Investment needs for infrastructure maintenance are growing every year due to the increasing ageing of the infrastructure and the greater loads to which they are subjected. For example, in European countries, the annual maintenance expenditure grows 5% each year. Consequently, apart from ensuring safety, optimising the maintenance investment must be a priority for infrastructure managers as delaying investments only escalates the costs and risks of an aging infrastructure.
Nowadays most maintenance plans are designed based on the findings of traditional visual inspections. The effectiveness of these visual inspections depends entirely on the skills and experience of the bridge inspector, requiring that these defects have a visible manifestation. Ensuring that transmission of information to the evaluating engineer who is responsible for deciding on any necessary action is done as accurately and consistently as possible is also a critical factor.
Non-destructive techniques (NDTs) have been developed in recent years to try to widen the range of identifiable defects (i.e. both visible and non-visible). However, even if an inspector uses this equipment, the inspection is still characterized by the discreet nature of the measures that limits the usefulness of traditional visual inspections.
SHM (Structural Health Monitoring systems) arise as a response to the need for more frequent, or even continuous, information on the condition of the bridge.
Many bridges in the world are instrumented based on different objectives; however, the vast majority of these SHM systems are put in place for the sole purpose of monitoring a particular defect or deficiency, by installing specific sensors in the immediate vicinity of the problematic area. Thus, the data recorded by the sensors installed on the structure are processed and analysed to track a known problem.
Although SHMs have traditionally been considered as a tool to try to guarantee safety, it is important that they become a tool that allows optimal maintenance planning. For this, the system must provide information on the infrastructure at a global level, not only on specific sections. Thus, to capture information about the structural response and detect abnormal behaviours, the sensor information must be used to validate and calibrate numerical models of the bridge itself.
However, it is not just a matter of makings a model of the bridge, but it must also be designed in such a way that continuous updating is made from large heterogeneous data sets under a unified data structure, with the aim that the digital model behaves in the same way as the real structure.
When this is achieved, we refer to it as the creation of a Digital Twin of the bridge. This is precisely one of the fundamental pillars of our methodology, although as we will explain below, it is not the only one.
What exactly is a Digital Twin?
Digital Twin (DT) refers to numerical models that can represent the real behaviour of the structure during its useful life. Thus, we could say that they are a “living” digital simulation that brings together all the data and models, while updating itself from multiple sources to represent its physical counterpart.
Having this Digital Twin allows the simulation of more extensive load scenarios and damage patterns, while offering the possibility of drawing conclusions about the behaviour of the rest of the structure beyond the exact points where the sensors have been installed.
In fact, the typical challenge associated with the analysis of structural response of the bridge is the behaviour of the non-instrumented components, which is addressed using a fully detailed three-dimensional finite element model. Building validated structural models with the structural response data collected from a bridge develops a comprehensive database for reliable prediction of bridge performance under various traffic and environmental loads.
Finite Element Analysis (FEA) models should not be confused with Digital Twins. A characteristic that distinguishes these models from the Digital Twin is the presence of a bidirectional connection between a Digital Twin and its physical counterpart, by being continuously updated with operational data.
Therefore, the Digital Twin integrates highly reliable multi-physical and multi-scale models / simulations with SHM data, maintenance history and all available historical data to reflect the life of your physical twin.
Our SHM methodology: Digital Twins and advanced algorithms for predicting damage evolution
As part of our methodology we develop what we have defined as a Digital Twin, but we do not limit ourselves to that. Using specific deterioration models and real-time measurements, it is possible to update the prediction of the useful life of the most relevant elements of the structure.
In fact, the only way in which an SHM system can become a tool for the true prediction of behaviour is if it is associated with a mathematical model, both global and of the evolution of deterioration itself, integrating multiple physical models and based on simulation data to provide sufficient knowledge about the condition and carry out predictions based on different scenarios.
This is how we propose our methodology, seeking to extract greater value from the information in the bridge monitoring data sets and creating a Digital Twin of the bridges that is combined with probabilistic algorithms for prognosis, constituting an innovative SHM system.
Our opinion as experts
The development of efficient bridge maintenance techniques is increasingly necessary. In this sense, although SHM (Structural Health Monitoring) systems could not be considered a viable option for the majority of bridges a few years ago, the decrease in the cost of the sensors and the increase in computational power have allowed for wide-scale implementation to respond to growing needs.
However, the client must be aware that SHM technology has a much greater potential than what is currently being offered in the market, and it is important that they know the scope that is really being offered and to what extent it allows to correctly address their needs and how it be can integrated into their current practices.
A Digital Twin maintained throughout the life cycle of a bridge, by updating the model in order to maintain the correspondence of the structural response between the actual bridge and the Digital Twin model, and easily accessible at any time, provides the owner / manager of the bridge an early insight into the potential risk induced by aging / deterioration, and even extreme climatic events.
Through improvements in their functionality, SHMs can serve to carry out continuous and real-time assessment of the condition of the bridge, and also, thanks to our methodology, be able to predict future condition based on the evolution of the data in different scenarios.
We believe that it is necessary for Civil Engineering to advance in its digitization and rely more on data for decision-making. This can only be achieved by joining analytical approaches based on physical models and advanced data processing.
With the help of predictive analytics, big data, and machine learning-like approaches, SHM can be implemented on bridges to optimize performance, support infrastructure managers decisions, and empower predictive maintenance of bridges so that managers incorporate the concept of efficiency in the area of human, technical and financial resources used for maintenance purposes.
References
Agdas, D., Rice, J. A., Martinez, J. R., & Lasa, I. R. (2016). Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods. Journal of Performance of Constructed Facilities, 30(3), 04015049.
Kong, X., Cai, C. S., & Hu, J. (2017). The State-of-the-art on framework of vibration-based structural damage identification for decision making. Applied Sciences, 7(5), 497
Omenzetter, P. (2015, September). Frameworks for structural reliability assessment and risk management incorporating structural health monitoring data. In Proceedings of the 1st Workshop, COST Action TU1402: Quantifying the Value of Structural Health Monitoring (pp. 49-63). Cost European Cooperation in Science and Technology.
Sancho, A (2019). A predictive method for bridge health monitoring under operational condition. Modelling for Engineering & Human Behaviour 2019 (1 ed., Vol. 1, pp. 155-159, ISBN:978-84-09-16428-8 (2019)).