Water mains failure studies have focused on failure clustering analysis which measures the spatial clustering of failures and failure factor analysis which quantifies the impact of various factors on failure occurrences. This study aims to enhance these two analyses by using a spatial analysis approach. The improvements include (1) initial global and multi-scale geographical statistics to measure the failure clustering, (2) refining quantitative relationships between failure factors and failure occurrences and (3) filling the research gap in subtropical regions by a Hong Kong case study. The global and multi-scale failure cluster measures are based on Moran's I and Ripley's K-statistic. Failure factors are analysed using descriptive statistics and regressions. Failures rates per unit pipe length were found highly clustered in space. The scale at which the failures are the most clustered was also identified. The failure factor analysis revealed quantitative relationships that were more detailed than previous studies, or specific for Hong Kong, between failure rate and four factors: pipe diameter, pipe age, material and temperature. The global failure cluster measure verifies the necessity for cluster analysis in identifying areas of high failure risk. The multi-scale measure suggests that it should be effective and economic to monitor areas of high failure risk if the area size is 1-1.5 km in radius. The refined failure factor analysis can enhance the accuracy of failure risk prediction models and results in several failure control recommendations for Hong Kong and other subtropical cities.
- correlation analysis
- global and multi-scale geographical statistics
- Moran's I; Ripley's K-statistic
- spatial analysis
ASJC Scopus subject areas
- Computer Science Applications
- Earth and Planetary Sciences(all)