Assessment, Costing and enhancement of long life, Long Linear assets

Project: Research council

Description

Infrastructure is fundamental to our economy and society, e.g. being one of the 10 pillars of the recently launched UK Industrial Strategy. Long linear (geotechnical) assets (LLAs) are a major component of this infrastructure and fundamental to the delivery of critical services over long distances (e.g. road & railway slopes, pipeline bedding, flood protection structures). Central government infrastructure investment will rise by almost 60% to £22 billion p.a. by 2022 (ONS). This will support both the development of new infrastructure, and the repair of existing infrastructure. At present, there are 10,200 km of flood defences in Great Britain; 80,000 km of highways; 15,800 km of railway). Failure of these assets is common-place (e.g. in 2015 there were 143 earthworks failures on Network Rail - >2 per week), the resulting cost of failure is high (e.g. for Network Rail, emergency repairs cost 10 times planned works, which cost 10 times maintenance), and vulnerability to these failures is significant (748,000 properties with at least a 1-in-100 annual chance of flooding; derailment from slope failure is the greatest infrastructure-related risk faced by our railways). However, the exact reasons for - and timing of - failure is, at present, poorly understood. This leads to unanticipated failures that cause severe disruption and damage to reputation. Current approaches to design and asset management perpetuate this situation as they are based on past experience, which cannot be extrapolated to future performance: the infrastructure is older, ever more intensively used and subject to increasingly extreme weather patterns. Together, these factors significantly increase the likelihood of failures in the future causing reduced performance and poorer service. Climate change has been identified as one of the factors driving this change.

There is an exciting opportunity to bring together new advances in research and technology with design and asset management practices from diferent LLAs to reduce the risks posed to infrastructure systems by deterioration and future change. Current techniques can estimate future rates of deterioration that might lead to failure in transport infrastructure slopes, but are difficult to scale up, do not capture all drivers of deterioration relevant to all LLAs, are poor at dealing with uncertainty and heterogeneity, and lack rigorous validation against representative field data. Different asset owners have access to vast quantities of failure and condition data from their networks (recently enabled by technological advances in data capture and storage) but use different approaches to address failure based on historical data. ACHILLES proposes a research programme that brings these approaches together, coupled with statistical advances to enable rigorous use of network data, and economics to assess the value of design, monitoring and mitigation options. Our long-term vision is for the UK's infrastructure to deliver consistent, affordable and safe services, underpinned by intelligent design, management and maintenance. ACHILLES proposes a Programme to address this challenge by combining laboratory/field experimentation, numerical modelling and simulation, statistical data and cost benefit analysis, and activities to enable its outcomes to be adopted by LLA owners/operators:

Deeper understanding of material and asset deterioration and how to model and predict
New design tools to account for deterioration; and assessment tools to characterise
Strategies to mitigate deterioration from material to asset scale
Decision-making framework to prioritise spending on design, monitoring and/or interventions that accounts for heterogeneity and uncertainty, and informs appropriate business cases
Better understanding of the importance of characterising heterogeneity and uncertainty for infrastructure decision making processes
Knowledge and tools to incorporate data analytics into asset assessment and monitoring
StatusActive
Effective start/end date1/07/1831/12/22