This paper presents a novel multiobjective genetic algorithm (MOGA) based on NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered.

This paper presents a comprehensive framework to manage the main risk events of highway construction pro­jects within three stages: (1) identification of potential risks; (2) assessment and prioritisation of identified risks based on fuzzy FMEA; (3) identification of appropriate response. The main criteria analysed for prioritising potential risk events are cost, time and quality which are quantified and combined using fuzzy AHP. A new expert system is suggested for identifying an appropriate risk response strategy for a risk event based on risk factor, control number and risk alloca­tion. The best response action for a risk event is then identified with respect to the same criteria using “scope expected deviation” (SED) index. The proposed methodology is demonstrated for management of risk events in a construction project of Bijar-Zanjan highway in Iran. For the risk event of “increase in tar price”, deviation from the target values of the criteria is analysed for business-as-usual state plus two risk response actions using SED index. The results show that the response action of “changing paving construction technology from asphalt pavement to RCC pavement” can success­fully cope with the risk event of “increase in tar price” and have the minimum deviation.

Despite providing water-related services as the primary purpose of urban water system (UWS), all relevant activities require capital investments and operational expenditures, consume resources (e.g. materials and chemicals), and may increase negative environmental impacts (e.g. contaminant discharge, emissions to water and air). Performance assessment of such a metabolic system may require developing a holistic approach which encompasses various system elements and criteria. This paper analyses the impact of integration of UWS components on the metabolism based performance assessment for future planning using a number of intervention strategies. It also explores the importance of sustainability based criteria in the assessment of long-term planning. Two assessment approaches analysed here are: (1) planning for only water supply system (WSS) as a part of the UWS and (2) planning for an integrated UWS including potable water, stormwater, wastewater and water recycling. WaterMet(2) model is used to simulate metabolic type processes in the UWS and calculate quantitative performance indicators. The analysis is demonstrated on the problem of strategic level planning of a real-world UWS to where optional intervention strategies are applied. The resulting performance is assessed using the multiple criteria of both conventional and sustainability type; and optional intervention strategies are then ranked using the Compromise Programming method. The results obtained show that the high ranked intervention strategies in the integrated UWS are those supporting both water supply and stormwater/wastewater subsystems (e.g. rainwater harvesting and greywater recycling schemes) whilst these strategies are ranked low in the WSS and those targeting improvement of water supply components only (e.g. rehabilitation of clean water pipes and addition of new water resources) are preferred instead. Results also demonstrate that both conventional and sustainability type performance indicators are necessary for strategic planning in the UWS.

WaterMet 2 in evaluating the sustainability related UWS performance, the suitability of using WaterMet 2 at the strategic level UWS planning and the importance of using an integrated assessment approach covering the full urban water cycle.

This paper presents a new approach for improving pipeline failure predictions by combining a datadriven statistical model, i.e. Evolutionary Polynomial Regression (EPR), with k-means clustering. The EPR is used for prediction of pipe failures in case iron pipes based on length, diameter and age of pipes as explanatory factors. Individual pipes are aggregated using their attributes of age, diameter and soil type to create homogenous groups of pipes. The k-means clustering is employed to partition input data into a number of clusters for individual EPR models. The proposed approach was demonstrated by application to a water distribution network in the UK. The prediction accuracy was evaluated using a cross-validation technique. Results show the proposed approach is able to significantly reduce the error of pipe failure predictions especially in the case of a large number of failures. The prediction models were used to calculate the failure rate of individual pipes for rehabilitation planning.