Monitoring surveys record and measure the movement or deformation of buildings and infrastructure (bridges, telecommunication towers and masts, dams, etc) across a broad range of projects. A monitoring scheme can help identify any potential issues within a structure and allow a plan of possible strengthening, modification, and repair schemes to be established, before further problems develop and progressive collapse occurs.

A high level of accuracy is required for movement to be as precise in sub-millimetres. Projects supply clients with real-time project data, ensuring that any problems are identified and communicated via SMS or email immediately, so that appropriate action can be undertaken on site.

Present day monitoring programmes generally fall into two distinct categories.

  • A fully automated monitoring scheme where monitoring technology (such as drones and wired and wireless sensors) continuously measure fixed points 24/7. The solutions combine the latest in monitoring technologies alongside cloud-based data hosting and reporting systems. The data can be reviewed and shared with the project teams through online secure portals. Alert systems can be installed to communicate when pre-determined tolerance levels are exceeded.
  • The second category is a manual monitoring scheme with site attendance on a regular basis to consistently measure the monitoring targets. The frequency of attendance is normally specified by the client at the start of the project but can change in line with evolving site activity. Ongoing set of data can be produced in spreadsheet format, including other data and outputs such as 3D digital models. This is compared with previous survey data to check for any noticeable trends or patterns in movement.
  • Typical health monitoring systems comprise of a network of sensors responsible for measuring different parameters relevant to the current state of the structure as well as its surrounding environment: such as stress, strain, vibration, inclination, verticality, humidity, and temperature.

1. Asset Condition Surveys

Asset condition surveys are ideal for assessing the impact of major infrastructure and construction projects on the wider built environment and the public. They can help establish whether there has been any consequential damage to neighbouring buildings or structures during the work, by carrying out pre- and post-construction surveys. Asset condition surveys should therefore be considered on major long-term infrastructure projects, which may take several years to complete and where there is a risk that claims may be made by adjacent asset owners after the project concludes.

Asset condition surveys can be completed by in-house specialist drone surveyors who fly the path of the development before construction commences and produce a vast range of photographs outlining the condition of existing buildings and other features. The photographs can be included in a detailed report which can then be referred to post construction, should any concerns be raised that the new construction led to structural issues such as cracking and excessive displacement. The reports should provide clear evidence that these problems were in place prior to the works commencing and can help avoid expensive and lengthy complaint or legal proceedings.

2. The Role of Finite Element Analysis (FEA) in Structural Health Monitoring (SHM)

Finite element analysis can used to perform analyses to complement the results from site survey, automated or manual monitoring. FEA can also be used to undertake back-analyses from limited data from experiments or predicting the physical characteristics of structures such as frequencies, mode shapes and using the test data for validation. FEA thus enables prediction of overall structural behaviour by expansion/extrapolation from the measurement data, once the model has been tuned by replicating the results of limited physical tests or measurements. It is thus possible to obtain all/additional data solely derived from FE calculated responses, after calibration with experimental results.

With machine learning, the finite element method can be used to generate input data for supervised learning using algorithms such as linear regression, Support-Vector Machines (SVM) and variations of neural networks such artificial neural networks (ANN), for prediction and classification (identification) of damage. The data derived from the FE model can be used to train the learning algorithm after being experimentally validated on a benchmark structure.

3. The Role of Digital Twins in Structural Health Monitoring

Digital twins enable visualization of the asset, track change, and perform analysis to better understand and optimize asset performance. Engineering Specifications, documents, drawings, models, analyses, geotechnical, and Original Equipment Manufacturer (OEM) specifications are necessary to develop digital twins. These can be complemented by monitoring data obtained during operations such as Internet of Things (IoT) feeds, Sensors, Drones, Cameras, Light Detection and Ranging (LiDAR), Point clouds. Digital twins are to be continuously updated with data from the physical asset. This data is used to understand and model the asset’s performance.

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