Evaluating rehabilitation proposals for water utilities can be sensitive and subjective. Often the cost is the first component considered in evaluating and prioritizing action plans. However, the impact that short-term versus long-term strategies and proactive versus reactive strategies could have on cost should equally be considered. Data is also vital to the assessment process but is often lacking (e.g. the condition of urban buried pipes), injecting uncertainty that can only be resolved as rehabilitation plans proceed. Assessing the cost of more investigation against the benefit of more knowledge is the basis of the Bayesian theory in decision-making analysis. Following this line of reasoning, researchers Roya Meydani, Tommy Giertz, and John Leander propose a Bayesian decision-making model for the case of water-loss management and rehabilitation in water distribution networks.
In their study, “Decision with Uncertain Information: An Application for Leakage Detection in Water Pipelines” in the Journal of Pipelines Systems Engineering and Practice, the authors wanted to identify a utility-based decision-making plan to address water loss management. To do this, they identified a set of actions for leakage rehabilitation, and evaluated the optimal choice by finding the lowest expected cost using Bayesian methods. Their model was then applied to a case study in Uppsala, Sweden. Learn more about their proposed model and how it can help to minimize the time for leakage detection and localization at https://doi.org/10.1061/(ASCE)PS.1949-1204.0000644. The abstract is below.
Abstract
Infrastructure rehabilitation comprises remedial and prevention measures; however, preventing failure is not always possible, and direct investigations to find evidence of failure are challenging. Urban buried pipes are among the infrastructure that needs recurrent remedial actions. At this point, it is important to raise the question of what the appropriate strategy to locate or rehabilitate leakage is. This paper aims to implement and evaluate a Bayesian decision model for the maintenance planning of a water network. This includes the treatment of uncertainties in the evaluation of the best decision in a short-term perspective. To this end, a utility-based optimization routine based on the Bayesian theory has been used. The proposed model, due to its simplicity, can facilitate the initial problem-structuring in the process of decision-making under uncertainty. The model has been demonstrated on a water distribution network in Sweden, optimizing the decisions for locating and rehabilitating leakages. The results show that the cost of interventions and probabilities of leakages has a significant influence on the most appropriate decision.
Read the paper in full in the ASCE Library: https://doi.org/10.1061/(ASCE)PS.1949-1204.0000644
