Owners of infrastructure assets have responsibility for the management of a diverse portfolio of civil engineering assets. These assets make up the foundations of modern society and are arguably pivotal in the economic growth and wellbeing of a nation. It is of no surprise therefore, that asset management business practises have risen in popularity as the UK’s infrastructure asset base continues to grow and inevitably ages with time. In the context of water and waste water infrastructure assets, which communities rely upon for health, economy and environmental sustainability, it is widely acknowledged that these assets have historically suffered from underinvestment. Whilst funding shortfalls have been evidenced historically, through the inadequacy of infrastructure to meet the needs and challenges of the past, it is of great concern that infrastructure expenditure is reducing in real terms as a result of the global financial crisis. This is leading to a widening funding gap between the available and the required finances for infrastructure investment which is further compounded by natural phenomena and human behaviours, i.e., climate change, population growth and urbanisation. To further intensify the problem, asset planning and management in the water industry is considered a complex and challenging discipline because of high interdependencies and the vast quantity of assets themselves. In acknowledgement of this global position, this thesis seeks to address some of the key challenges faced by utility companies in the adoption of asset management best practice across water and waste water assets, namely: • Operational decision making - the efficient and effective specification of least-cost rehabilitation programmes from condition information that ensure behavioural alignment with an organisations strategic objectives. • Tactical decision making - achieving risk based asset level inspection prioritisation that considers serviceability performance, for two particularly challenging asset groups: i.) High value - low volume assets and ii). Low value - high volume buried infrastructure. • Strategic decision making - identifying optimal long-term investment plans and asset management policies for assets that have previously not benefited from such technological advancements. To improve upon operational decision making, the author capitalises on the availability of condition inspection information for buried sewerage infrastructure by applying advanced optimisation techniques to help form an environment where the decision makers is presented with an array of optimal rehabilitation solutions. The trade-off curve that is presented uniquely evaluates solutions for the benefits they offer in-terms of: condition improvement, cost and operational performance. A financially favourable comparison (up to 45% saving) is drawn between the optimisation results which are automatically generated by the model and those that have been developed manually by experienced engineers in a ‘real world’ case study. However, it could be argued that the greatest benefit arises from the trade curve of feasible solutions which are presented to the decision maker across a range of investment levels. In recognition that tactical and strategic decision making have been the focus of a substantial amount of research for commonly found infrastructure assets, i.e., public sewers and water mains, a focus has been placed on improving upon and adopting best practise across infrastructure assets which have not previously benefited from the technological developments across these decision making levels. Firstly, a methodology for translating standardised condition inspection information into more meaningful reliability scores, to support risk based planning and decision making, is presented for service reservoirs. A service reservoir can be regarded as high value- low volume infrastructure asset and would typically have its condition evaluated between 1 (poor) to 5 (good). A case study demonstrates how this new reliability scoring mechanism has been successfully applied during a typical structural condition survey. The output from this process is a fully document reliability assessment for each component of the service reservoir. The output can be aggregated to provide an overall reliability assessment for the structure and/or used to target specific remedial works to troublesome components. Secondly, two methodologies are presented which address the fact that high volume – low value infrastructure assets across both the water distribution and wastewater collection networks, are typically less well understood and often sub-optimally managed in comparison to more critical or higher value assets. 1. A methodology has been developed to help UK water companies overcome the recent legislative changes associated with Section105A of the Water Act; which has transferred ownership of the private sewer network to UK water companies. The new methodology which has been developed, has allowed one of the UK’s water and sewerage companies to initiate a proactive asset management programme with the aim of addressing the deteriorating condition of these assets whilst also tackling their associated serviceability performance. Initially, a number of GIS tools are used to provide an estimate of the likely extent of the transferred network before a well-established public sewer deterioration model is used to predict the condition and operational performance of these S105A assets over time. 2. A novel deterioration modelling framework is developed by coupling the latest geospatial technologies with statistical deterioration modelling techniques. The modelling framework is specifically applied to small diameter water distribution assets (25-50mm diameter), known as communication pipes, which connect individual properties to the water distribution mains. Reliability curves are developed from failure data provided by two UK based Water Companies that have captured specific communication pipe failure records since 2001. The deterioration modelling curves and supporting data are compared and contrasted to demonstrate the robustness of this modelling approach, which is shown to be capable of modelling failure rates to a high degree of accuracy. This was validated by comparing the predicted number of failures against three years of failure data not used during the model building process. The yearly failure counts were predicted to within +/-5% accuracy and the overall cumulative modelled failure count at the end of 2014 was predicted within 1%. To conclude, the successful deterioration modelling tools for communication pipes are explored further, via the development of a strategic whole life cost optimisation framework for these assets. The outputs from the previous geospatial mapping tool are used alongside the calibrated Weibull deterioration curves to drive a whole life cost and performance analysis. Against this improved understanding of whole life costs, an optimisation algorithm is used to evaluate the trade-off between whole life costs (totex) and the prevention of future asset failures (serviceability). The model successfully identifies optimised investment policies according to the decision maker’s priorities which is evidenced in a case study that shows outperformance against existing maintenance policies for these assets. Financial savings in the region of £8.5M, or the prevention of 1,320 asset failures, were shown to be possible over a 25 years planning horizon in the case study. For the avoidance of confusion, the term ‘integrated’ is considered from the perspective of the three decision making levels associated with the management of an asset, namely: strategic, tactical and operational decision making. Therefore, data quality improvements and the management of information transactions between decisional levels are inherently considered within all of the methodologies developed in this thesis.

The fundamental purpose of drinking water distribution systems is to provide safe drinking water at sufficient volumes and optimal pressure with the lowest lifecycle costs from the source (treatment plants, raw water source) to the customers (residences, industries). Most of the distribution systems in the US were laid out during the development phase after World War II. As the drinking water infrastructure is aging, water utilities are battling the increasing break rates in their water distribution system and struggling to bear the associated economic costs. However, with the growth in sensory technologies and data science, water utilities are seeing economic value in collecting data and analyzing it to monitor and predict the performance of their distribution systems. Many mathematical models have been developed to guide repair and rehabilitation decisions in the past but remain largely unused because of low reliability. This is because any effort to build a decision support framework based on a model should rest its foundations on a robust knowledge base of the critical factors influencing the system, which varies from utility to utility. Mathematical models built on a strong understanding of the theory, current practices and the trends in data can prove to be more reliable. This study presents a framework to support repair and rehabilitation decisions for water utilities using water pipeline field performance data. / Master of Science / The fundamental purpose of drinking water distribution systems is to provide a safe and sufficient volume of drinking water at optimal pressure with the lowest costs to the water utilities. Most of the distribution systems in the US were established during the development phase after World War II. The problem of aging drinking water infrastructure is an increasing financial burden on water utilities due to increasing water main breaks. The growth in data collection by water utilities has proven to be a useful tool to monitor and predict the performance of the water distribution systems and support asset management decisions. However, the mathematical models developed in the past suffer from low reliability due to limited data used to create models. Also, any effort to build sophisticated mathematical models should be supported with a comprehensive review of the existing recommendations from research and current practices. This study presents a framework to support repair and rehabilitation decisions for water utilities using water pipeline field performance data.