Revolutionising Operational Safety and Economy for High-value Infrastructure using Population-based SHM (ROSEHIPS)
Lead Research Organisation:
University of Sheffield
Department Name: Mechanical Engineering
Abstract
Healthy infrastructure is critical in ensuring the continued health of UK society and the economy. Unfortunately, monitoring and maintaining our buildings and transport network is expensive. Considering bridges, inspection is usually carried out visually by human experts. There are not the resources to carry out the inspections as often as desired, or to make any repairs as quickly as needed; in the UK a backlog of maintenance works, identified in 2019, will cost £6.7bn. When resources are stretched, mistakes can be made, sometimes with tragic consequences; in 2018, despite warnings about possible problems, the Morandi Bridge in Genova, Italy, collapsed at a cost of 43 lives. Collapse is not the only problem; extreme weather events driven by climate change can test the performance of infrastructure beyond its limits e.g. consider the cost and inconvenience caused by bridge closures forced by flooding.
Bridges are only one concern. The offshore wind (OW) sector has driven down energy costs and increased power output, and now pioneers a global change to clean energy. The UK leads globally in OW energy, with ~8 GW of capacity, expected to exceed 25 GW by 2030, providing almost one third of the UK's annual electricity demand and helping meet the Climate Change Act's (2008) difficult 2050 target for an 80% cut in UK carbon output. The drive for turbines in deeper water demands new ways of asset management, decision making and controlling and limiting operation/maintenance lifetime costs. As turbines increase in numbers, size, and capacity, these issues become even more important.
The issues highlighted above are common across all elements of our infrastructure network (this PG will also consider telecoms infrastructure; another key test bed) and can be mitigated by automating the health monitoring. Instead of expensive, error-prone, human inspections, diagnoses can be provided economically by permanently-installed sensors, collecting structural data continuously and interpreting it via computer algorithms. This aim has led to the research discipline of Structural Health Monitoring (SHM), a subject of academic activity for over three decades. Despite intensive effort, SHM has not transitioned to widespread use because of a number of barriers - technical and operational.
The main technological barriers are: optimal implementation of hardware systems; confident detection in the face of confounding effects for in situ structures e.g. wind, traffic, for bridges; lack of damage-state data limiting the potential of machine learning for SHM. The operational barriers are: inertia - over-reliance on conservative design codes; trust - the SHM system must be as reliable as the structure itself; transparency - complex technology must deliver interpretable, secure decision support. The key to progress is to shift from thinking about individual structures to thinking about populations.
Population-Based SHM (PBSHM) is a game-changing idea, emerging in the UK very recently, with the potential to overcome the technological barriers above and transform our ability to automatically infer the condition of a structure, or a network of structures, from sensor data; this depends on an ability to collect a broader range of data, enriched into knowledge.
ROSEHIPS will extend and exploit PBSHM, developing machine learning, sensing and digital twin technology for automated inference of health for structures in operation now, and drive new standards for safer, greener structures in future. The Programme brings together the perfect team, mixing complementary skills in machine learning and advanced data analysis with expertise in new sensor systems and insight into complex infrastructure systems.
ROSEHIPS will provide open-source software systems, illustrated by realistic demonstrators and pre-populated with real-world data. Owners/operators will be able to customise and protect/secure their own data, while exploiting the knowledge base given.
Bridges are only one concern. The offshore wind (OW) sector has driven down energy costs and increased power output, and now pioneers a global change to clean energy. The UK leads globally in OW energy, with ~8 GW of capacity, expected to exceed 25 GW by 2030, providing almost one third of the UK's annual electricity demand and helping meet the Climate Change Act's (2008) difficult 2050 target for an 80% cut in UK carbon output. The drive for turbines in deeper water demands new ways of asset management, decision making and controlling and limiting operation/maintenance lifetime costs. As turbines increase in numbers, size, and capacity, these issues become even more important.
The issues highlighted above are common across all elements of our infrastructure network (this PG will also consider telecoms infrastructure; another key test bed) and can be mitigated by automating the health monitoring. Instead of expensive, error-prone, human inspections, diagnoses can be provided economically by permanently-installed sensors, collecting structural data continuously and interpreting it via computer algorithms. This aim has led to the research discipline of Structural Health Monitoring (SHM), a subject of academic activity for over three decades. Despite intensive effort, SHM has not transitioned to widespread use because of a number of barriers - technical and operational.
The main technological barriers are: optimal implementation of hardware systems; confident detection in the face of confounding effects for in situ structures e.g. wind, traffic, for bridges; lack of damage-state data limiting the potential of machine learning for SHM. The operational barriers are: inertia - over-reliance on conservative design codes; trust - the SHM system must be as reliable as the structure itself; transparency - complex technology must deliver interpretable, secure decision support. The key to progress is to shift from thinking about individual structures to thinking about populations.
Population-Based SHM (PBSHM) is a game-changing idea, emerging in the UK very recently, with the potential to overcome the technological barriers above and transform our ability to automatically infer the condition of a structure, or a network of structures, from sensor data; this depends on an ability to collect a broader range of data, enriched into knowledge.
ROSEHIPS will extend and exploit PBSHM, developing machine learning, sensing and digital twin technology for automated inference of health for structures in operation now, and drive new standards for safer, greener structures in future. The Programme brings together the perfect team, mixing complementary skills in machine learning and advanced data analysis with expertise in new sensor systems and insight into complex infrastructure systems.
ROSEHIPS will provide open-source software systems, illustrated by realistic demonstrators and pre-populated with real-world data. Owners/operators will be able to customise and protect/secure their own data, while exploiting the knowledge base given.
Organisations
- University of Sheffield (Lead Research Organisation)
- Translink (United Kingdom) (Project Partner)
- Department for Infrastructure (Project Partner)
- ETH Zurich (Project Partner)
- Cowi (Denmark) (Project Partner)
- Xilinx (Ireland) (Project Partner)
- Vattenfall (United Kingdom) (Project Partner)
- Arqiva (United Kingdom) (Project Partner)
- DYWIDAG-Systems International (UK) (Project Partner)
- Technical University of Denmark (Project Partner)
- Cellnex (UK) (Project Partner)
- Los Alamos National Laboratory (Project Partner)
- Politecnico di Milano (Project Partner)
- Sengenia (United Kingdom) (Project Partner)
- Siemens Gamesa (Project Partner)
- Devon County Council (Project Partner)
- Ferrovial (United Kingdom) (Project Partner)
- KU Leuven (Project Partner)
Publications
Wang M
(2024)
Real-time displacement measurement for long-span bridges using a compact vision-based system with speed-optimized template matching
in Computer-Aided Civil and Infrastructure Engineering
Wang M
(2022)
Completely non-contact modal testing of full-scale bridge in challenging conditions using vision sensing systems
in Engineering Structures
Brennan DS
(2022)
On the application of population-based structural health monitoring in aerospace engineering.
in Frontiers in robotics and AI
Stihi A
(2024)
On Gait Consistency Quantification Through ARX Residual Modeling and Kernel Two-Sample Testing
in IEEE Transactions on Biomedical Engineering
O'Higgins C
(2024)
A method to maximise the information obtained from low signal-to-noise acceleration data by optimising SSI-COV input parameters
in Journal of Sound and Vibration
Giglioni V
(2024)
A domain adaptation approach to damage classification with an application to bridge monitoring
in Mechanical Systems and Signal Processing
Ferguson A
(2024)
Sampling methods based on expected traffic-volume information for long-term rotation-based bridge SHM in resource-constrained environments
in Mechanical Systems and Signal Processing
Tsialiamanis G
(2023)
Towards a population-informed approach to the definition of data-driven models for structural dynamics
in Mechanical Systems and Signal Processing
Bunce A
(2023)
A robust approach to calculating bridge displacements from unfiltered accelerations for highway and railway bridges
in Mechanical Systems and Signal Processing
Dardeno T
(2024)
On the hierarchical Bayesian modelling of frequency response functions
in Mechanical Systems and Signal Processing
Tsialiamanis G
(2024)
On a meta-learning population-based approach to damage prognosis
in Mechanical Systems and Signal Processing
Arnaud Vadeboncoeur
(2023)
Article
in Random Grid Neural Processes for Parametric Partial Differential Equations
O'Higgins C
(2023)
Minimal Information Data-Modelling (MID) and an Easily Implementable Low-Cost SHM System for Use on a Short-Span Bridge
in Sensors
Ferguson A
(2022)
Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers
in Sensors
Haywood-Alexander M
(2022)
A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves.
in Sensors (Basel, Switzerland)
Poole J
(2022)
On statistic alignment for domain adaptation in structural health monitoring
in Structural Health Monitoring
Smith S.M.
(2023)
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Bee S.C.
(2023)
When is an SHM Problem a Multi-Task-Learning Problem?
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Papatheou E.
(2023)
On the Use of Model-Based Versus Data-Based Approaches for Virtual Sensing in SHM
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Delo G.
(2023)
On the Influence of Structural Attributes for Assessing Similarity in Population-Based Structural Health Monitoring
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Dardeno T.A.
(2023)
Hierarchical Bayesian Modelling of a Family of FRFs
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Brassington A.
(2023)
Detection, Localisation, and Quantification of Bolt Looseness in an Aluminium Plate Using Lamb Wave Analysis
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Poole J.
(2023)
Physics-Informed Transfer Learning in PBSHM: A Case Study on Experimental Helicopter Blades
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Clarkson D.R.
(2023)
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Lindley C.A.
(2023)
Acoustic Emission Source Location Using Bayesian Optimisation for a Composite Helicopter Blade
in Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
Description | Model Validation & Uncertainty Analysis Sandpit |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Discussion day based on the following - In any attempt to mathematically model an engineering structure, a crucial part of the exercise must be to assess if the model is fit for purpose; this is the process of model validation. If this process is conducted properly, it will also account for any uncertainties in the model itself and in any data used for calibration. The University of Sheffield Laboratory for Verification and Validation (LVV) was conceived and constructed as a unique facility for model validation across scales and environments. The purpose of this sandpit is to introduce the LVV in a workshop environment, where attendees can examine the facilities available and discuss how those facilities can best serve their needs and those of the wider dynamics community. |
Year(s) Of Engagement Activity | 2023 |