Modified sewer asset management to accommodate London's future sustainable development

Purpose This paper is about best practice in managing legacy drainage assets to support sustainable urban regeneration. The purpose of this paper is to describe best practice sewer asset management (AM) and to adjust the current reactive maintenance approach for sewers, to one that accommodates long-term operational and town planning needs. The development of an improved sewer deterioration model (DM) provided an important tool for this. Design/methodology/approach This research adopts a mixture of qualitative and quantitative approaches to analyse a total network length of 24,252 km which represents 703,156 records of historic sewer structural condition inspection data. This was used to build an improved DM. These models were used as inputs into a proactive AM approach that improves upon recommendations in the Sewerage Rehabilitation Manual developed by Water Research Centre. Findings This is a paradigm shift and goes beyond the current culture of OFWAT (Water Services Regulation Authority) supervision, five-year asset management period and occasional environmental penalties. A new legislative model may be needed; especially because a report by UKWIR (Water Industry Research) in 2015 identified that nationally the rate of sewer network deterioration is outpacing available investment and significant health problems may arise in addition to those from developmental pressures. Research limitations/implications - The authors have researched and managed old sewer networks and present a review of the new issues raised by intensive development, particularly for the London region, but applicable elsewhere, and how these must lead to a modified risk, and novel incentive-based approach to AM, if the system is not to fail. Originality/value - Large, legacy databases of several decades of sewer network performance records have been combined and analysed as stratified, heterogeneous sets with Gaussian distributions; thereby improving on previous assumptions of homogeneous data. The resulting rigorous DMs are the foundation of new approaches to sustainable risk management of large urban networks.