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Cambridge Centre for Smart Infrastructure and Construction

An Innovation and Knowledge Centre funded by EPSRC and Innovate UK

Studying at Cambridge

 

Managing and operating infrastruture

Current 'Managing and Operating Infrastructure' projects:

1. Bridge inspector – an automated approach to help prioritise bridge inspection programmes

2. Data-driven asset management – a framework for linking ISO and BIM standards for whole-life value

3. Sustainable design and management of industrial assets

4. Next generation converged digital infrastructure (NG-CDI)

5. Optimal monitoring of a masonry arch skew bridge

6. Automating the visual inspection process for masonry arch bridge condition monitoring

7. Satellite monitoring for remote structural monitoring of infrastructure

8. Predictive maintenance of bridges 

9. Infrastructure information management

The following projects also appear under 'The Use and Development of Sensors'

10. Settlement monitoring of heritage structures during Bank station capacity upgrade

11. Tram vibration and impact monitoring to lower maintenance costs and improve public acceptance

 

 

1. Bridge inspector – an automated approach to help prioritise bridge inspection programmes

Manual bridge inspection is laborious, subjective, incomplete and costly. Existing research methods for automated bridge inspection are able to detect one class of damage type based on images. CSIC is developing a multiclass approach that also considers the 3D geometry (as inspectors do) using high-resolution images of the structure, which are automatically mapped on to 3D models of the respective bridge. The three-step approach mimics the stages of a visual inspection using a machine learning classifier to separate visually intact concrete from irregular patterns. It then classifies the irregular findings into a defect category before additional information, for example the orientation or location of a crack, is used to refine the type of defect. Engineers can inspect and assess the bridge from their office (remotely) as if they were standing on site in front of the actual asset. Project contact is Laing O’Rourke Lecturer in Construction Engineering and CSIC Co-Investigator Dr Ioannis Brilakis.

 

2. Data-driven asset management – a framework for linking ISO and BIM standards for whole-life value

As the world of BIM L 3, Digital Built Britain (DBB) and UK Digital Economy beckons, there will be a strong focus of data driven construction solutions. This research project, in partnership with Costain, will progress the use of BIM as the cornerstone of information management for asset maintenance and management (BIM levels 3 and 4).  The information required for through-life management and a method to link this to the BIM model to provide a fully integrated asset management platform will be identified for a given asset type.

The aim of the project is to create a model based framework approach to aid in the development of whole-life asset information requirements (AIR) linking the BIM 1192 standards with the ISO 55000 standards (Figure 1).  A tool will be built that can automaticity link AIR to Uniclass 2015 - a unified classification system for the construction industry. Uniclass 15 contains consistent tables classifying items of all scale from a facility such as a railway through to products such as a CCTV camera in a railway station. The asset information model will be validated to the organisation information requirements and objectives.  Project contacts are Dr Ajith Parlikad, Senior Lecturer in Industrial Systems at the Institute for Manufacturing and CSIC Investigator, and PhD Researcher James Heaton.

 

3. Sustainable design and management of industrial assets

This project aims to develop a framework that helps industries understand and manage the whole-life cost and value generated by their assets. Leading companies in the infrastructure and manufacturing sectors are developing an awareness of most of the costs incurred throughout an asset’s lifecycle. Some companies use Total Cost of Ownership (TCO) as a key metric to support procurement, operations and maintenance decisions. There is also increasing emphasis on extracting the maximum value from the assets, instead of thinking only about cost. The importance of value maximisation in physical asset management is currently accepted, in particular because of the ISO family of standards on asset management ISO 5500X. However, there is a distinct lack of clarity and understanding of what value means, how to identify and quantify value, and how to base decisions on it.

The purpose of the project is to study the evolution of Total Value of Ownership (TVO) and TCO as used in industry and understood by academics, with the aim to develop a framework for understanding, quantifying, and using TVO/TCO for decision-making. The aim is to inform industry how the concept of TVO/TCO can bring a positive step-change to the effectiveness of asset management.  Project contacts are Dr Ajith Parlikad, Senior Lecturer in Industrial Systems at the Institute for Manufacturing and CSIC Investigator, and Prof Duncan McFarlane, Professor of Industrial Informational Engineering at the Institute for Manufacturing and CSIC Investigator. 

 

4. Next generation converged digital infrastructure (NG-CDI)

This project is part of the NG-CDI Prosperity Partnership that seeks to develop next-generation data-driven methods and technologies for the resilient, autonomic digital infrastructure of the future. It will forge converged digital infrastructure for the UK, creating a radically new data-driven architecture for autonomous operation of future telecommunications infrastructure, with capability to adapt and respond to new ICT services.

Led by CSIC Co-Investigators, who are part of the University’s Distributed Information and Automation Laboratory (DIAL), the project brings expertise in Internet of Things (IoT) and cyber physical systems in the manufacturing domain to the digital telecommunications infrastructure and is synergetic with CSIC’s research in smart infrastructure. The focus will be to develop a multi-agent architecture and to develop decision-support algorithms and strategies for predictive asset management. Organisational capabilities to fully exploit the potential of these new technologies will also be developed. Project contact is Dr Ajith Parlikad, Senior Lecturer in Industrial Systems at the Institute for Manufacturing and CSIC Investigator.

 

5. Optimal monitoring of a masonry arch skew bridge

Many ageing masonry bridges are in need of continuous assessment; this is typically completed through visual inspection, though in some cases crown displacements are monitored. This project seeks to monitor a specific masonry arch skew railway bridge with several techniques, with the specific aim of identifying pros and cons of various monitoring techniques for masonry bridge typologies. Specifically, CSIC, in collaboration with AECOM and Network Rail, will monitor a masonry arch skew bridge that has experienced severe distress. Numerous sensors will be deployed, including fibre-optic sensing and videogrammetry. 

The data will first be used to reveal the in-plane flow of force through the skew arch, which until now has not been well understood.  Second, local strain and displacement measurements will be correlated to global (3D) movements obtained from fibre-optic and videogrammetry monitoring. This will facilitate the decision-making on how to optimally monitor to detect the specific service performance that may be a concern. Interpretation of these results will be complemented by modelling of the bridge, so that the monitoring data can be benchmarked against currently applied modelling techniques.  Project contacts are CSIC Investigator Dr Matthew DeJong, and PhD Researcher Sam Cocking

 

6. Automating the visual inspection process for masonry arch bridge condition monitoring

Currently, the condition of masonry arch bridges is predominantly assessed via manual visual inspection. With the collection of image and laser scan data becoming increasingly fast, there is potential to automate this process. This would help to reduce the subjectivity of the current manual methods as well as reducing cost and improving safety. The automated process being developed seeks to combine image and laser scan data to detect and identify defects in the structure, and to determine their severity. The defects identified will then be used to infer the cause and mechanism of damage in the bridge. In this way, the severity and structural implication of the defects and deformations can be determined to more accurately assess the current condition and capacity of the bridge.  Project contacts are CSIC Investigator Dr Matthew DeJong, and PhD Researcher Daniel Brackenbury

 

7. Satellite monitoring for remote structural monitoring of infrastructure

The widespread deterioration and recent collapses of bridges, dams, tunnels and other key services have highlighted the importance of structural health monitoring as a tool to aid infrastructure asset owners and managers.  This research investigates advances in satellite measurement technologies and understand their relevance, utilisation, and limitations to civil engineering applications, such as bridge or embankment monitoring. These data sets are then compared with traditional measurement techniques from sensors installed on existing bridges (including fibre optic sensors, wireless sensor networks, traditional surveying techniques etc.). From this, they can be complemented with traditional measurement techniques to provide a more effective strategy for interpreting data to provide useful information and value to asset owners.  This research aims to address the following research questions:

- Can satellite measurement technology provide remote measurement and monitoring such that it is able to replace or complement traditional forms of physical measurement and monitoring infrastructure assets? 

- Can satellite measurement and imagery be used to indicate signs or precursors of failure in infrastructure assets?

The primary method of satellite measurement used in this research is Interferometric Synthetic Aperture Radar (InSAR) which has the capability to provide wide-area, high-density, remote measurements of movement. Situations for which satellite measurement technology could potentially be useful include identifying impending structural failures by detecting small movements in advance of, or prior to, collapse; identifying unusual movement that would indicate potential problems (e.g. seized bridge bearings); and, looking at bridges and other assets before and after significant events (e.g. flooding). Project contact is PhD Researcher Sakthy Selvakumaran

 

8. Predictive maintenance of bridges 

Complex industrial assets such as bridges, power transformers etc. are subject to complex deterioration processes. Understanding such complexities, and maintaining such assets, is a major challenge. This research aims to design a novel approache for optimising condition-based maintenance policies for assets with complex deterioration processes. These policies aim to optimise the inspection and replacement plans for such assets and their constituent components so as to minimise the costs and risks involved, while maintaining performance. Working with a local authority, CSIC assessed the effectiveness of the approach on a road bridge system. At the bridge level, detailed deterioration models were formulated for different components of bridges under a range of exposure levels.  At the system level, the economic dependence and two different levels of structure dependence are considered.  The timing of maintenance activities is then optimised for the system based on current predictions for both risk and cost, with a goal of reducing the traffic management cost by combining maintenance activities. The result showed that significant maintenance costs (approx. 10 per cent) can be saved by implementing this approach.  Project contacts are Dr Ajith Parlikad, Senior Lecturer in Industrial Systems at the Institute for Manufacturing and CSIC Investigator, and Research Associate Dr Zhenglin Liang

 

9. Infrastructure information management

The critical importance of information management, processes and strategies is gaining momentum within the wider construction industry. This is being guided by new and emerging technologies, with the support of industry standards solely focused within Building Information Modelling (BIM) and Asset Management. As BIM adoption, implementation and development becomes more prolific in industry, asset owners are seeking to use the newly found information to achieve whole-life cycle performance efficiencies from their physical assets. The development of this information is representing a paradigm shift, where information is no longer created and used for a single purpose. Information can be transferable and used within different life-cycles, e.g. the same datasets used in the construction phase can be used within the operational phase, allowing for new and innovative ways to apply the data. However, currently the data-sets generated are usually held in disparate and incoherent platforms. Such data-sets are generated with multiple enterprise software developments using an array of standards and format types. As a result, the optimum value from the information is often not fully realised. 

The focus of this research is on developing tools for integrating different data sources to support whole-life asset management of infrastructure assets and systems. This will involve developing BIM models of selected structures and spaces, identifying data requirements for asset management, defining asset information models and integrating such data with the BIM models. We will work with our industry partners such as English Heritage to understand their requirements, identify specific case studies and demonstrator projects, and develop generalizable tools and guidelines for asset information management. The longer-term goal of this activity is to support the Digital Built Britain’s Level 3 and Level 4 agenda. Project contact is Dr Ajith Parlikad, Senior Lecturer in Industrial Systems at the Institute for Manufacturing and CSIC Investigator.  

 

10. Settlement monitoring of heritage structures during Bank station capacity upgrade
CSIC is applying new-generation sensing techniques, including fibre optic strain sensing, point cloud and satellite displacement monitoring, to monitor the structural response of heritage buildings during tunnelling work for the Bank station capacity upgrade

The proposed tunnels are in close proximity to Christopher Wren’s St Mary Abchurch and George Dance’s Mansion House. There are significant uncertainties regarding the behaviour of the ground and building during the tunnelling works taking place between 2017 and 2021, making monitoring a necessary mitigation measure. Sensing data will be used to provide a critical assessment of analysis methods for tunnelling-induced damage in historic buildings and offer reassurance to asset owners and managers. Detailed data will allow informed assessment and timely intervention, if necessary, to avoid potential costly remedial action. Project contacts are Brunel Research Fellow Dr Sinan Açikgöz and CSIC Investigator Dr Matthew DeJong.

 

11. Tram vibration and impact monitoring

The Vibration and Impact Monitoring of Tram Operations (VIMTO) project aims to develop a vehicle-based automated system for track monitoring that may be permanently installed on service trams running on an operational network. This novel form of track vibration monitoring uses the trams themselves as the primary monitoring instrument. Low-cost instrumentation mounted on tram axle-boxes records vibration signatures simultaneously with positioning data to produce a map of the network in terms of its propensity to generate ground-borne vibration. The map offers real-time continuous monitoring of the network allowing asset managers to observe the rate of deterioration. This data is key to the formulation of an optimised maintenance strategy that eliminates costly track failures.

There are more than 290km of modern tramway in the UK alone, the majority of which runs through densely populated urban areas. The disturbance to building occupants caused by tram-generated ground-borne vibration presents a significant barrier to the expansion of tram networks in our cities. Improved track monitoring leading to lower maintenance costs and increased public acceptability of trams directly supports the expansion of this more environmentally sustainable mode of transport in both domestic and international markets.  Although the current focus of VIMTO is on tram operations, the proposed monitoring methodology is broadly applicable to other rail transport systems, including underground railways. Project contact is CSIC Investigator Dr James Talbot.