Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

There are approximately 70,000 masonry arch bridge spans on the UK road and rail networks (approx. 1 million spans worldwide), the vast majority of which are now well beyond the 120 year life usually expected of bridges. Though masonry arch bridges are in general considered long-lived structures, large numbers are now showing signs of distress. However, the cost of replacing these bridges in the UK alone would run into tens of billions of pounds, and their aesthetic and heritage value is also significant. Unfortunately the methods currently used to assess their capacity are antiquated and/or over-simplistic, making the task of prioritising renewal or refurbishment schemes extremely difficult (the still widely used MEXE method of assessment, which dates back to the 1940s, has very limited predictive capability and offers little scope for future enhancement). Weathering, continually increasing traffic volumes and factors such as the increased frequency of flood events brought about by climate change (affecting bridges over water) only serve to exacerbate the situation. Furthermore, although the primary focus of recent research has been on prediction of structural failure (the `ultimate limit state'), prediction of the level of service load above which incremental damage occurs (the `permissible limit state') is now a key priority for infrastructure owners, who are under increasing pressure to provide transport networks which are resilient. However, a significant barrier to delivering this using existing tools is that current assessment codes prescribe a fixed ratio between the ultimate and permissible load carrying capacities, which, given the diverse range of bridges in the field, is inappropriate and can lead to highly imprecise bridge assessments, and in turn to major economic implications.The present situation stems from our limited understanding of the 'real-world' behaviour of masonry arch bridges, which typically contain soil fill material surrounding and interacting with the arch barrel when loading is applied, and where both working (cyclic) and ultimate loading regimes are important. Developing an improved understanding of such behaviour is the main focus of this project. To achieve this, highly instrumented soil-arch interaction tests will be undertaken, with low-friction, clear sided, medium and full-scale test chambers and state-of-the-art Particle Image Velocimetry (PIV) techniques used to ensure a comprehensive and high quality experimental data-set is obtained. Test variables will include loading type (quasi-static vs. cyclic), bridge type (road vs. railway), fill material type and the presence or otherwise of near-traffic surface strong / stiff layers. Numerical modelling techniques and novel `system identification' techniques will be employed to ensure the full experimentally obtained data-set is used when validating the models developed. Finally, the ultimate objective is to use the improved understanding obtained to develop more rational assessment tools for use by engineers.

Walking (or cycling) around an area helps people to keep physically active in their daily life, reducing the risks of obesity and depression. Streets that are pleasant to walk along also provide opportunities for people to meet and chat with friends and acquaintances. This both enhances the quality of life and is good for health. Busy roads can deter people from going outside their home to socialise, walk or cycle because of noise or fear of injury. This can also lead to people deciding to avoid making trips, particularly if the alternative to crossing a busy road (e.g. an underpass) increases distances or is considered inaccessible, unsafe or unpleasant. People living on streets with heavy traffic know fewer neighbours and have fewer local friends than people living on streets with less traffic; people with fewer social contacts have worse physical and mental health and die younger. When people do not even try to cross roads because of traffic, they often cannot reach shops, health facilities, services, friends or family easily. This is called community severance (CS). All these effects are worse in older and other vulnerable groups, for whom mobility and social ties are fundamental to good health. This severance increases social inequalities and exclusion, leading to various economic and social costs. CS probably affects people's physical and mental health and wellbeing too, but this has not been studied very much. Studying health effects of community severance is challenging, as there are no agreed measurement methods that can be used easily and because this is a complex subject, crossing several areas of expertise. We will first study two residential areas to develop an in-depth understanding and measure of CS. We will ask local residents what is important to them. We will observe what happens in practice when older people try to walk around their neighbourhood. We will consider all this information in the context of the whole area, the levels and composition of road traffic and the way streets connect to each other. We will use the information we collect to develop ways to measure CS in three ways: (i) questions to individuals to assess the effects on them, (ii) how they value these impacts, and (iii) a measuring tool to estimate the extent of community severance due to particular types of roads or road layouts. We will then test these tools in two different residential areas. The main methods we will use are: community engagement, to explore perceptions and measures of CS and potential solutions; household-based surveys of travel behaviour, social networks, health and wellbeing; computerised surveys to elicit residents' values for severance and mitigation; on-street surveys of travel behaviour; measurement of traffic and road characteristics; space syntax methods, to study how the network of streets affect accessibility and mobility; and analyses integrating these discipline-specific methods. The final stage will be to test the impacts on CS - and thus on mobility and wellbeing - of proposed interventions to reduce CS. By the end of this project, we will have developed and tested three tools. The first two will be for local government to use, to model and to value levels of CS in their area. The third will be a set of questions that can be asked in surveys to find out whether and how severance affects local people. The survey can then be used by local communities, providing information they can use in discussions with local councillors and staff. The tools can be used by local government to test proposed transport policies, development plans and interventions to assess whether they will affect severance. They can also be used by researchers to find out whether and how CS affects people's mobility, social isolation, and short- and long-term health and wellbeing. The survey can also be used in national surveys so that a more complete picture of this problem is obtained across the UK.

The UK's road network totals around 250,000 miles of paved roads providing a means for efficient distribution of goods and services, supporting UK economic security and social prosperity, and this support will continue to be needed whatever the future of automated vehicle technology. The entire road network has been valued at £750 billion and as the UK's main transport infrastructure provides a vital service to road users, commerce and industry. However, road surface damage, particularly potholes, has become a serious safety and performance concern for all network users. The need to improve the quality, longevity and accessibility of the highway network is a vital concern of government, industry and the travelling public. It is highlighted by the recent dramatic increase in the number of cars taken in for repair of pothole-induced damage (up from 6.3M to 8.2M in two years according to a Kwik Fit survey, at an estimated annual cost to motorists of about £900M) and the maintenance backlog for local highway authorities, costed at £9.8B by the Asphalt Industry Alliance earlier this year. Episodes of severe weather in recent years (record-breaking rainfall, extreme cold-weather events), combined with tight financial constraints on highway authorities, have also led to a much publicised 'pothole epidemic', and the situation is made worse by the lack of longevity sometimes achieved in defect repairs. Against this background, this proposal has twin interrelated ambitions to (1) enable the design/construction of roads so as to minimise surface damage (i.e. prevention); and (2) induce a step change in the science of road repair (i.e. management). These ambitions can only be realised by establishing a level of understanding that does not currently exist within the pavement engineering community. This involves isolating, by both experimental studies and theoretical modelling, the real root causes of road surface damage - although it is well known that water and ice play vital roles. This knowledge has then to be combined with evaluation of actual road data in order to produce a robust and validated design and analysis tool and to generate appropriate construction and maintenance guidance. The research needed to successfully deliver these twin ambitions will require the combined effort and expertise of pavement engineers, materials scientists and computational fluid dynamics experts, expertise found at the University of Nottingham and Brunel University. In addition, the project will only be possible through the assistance of industrial partners with specific capabilities that will complement the academic input from Nottingham and Brunel. These comprise: three highway authorities (Highways England, Transport for London and Nottinghamshire County Council), giving access to data resources as well as direct field investigation opportunities; two umbrella organisations (ADEPT - representing local authority highways departments, RSTA - representing suppliers and contractors concerned with road surface treatments); and, finally, one producer of highway material test equipment (Cooper Technology), giving specialist input into test development.

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 different 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