Particle accumulation and circulation in water distribution systems are significant in the development of good management practices to protect against discoloration events, which are a major cause of water customer complaints. Quantifying the amount of particles deposited in water pipes is usually done by obtaining total suspended solid measurements while performing flushing sequences, which requires time, skills, and equipment. Some authors explored the possibility of rapidly approximating total suspended solids concentration (TSSC) in water pipes by measuring water turbidity on site, but they obtained different results and coefficients of correlation. This paper presents the results of tests performed in the laboratory on a test loop. Unidirectional flushing (UDF) and air scouring sequences were performed under various hydraulic conditions and two different particle origins. Samples were obtained along each sequence, and the turbidity and TSSC were measured. The results illustrate that the ratio between turbidity and TSSC may vary greatly between samples, up to 10 times during UDF sequences and 20 times during air scouring sequences. Particle origin, flushing method, and sampling time are all factors impacting the turbidity/TSSC ratio. This is why TSSC should not be estimated from a single turbidity reading.

20Existing methods used to identify the important factors that can improve predicting structural 21 deterioration of sewer pipes rarely take into account the interactions and correlations among 22 them. Here we present a standardized method that combines use of the Cox model and 23 likelihood ratio test, and overcomes these limitations of previously employed methods. This 24 combined method is applied to the pipes of two Canadian sewer systems, and its results are 25 compared to the results of two simpler methods for the identification of the factors that 26 significantly influence sewer pipe deterioration. The three methods identified pipe age as the 27 principal factor driving the structural deterioration of sewer pipes. However, slight differences 28 between the methods for other potential influential factors (material, slope and diameter) 29showed that accounting for the interactions and correlations among factors, as is possible with 30 the proposed method, is crucial to identifying the factors having a significant impact on pipe 31 deterioration. 32 33 Résumé 37 Les méthodes existantes permettant d'identifier les facteurs d'influence qui doivent être pris en 38 compte dans la modélisation de la détérioration structurale des conduites d'égout prennent 39 rarement en compte les interactions et/ou les corrélations entre ces facteurs. Une méthode 40 standardisée, basée sur l'utilisation combinée du modèle de Cox et du test du rapport de 41 vraisemblance, est proposée dans cet article. Cette méthode est appliquée aux conduites de 42 deux réseaux d'égout canadiens et ses résultats sont comparés aux résultats de deux 43 méthodes plus simples pour l'identification des principaux facteurs influents. Les trois méthodes 44 identifient l'âge des conduites comme étant le principal facteur d'influence dans le processus de 45 détérioration des conduites. Cependant, de légères différences entre les résultats de ces 46 méthodes concernant certains facteurs potentiellement influents (matériau, pente et diamètre) 47 démontrent que la prise en compte des interactions et des corrélations entre les facteurs, 48 rendue possible avec la méthode proposée, est cruciale pour identifier les facteurs ayant un 49 impact significatif. 50 51 Mots-clés: analyse de survie; covariables; état structural; Kruskal-Wallis; modèle de Cox; 52 rapport de vraisemblance. 53 54 55 56 Page 3 of 41 Can. J. Civ. Eng. Downloaded from www.nrcresearchpress.com by Laurentian University on 12/01/17 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record. Bauwens, 2010): 1) physical models that are based on the physical mechanisms governing the 60 deterioration of pipes (e.g. Konig, 2005); 2) artificial intelligence models (e.g. Tran et al. 2006; 61 Kleiner et al. 2006); and 3) statistical models (e.g. Duchesne et al. 2013). The input data for 62each of the three model types are pipe condition ratings, which summarize the defects (nature, 63 nu...

No abstract

No abstract