Infrastructure is a large part of the UK's assets. Efficient management and maintenance of infrastructure are vital to the economy and society. The application of emerging technologies to advanced health monitoring of existing critical infrastructure assets will quantify and define the extent of ageing and the consequent remaining design life of infrastructure, thereby reducing the risk of failure. Emerging technologies will also transform the industry through a whole-life approach to achieving sustainability in construction and infrastructure in an integrated way - design and commissioning, the construction process, exploitation and use, and eventual de-commissioning. Crucial elements of these emerging technologies will be the application of the latest sensor technologies, data management tools and manufacturing processes to the construction industry, both during infrastructure construction and throughout its design life. There will be a very substantial market for exploitation of these technologies by the construction industry, particularly contractors, specialist instrumentation companies and owners of infrastructure.In this proposal, we seek to create the Innovation and Knowledge Centre for Smart Infrastructure and Construction that will bring together four leading research groups in the Cambridge Engineering Department and the Computer Laboratory (sensors, computing, manufacturing engineering and civil engineering), along with staff in other faculties - the Judge Business School and the Department of Architecture. The Centre will develop and commercialise emerging technologies which will provide radical changes in the construction and management of infrastructure, leading to considerably enhanced efficiencies, economies and adaptability. We propose to create 'Smart Infrastructure' with the following attributes: (a) minimal disturbance and maximum efficiency during construction, (b) minimal maintenance for new infrastructure and optimum management of existing infrastructure, (c) minimal failures even during extreme events (fire, natural hazards, climate change), and (d) minimal waste materials at the end of the life cycle. The IKC will focus on the innovative use of emerging technologies in sensor and data management (e.g. fibre optics, MEMS, computer vision, power harvesting, Radio Frequency Identification (RFID), and Wireless Sensor Networks). These will be coupled with emerging best practice in the form of the latest manufacturing and supply chain management approaches applied to construction and infrastructure (e.g. smart building components for life-cycle adaptive design, innovative manufacturing processes, integrated supply chain management, and smart management processes from building to city scales). It will aim to develop completely new markets and achieve breakthroughs in performance.The business opportunities in construction and infrastructure are very considerable, not only for construction companies but also for other industries such as IT, electronics and materials. The IKC is designed to respond directly and systematically to the input received from industry partners on what is required to address this issue. Through the close involvement of industry in technical development as well as in demonstrations in real construction projects, the commercialisation activities of emerging technologies will be progressed during the project to a point where they can be licensed to industry. The outputs of the IKC will provide the construction industry, infrastructure owners and operators with the means to ensure that very challenging new performance targets can be met. Furthermore the potential breakthroughs will make the industry more efficient and hence more profitable. They will also give UK companies a competitive advantage in the increasingly global construction market.

Sensors are everywhere, facilitating real-time decision making and actuation, and informing policy choices. But extracting information from sensor data is far from straightforward: sensors are noisy, prone to decalibrate, and may be misplaced, moved, compromised, and generally degraded over time. We understand very little about the issues of programming in the face of pervasive uncertainty, yet sensor-driven systems essentially present the designer with uncertainty that cannot be engineered away. Moreover uncertainty is a multi-level phenomenon in which errors in deployment can propagate through to incorrectly-positioned readings and then to poor decisions; system layering breaks down when exposed to uncertainty. How can we be assured a sensor system does what we intend, in a range of dynamic environments, and how can we make a system ``smarter'' ? Currently we cannot answer these questions because we are missing a science of sensor system software. We will develop the missing science that will allow us to engineer for the uncertainty inherent in real-world systems. We will deliver new principles and techniques for the development and deployment of verifiable, reliable, autonomous sensor systems that operate in uncertain, multiple and multi-scale environments. The science will be driven and validated by end-user and experimental applications.

Project partners: Transport Scotland, Scottish Water, SSE, Atkins and ARU Partners' Challenge: The increased frequency and magnitude of environmental hazards in Scotland, in the last decade, has resulted in widespread failures in CI networks and their interdependent systems (e.g., devastating impacts of 2002 Glasgow flooding and 2013 storm on Carradale and Kintyre). The challenge currently faced by our industry partners are the lack of shared understanding of interdependencies and interoperability and a robust resilience-informed DSS. To tackle this challenge, this project will adopt recently developed Resilience and Vulnerability-based DSS (RV-DSS) to map interdependent network critical components for a real case study in Scotland. Aims and Objectives: This project aims to adopt a newly developed DSS to model infrastructure interdependencies of three CI networks (Water, Transport, Energy) providing a measure of network resilience in response to hazardous events, in addition to measure of vulnerability. Broken down our objectives are to: 1. Apply the RV-DSS to a real case study, North Argyll, providing resilience and vulnerability-informed management strategies and a comparative CBA of pre/post-utilisation of RV-DSS 2. Refine the RV-DSS based on lessons learnt on case study and stakeholders comments. 3. Provide a guideline on building a case study for RV-DSS and engaging stakeholders in utilisation of RV-DSS Project Plan: The proposed project consists of three Work Packages (WP), three Deliverables (D), two Reports (R), two Workshops (WS) and six Consortium Meetings (M) details of which are summarised below. WP1 will cover the overall management and progress of the project. This includes organising six consortium meetings and monthly summary-reports, providing project partners with progress being made. This WP will produce two technical reports: R1. Interim report containing project progress, due on Month 3 and R2. Final innovation report: a comprehensive report on project findings and a comparative CBA of pre and post RV-DSS analyses, due on Month 6. The overall objective of WP2 is to adopt RV-DSS to North Argyll case study, assessing the resilience of transport, water and energy network in response to failure propagation induced by infrastructure interdependencies. This WP will produce two deliverables: D1. Delivering interdependent asset register map for North Argyll, a central data repository for all assets and their functional and physical links in energy, transport and water networks located in North Argyll, in a form of a GIS map (Due on Month 4). D2. Delivering resilience and vulnerability assessment report of North Argyll interdependent infrastructure system, an exemplar of RV-DSS application (Due on Month 6). WP3 directs lessons learnt from WP2 and stakeholders comments during planned workshops and consortium meetings to refine the RV-DSS to better suit project partners' needs. As part of this WP, a manual for RV-DSS and a guideline on building a case study for RV-DSS will be delivered (D3, Due on Month 6) Benefits for Project partners: The refined RV-DSS alongside with its manual produced as part of this project can in general help all industry partners to manage future uncertainties in their long-term infrastructure investment decisions. D1 and D2 will provide means of updating current integrated infrastructure design, maintenance and operation methods in North Argyll. D3 will extend the application of these findings to a general case study. The industry-tuned RV-DSS can support project partners in developing an understanding of risk propagation through their systems and also prioritisation of asset management strategies at an organisational level. Additionally, it will play a crucial role in the promotion of road & rail, water and energy industries' services, management and maintenance strategies. Duration and Total Cost: The project is a 6-month project with the total estimated 80% FEC of £62,339.

This co-designed project with Highways England will reduce the ice hazard on motorways. As part of a nationwide trial of smart motorways, 'all lane' and 'hard shoulder running' are being used to increase capacity on critical sections of the GB motorway network. However, this approach, along with traditional road widening, has implications for winter road maintenance strategy. Significant surface temperature differences exist between lanes on motorways and it is not uncommon to find a difference of 2C between the inside and outside lane of the motorway. For example, in the case of hard shoulder running, differences between the inside lane and the previously un-trafficked hard shoulder of the motorway will be significant. A consequence of this, is when the hard shoulder is first opened to traffic (e.g. during the morning rush hour), an ice hazard may exist on this lane, whilst the other lanes remain above freezing. There is a need to better understand, and monitor, these differences in temperature so that it can be incorporated into the winter maintenance strategy used by Highways England. This project will translate existing technology developed on previous research projects to quantify the temperature difference between motorway lanes. The data will be available in real time, using an 'internet of things' approach alerting engineers of the need to conduct gritting operations on cooler lanes. Hence, this project will not only impact on the actions of the project partners (potentially saving money on gritting operations), but will also improve the safety of the network for all road users. The project is particularly timely given the ongoing inquiry into 'all lane running' by the commons select transport committee. The overall aim of this project is to produce a prototype decision support system that will provide information regarding the surface temperature of all motorway lanes which can be used to make efficiency savings as well as being consulted prior to opening the hard shoulder to traffic. This aim will be realised by the following objectives: 1. Installation of transects of networked low-cost sensors across motorway gantries, providing surface temperature information across all lanes. 2. Development of a cloud based data hub to process and visualise data 3. Integration of data into existing winter maintenance / smart motorways strategies. This short project has two key deliverables. Firstly, the project will provide tacit knowledge of temperature differences on multi-laned roads. This information can then be used inform winter maintenance strategy more generally across the network without significant further investment. Secondly, the prototype decision support system will provide detailed and reliable real-time measurements on the selected road section leading to efficiency savings for the project partner. This approach could readily be extended across the network, given the compatibility of this approach with the 'smart motorway' approach. The project will last for 7 months to cover a substantial part of the winter season (November 2016 - May 2017) and will cost in the region of £70k (£56k 80% FEC).

When storms cause trees to fall onto power lines, roads and railways this can pose serious threats to human life and disruption to electricity supplies and transport leading to large financial costs to both operators and users of these networks. There is growing evidence that climate change will lead to an increase in storminess in the UK so the problems associated with tree failure are likely to grow. This project aims to use a computerised system for predicting which trees are likely to fall onto powerlines, roads and railways during the types of storms that typically occur in the UK. This will allow the operators of these infrastructure networks to fell those trees that are most likely to fail and cause disruption. The project will make use of newly-developed techniques which employ airborne laser scanners to map trees and measure their key properties, which will improve our ability to estimate the susceptibility of trees to failure. So the outputs of the system will enable the operators of infrastructure networks to pro-actively manage trees in order to improve the resilience of their infrastructure to future storms. The intensity and impacts of storms vary considerably over space and time so it is not possible to manage trees for all possible conditions. Therefore we will develop the tree failure prediction system so that it is able to use short-range weather forecasts (up to 5 days) which are the most reliable predictions of impending storm events. This will enable the system to predict which trees are likely to fail and cause disruptions to infrastructure networks during the forthcoming storm conditions. This information will help the network operators to draw up effective plans for responding to and recovering from storms, e.g. by organising field teams to be in the locations where greatest tree damage is likely to occur so they can remove fallen debris and repair the infrastructure. To be effective our tree failure prediction system will need to operate quickly and repeatedly so it can respond to regular updates in weather forecasts as storms develop. Also it needs to incorporate assessments of the large number of trees which surround the power, road and rail networks in the UK. Therefore, to achieve this, we will make use of the very powerful cloud-based computing technology that is now rapidly developing. The outputs of our system will be conveyed to users via an interactive web page which will support strategic decision-making and a mobile app that will support field teams. Keywords: tree failure, storm, prediction, power supply, road, rail, decision-making, resilience. The following organisations are stakeholders in the project and will form an advisory board to oversee our work: UK Power Networks, Scottish Power, Transport Scotland, Scottish Water, Bluesky International, Atkins Global.