In real world water distribution systems (WDS) uncertainty can arise in a number of different ways. Variations in the performance of parts (for example pipe roughness) can affect the performance of the system. Uncertainty in the requirements the system must satisfy (such as demand at a node) will affect the ability of the system to meet those requirements. An algorithm which can reduce the number of fitness evaluations required to find performance probabilities for systems operating under uncertainty has the potential to significantly reduce computation times required for optimisation. Furthermore when system uncertainties include mechanical failures such as pipe bursts, blockages and leaks, costs can be associated with underperformance allowing such an algorithm to offer risk-based optimisations of systems by assigning an expected consequence of failure to each design. Such optimisations will find a family of solutions offering a trade-off between the cost of the system and the expected future costs or consequences due to failures and other uncertainties.The need for an optimisation technique which is not only capable of optimising systems under uncertainty, but is also scalable to large WDS is at the heart of the proposed research.This research project brings mathematical techniques for statistical sampling and evolutionary optimisation together with an engineering knowledge of the design of water distribution systems under uncertainty.

It is widely acknowledged that the water and wastewater infrastructure assets, which communities rely upon for health, economy and environmental sustainability, are severely underfunded on a global scale. For example, a funding gap of nearly $55 billion has been identified by the US EPA (ASCE, 2011). In England and Wales, the total estimated capital value of water utility assets is £254.8 billion (Ofwat, 2015), but between 2010 and 2015 only £12.9 billion was allocated for maintaining and replacing assets. Combined with the drive to reduce customers' bills, there will be even more pressure on water companies to find ways to bridge the gap between the available and required finances. As a result of this it is not surprising that optimisation methods have been extensively researched and applied in this area (Maier et al., 2014). The inability of those methods to include into optimisation 'unquantifiable' or difficult to quantify, yet important considerations, such as user subjective domain knowledge, has contributed to the limited adoption of optimisation in the water industry. Many cognitive and computational challenges accompany the design, planning and management involving complex engineered systems. Water industry infrastructure assets (i.e., water distribution and wastewater networks) are examples of systems that pose severe difficulties to completely automated optimisation methods due to their size, conceptual and computational complexity, non-linear behaviour and often discrete/combinatorial nature. These difficulties have first been articulated by Goulter (1992), who primarily attributed the lack of application of optimisation in water distribution network (WDN) design to the absence of suitable professional software. Although such software is now widely available (e.g., InfoWorks, WaterGems, EPANET, etc.), the lack of user under-standing of capabilities, assumptions and limitations still restricts the use of optimisation by practicing engineers (Walski, 2001). Automatic methods that require a purely quantitative mathematical representation do not leverage human expertise and can only find solutions that are optimal with regard to an invariably over-simplified problem formulation. The focus of the past research in this area has almost exclusively been on algorithmic issues. However, this approach neglects many important human-computer interaction issues that must be addressed to provide practitioners with engineering-intuitive, practical solutions to optimisation problems. This project will develop new understanding of how engineering design, planning and management of complex water systems can be improved by creating a visual analytics optimisation approach that will integrate human expertise (through 'human in the loop' interactive optimisation), IT infrastructure (cloud/parallel computing) and state-of-the-art optimisation techniques to develop highly optimal, engineering intuitive solutions for the water industry. The new approach will be extensively tested on problems provided by the UK water industry and will involve practicing engineers and experts in this important problem domain.