Structural Performance Monitoring Using a Dynamic Data-Driven BIM Environment

Abstract

Structural health monitoring data has not been fully leveraged to support asset management due to a lack of effective integration with other datasets. A Building Information Modelling (BIM) approach is presented to leverage structural monitoring data in a dynamic manner. The approach allows for the automatic generation of parametric BIM models of structural monitoring systems that include time-series sensor data; and it enables data-driven and dynamic visualisation in an interactive 3D environment. The approach supports dynamic visualisation of key structural performance parameters, allows for the seamless updating and long-term management of data, and facilitates data exchange by generating Industry Foundation Classes (IFC) compliant models. A newly-constructed bridge near Stafford, UK, with an integrated fibre-optic sensor based monitoring system was used to test the capabilities of the developed approach. The case study demonstrated how the developed approach facilitates more intuitive data interpretation, provides a user-friendly interface to communicate with various stakeholders, allows for the identification of malfunctioning sensors thus contributing to the assessment of monitoring system durability, and forms the basis for a powerful data-driven asset management tool. In addition, this project highlights the potential benefits of investing in the development of data-driven and dynamic BIM environments.

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“…Figure 1 shows the completed bridge and its sensor instrumentation arrangement. Real-Time Sensor Data Visualization Using BIM SHM datasets were incorporated into a dynamic BIM environment for visualizing real-time sensor data and associated bridge behavior [7], [8]. Specifically, this can be used to help identify anomalies in the data (due to e.g.…”

“…The case study considered two railway bridges in Staffordshire, UK which have pervasive sensor networks installed at the time of construction. The study involves an interdisciplinary collaboration between the Cambridge Centre for Smart Infrastructure and Construction (CSIC) and the Alan Turing Institute (ATI), combining the expertise of bridge monitoring [4], finite element modeling [5], [6], building information modeling (BIM) [7], [8] and statistical modeling [9], [10], with the end objective of creating a working digital twin for the instrumented Staffordshire bridges.…”