meanwhile, chloride ion, pH, ORP, and free chlorine were found as the most important features for HRI prediction. This study demonstrates that ML has the potential in the prediction of HAA occurrence and toxicity. By identifying key WQPs impacting HAA occurrence and toxicity, this research offers valuable insights for targeted DBP mitigation strategies.

The Application of Lophatherum Gracile Brongn Flavonoids in Wheat Flour Products: Effects on the Structural and Functional Characteristics of Wheat Dough.

The effects of lophatherum gracile brongn flavonoids on the multiscale structure and functional properties of wheat dough were investigated. Wheat dough samples with varying contents of lophatherum gracile brongn flavonoids were analyzed to assess changes in thermal-mechanical rheological properties, microstructure, chemical interactions, water distribution, and macropolymer formation by Mixolab mixer, fluorescence microscopy, and low-field nuclear magnetic resonance (LF-NMR). The findings revealed that lophatherum gracile brongn flavonoids disrupted the three-dimensional network of gluten proteins in the wheat dough, leading to decreased water-binding capacity and reduced gluten protein crosslinking while enhancing thermal stability and inhibiting the starch retrogradation of the dough. This study provided important insights into the interaction mechanisms between lophatherum gracile brongn flavonoids and the proteins/starch in wheat dough, offering theoretical guidance for the development of novel wheat-based products for industrialization and practical production.

Online state estimation in water distribution systems via Extended Kalman Filtering.

Operators of water distribution systems (WDSs) need continuous and timely information on pressures and flows to ensure smooth operation and respond quickly to unexpected events. While hydraulic models provide reasonable estimates of pressures and flows in WDSs, updating model predictions with real-time sensor data provides clearer insights into true system behavior and enables more effective real-time response. Despite the growing prevalence of distributed sensing within WDSs, standard hydraulic modeling software like EPANET do not support synchronous data assimilation. This study presents a new method for state estimation in WDSs that combines a fully physically-based model of WDS hydraulics with an Extended Kalman Filter (EKF) to estimate system flows and heads based on sparse sensor measurements. To perform state estimation via EKF, a state-space model of the hydraulic system is first formulated based on the 1-D Saint-Venant equations of conservation of mass and momentum. Results demonstrate that the proposed model closely matches steady-state extended-period models simulated using EPANET. Next, through a holdout analysis it is found that fusing sensor data with EKF produces flow and head estimates that closely match ground truth flows and heads at unmonitored locations, indicating that state estimation successfully infers internal hydraulic states from sparse sensor measurements. These findings pave the way towards real-time operational models of WDSs that will enable online detection and mitigation of hazards like pipe leaks, main bursts, and hydraulic transients.

Mechanistic insights into combined effects of continuous microwave heating and tremella powder addition on physiochemical properties of Nemipterus virgatus surimi gel.

The physicochemical properties of Nemipterus virgatus surimi gel were investigated, with tremella powder (TP) at concentrations ranging from 0 to 0.5% (w/w) combined with continuous microwave heating (CMH) using water-bath heating (WBH) as control. Results showed that TP addition (0.1%-0.3%, w/w) could significantly enhance the water holding capacity and reduce whiteness and cooking loss, attributed to the changed lateral relaxation time of water distribution. Notably, at 0.3% TP and 80 °C, the gel strength significantly increased by 96.84%, and the hardness, chewiness, and adhesiveness improved, but the quality of surimi decreased above 0.3% TP. The gel network structure was influenced by protein secondary structure composition, especially for increasing ß-sheet in Raman spectra, thus promoting the gel microstructure density and uniform protein distribution. These findings offer insights for enhancing surimi gel quality and broadening tremella application in product processing.

A synergistic approach combining computational fluid dynamics simulation with hydrolysis-acidification for dye wastewater treatment.

Wastewater treatment is effectively conducted using anaerobic biological methods. Nevertheless, the efficiency of these methods can be hindered by challenges like short-circuits and dead zones, particularly in treating persistent contaminants. This work utilized computational fluid dynamics (CFD) simulations to enhance water distribution, ensuring uniform interactions between solid and liquid phases, and thus mitigating issues related to short-circuits and dead zones. Such enhancements notably amplified the anaerobic biological process's efficiency. Furthermore, dye biodegradability was improved through the application of the hydrolysis acidification technique. Optimal hydraulic retention time for the hydrolysis-acidification reactor, established at 9 h, was determined via sludge cultivation and domestication for stable operation. During stable operation, an elevation in effluent volatile fatty acids was observed, alongside a COD removal rate fluctuating between 15% and 29%. Approximately 50% was noted as the rate of color removal. Simultaneously, a noticeable decrease in effluent pH occurred, with total nitrogen removal approximating 8%. An estimated BOD5/COD ratio of 0.32 was recorded. The incorporation of microbial agents led to an enhanced COD removal, ranging from 28% to 33%, thereby stabilizing the effluent BOD5/COD ratio at around 0.35. This research highlights the advantages of optimizing water distribution in anaerobic reactors, particularly when combined with hydrolysis-acidification techniques, effectively addressing issues of short-circuits and dead zones.

Effects of soaking treatment on water distribution of rice grains, starch characteristics and eating quality of wet rice noodles.

Herein, rice was subjected to different soaking processes on water distribution of rice grains, starch characteristics, and eating quality of fresh wet rice noodles. Results demonstrated that, when soaked at temperatures between 10 °C and 40 °C for 120 min, rice grains reached saturation in water absorption, and the hardness gradually stabilized. However, the moisture continued to penetrate the interior of rice grains after 120 min, leading to an increase in moisture content, higher water permeability, and enlarged water migration channels. With extended soaking time periods, the content of damaged starch in rice flour considerably decreased. Although the gelatinization temperature of rice starch decreased after soaking, the enthalpy required for gelatinization increased. The relative crystallinity of rice starch demonstrated an increasing trend, followed by a decreasing trend, and reached its highest value of 18.18 % after 60 min of soaking. To summarize, the texture indices of fresh rice noodles demonstrated an increasing trend, although stretching and cooking quality demonstrated a trend of initially increasing and then decreasing with no considerable changes observed between 120 and 240 min of soaking. In summary, moderate soaking treatment can enhance the edible quality of fresh wet rice noodles.

Fate and transport of chlorine dioxide: Modeling chlorine dioxide in water distribution systems.

This study aims to conduct a comprehensive analysis of switching disinfectants from sodium hypochlorite bleach to chlorine dioxide (ClO2) in the water distribution system of Geyikbayiri, Antalya. For this purpose, bulk decay rates of ClO2 at various water temperatures were determined in laboratory studies. The study revealed ClO2 bulk decay rates of 0.12639 day-1, 0.17848 day-1, and 0.19621 day-1 at temperatures 15°C, 20°C, and 30°C, respectively. The EPANET, a widely employed computer program for simulating the extended-period behavior of hydraulic and water quality in pressurized pipes, was utilized for the analysis of the fate and transport of ClO2. A hydraulic model was first developed, calibrated, and verified using distinct data sets. The Hazen-Williams friction coefficient of the PSA was determined to be 120 by the trial-and-error method with a mean absolute error (MAE) of 0.408 m. A ClO2 model was then integrated with the calibrated and verified hydraulic model, revealing a wall decay rate of 0.01 m/day and an average MAE of 0.034 mg/l. After calibration and verification of the ClO2 model, several management scenarios were developed, and ClO2 dosing rates were determined. The study showed that ClO2 dosing rates of 0.40 mg/l and 0.45 mg/l should be applied to keep ClO2 concentrations within certain limits. PRACTITIONER POINTS: Disinfectants must maintain a sufficient residual in water distribution systems. Chlorine dioxide requires less contact time and is not affected by pH fluctuations. Modeling serves as a decision-making tool for the management of disinfectants. Bulk and wall decay rates of chlorine dioxide are crucial for management strategies. Chlorine dioxide is a good alternative as a disinfectant in such systems.

Customised-sampling approach for pipe failure prediction in water distribution networks.

This paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and class weighting as primary strategies. These techniques aim to treat the imbalanced datasets by adjusting the representation of minority and majority classes. Under-sampling reduces data in the majority class, over-sampling adds data to the minority class, and class weighting assigns unequal weights based on class counts to balance the influence of each class during machine learning (ML) model training. In this paper, the mentioned approaches were used at levels other than "balance point" to construct pipe failure prediction models for a WDN with highly imbalanced data. F1-score, and AUC-ROC, were selected to evaluate model performance. Results revealed that under-sampling above the balance point yields the highest F1-score, while over-sampling below the balance point achieves optimal results. Employing class weights during training and prediction emphasises the efficacy of lower weights than the balance. Combining under-sampling and over-sampling to the same ratio for both majority and minority classes showed limited improvement. However, a more effective predictive model emerged when over-sampling the minority class and under-sampling the majority class to different ratios, followed by applying class weights to balance data.

Analysis of THM formation potential in drinking water networks: Effects of network age, health risks, and seasonal variations in northwest of Iran.

Various factors influence the formation of disinfection by-products (DBPs) in drinking water. Therefore, it is crucial to study the formation of DBPs and identify the associated influencing agents in water distribution networks (WDNs) to effectively prevent and control the health risks posed by DBPs. This research aimed to examine THM concentrations in the WDNs of Maragheh, Iran, focusing on seasonal variations. It also compared THM levels between new and old WDNs and assessed the health risks associated with exposure to THMs through various exposure routes. The mean concentrations of Chloroform, BDCM, DBCM, and Bromoform were 44.28 ± 18.25, 12.66 ± 5.19, 3.16 ± 0.89, and 0.302 ± 0.89 µg/L, respectively. Therefore, Chloroform was the predominant compound among the THM species, accounting for over 72 % of the total THMs (TTHMs). The average TTHMs concentration in summer (69.89 µg/L) was significantly higher than in winter (50.97 µg/L) (p < 0.05). Except for Bromoform, concentrations of other THM species in the new WDNs were considerably lower than in the old WDN (p < 0.05). The mean lifetime cancer risk (LTCR) rates for oral and dermal exposure routes to THMs were negligible and within acceptable risk levels. However, the LTCR mean values for inhalation exposure routes to THMs in winter and summer were within low (1 × 10-6 ≤ LTCR <5.1 × 10-5) and high acceptable risk levels (5.1 × 10-5 ≤ LTCR <10-4), respectively. Inhalation exposure presented the highest cancer risk among the various exposure routes. The hazard index values for oral and dermal contact with THMs were less than 1. Finally, sensitivity analysis revealed that the ingestion rate and exposure duration of THMs had the most significant positive effect on chronic daily intake (CDI) values and cancer risk. However, further comprehensive investigations are needed to develop effective solutions for reducing and controlling the precursors of DBP formation, as well as identifying suitable alternative disinfection compounds that minimize by-product formation.

Advancing the analysis of water pipe failures: a probabilistic framework for identifying significant factors.

The failure of water pipes in Water Distribution Networks (WDNs) is associated with environmental, economic, and social consequences. It is essential to mitigate these failures by analyzing the historical data of WDNs. The extant literature regarding water pipe failure analysis is limited by the absence of a systematic selection of significant factors influencing water pipe failure and eliminating the bias associated with the frequency distribution of the historical data. Hence, this study presents a new framework to address the existing limitations. The framework consists of two algorithms for categorical and numerical factors influencing pipe failure. The algorithms are employed to check the relevance between the pipe's failure and frequency distributions in order to select the most significant factors. The framework is applied to Hong Kong WDN, selecting 10 out of 21 as significant factors influencing water pipe failure. The likelihood feature method and Bayes' theorem are applied to estimate failure probability due to the pipe materials and the factors. The results indicate that galvanized iron and polyethylene pipes are the most susceptible to failure in the WDN. The proposed framework enables decision-makers in the water infrastructure industry to effectively prioritize their networks' most significant failure factors and allocate resources accordingly.