Rehan Sadiq

The water quality in a distribution system is affected by many factors, including operational and environmental conditions as well as the condition in and around the distribution network. Lack of reliable data as well as knowledge gaps with respect to the impact of these factors on water quality make the quantification of water quality failure risk very challenging. Furthermore, the variability inherent in (sometimes) thousands of kilometers of distribution pipes presents added complexities. Major modes of water quality failures can be classified into intrusion of contaminants, regrowth of bacteria (biofilm), water treatment breakthrough, leaching of chemicals or corrosion products from system components, and permeation of organic compounds through plastic pipes. Deliberate contamination and negligence of operators have in recent years become an added concern. In earlier works by Sadiq et al. ( 2004 Sadiq, R. , Kleiner, Y. and Rajani, B. 2004. Aggregative risk analysis for water quality failure in distribution networks. Journal of Water Supply Research and Technology: Aqua, 53(4): 241–261. [Web of Science ®] [Google Scholar] , 2007 Sadiq, R. , Kleiner, Y. and Rajani, B. 2007. Water quality failures in distribution networks – risk analysis using fuzzy logic and evidential reasoning. Risk Analysis – An International Journal, 27(5): 1381–1394. [Crossref], [PubMed], [Web of Science ®] [Google Scholar] ), an aggregative risk analysis approach using hierarchical structure was proposed to describe all possible mechanisms of contamination. In this paper a similar structure is used as a basis for a fault-tree approach. While fault-tree analysis is widely used for many engineering applications, in this paper we specifically explore how interdependencies among factors might impact analysis results. Two types of uncertainties are considered in the proposed analysis. The first is related to the likelihood of risk events, and the second is related to non-linear dependencies among risk events. Each basic risk event (input factor) is defined using a fuzzy probability (likelihood) to deal with its inherent uncertainty. The dependencies among risk events are explored using Frank copula and Frechet's limit. The proposed approach is demonstrated using two well-documented episodes of water quality failures in Canada, namely, Walkerton (ON) and North Battleford (SK).

The evaluation of the risk of water quality failures in a distribution network is a challenging task given that much of the available data are highly uncertain and vague, and many of the mechanisms are not fully understood. Consequently, a systematic approach is required to handle quantitative‐qualitative data as well as a means to update existing information when new knowledge and data become available. Five general pathways (mechanisms) through which a water quality failure can occur in the distribution network are identified in this article. These include contaminant intrusion, leaching and corrosion, biofilm formation and microbial regrowth, permeation, and water treatment breakthrough (including disinfection byproducts formation). The proposed methodology is demonstrated using a simplified example for water quality failures in a distribution network. This article builds upon the previous developments of aggregative risk analysis approach. Each basic risk item in a hierarchical framework is expressed by a triangular fuzzy number, which is derived from the composition of thelikelihoodof a failure event and the associated failureconsequence. An analytic hierarchy process is used to estimate weights required for grouping noncommensurate risk sources. The evidential reasoning is proposed to incorporate newly arrived data for the updating of existing risk estimates. The exponential ordered weighted averaging operators are used for defuzzification to incorporate attitudinal dimension for risk management. It is envisaged that the proposed approach could serve as a basis to benchmark acceptable risks in water distribution networks.

Intrusion of contaminants into water distribution networks requires the simultaneous presence of three elements: contamination source, pathway and driving force. The existence of each of these elements provides ‘partial’ evidence (typically incomplete and non-specific) to the occurrence of contaminant intrusion into distribution networks. Evidential reasoning, also called Dempster–Shafer theory, has proved useful to incorporate both aleatory and epistemic uncertainties in the inference mechanism. The application of evidential reasoning to assess risk of contaminant intrusion is demonstrated with the help of an example of a single pipe. The proposed approach can be extended to full-scale water distribution networks to establish risk-contours of contaminant intrusion. Risk-contours using GIS may help utilities to identify sensitive locations in the water distribution network and prioritize control and preventive strategies.

Dearth of data is the greatest acknowledged obstacle to the deterioration modeling of the buried infrastructure assets. In the last two decades numerous models have been proposed with a greater emphasis on Markovian Deterioration Processes (MDP). The MDP requires that the condition of the deteriorating system be encoded as an ordinal condition rating, based on numerous distress indicators obtained possibly from direct and indirect observations, as well as from non-destructive tests. The encoding of distress indicators into condition rating is inherently imprecise and involves subjective judgment. This imprecision is not considered, let alone propagated in the traditional application of the MDP. In this paper a new approach is presented to model the deterioration of buried infrastructure assets using a fuzzy rule-based, non-homogeneous Markov process. This deterioration model yields the ‘possibility’ of failure at every time step along the life of the asset. The use of fuzzy sets and fuzzy techniques help to incorporate the inherent imprecision and subjectivity of the data, as well as to propagate these attributes throughout the model, yielding more realistic results. This paper is the first of two companion papers that describe a complete method of managing failure risk of large buried infrastructure assets. The second companion paper describes how the condition ratings along the life of the asset are converted to risk values and how effective decisions can be made about the renewal and/or scheduling the next inspection of the asset.