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.
Highlights
Monitoring the structural performance of built assets is one of the primary tasks addressed by what is known as structural health monitoring (SHM), which is essentially an assessment of structural performance and damage identification (Farrar and Worden 2007)
SHM systems use sensors to measure parameters that indicate the structural performance of a built asset; these parameters are usually related to the loads that the structure is subject to and its response
Semantic web technologies have been increasingly used to enable communications in fragmented, heterogeneous, multinational business environments. This has led to the development of ifcOWL a sematic web technology that allows to encode Industry Foundation Classes (IFC) files in the W3C Ontology Web Language (OWL) (Beetz et al 2008). ifcOWL has proven to be very efficient to handle many types of data related to built assets (Pauwels and Terkaj 2016); but, it is not the best solution to describe geometrical data
Summary
Monitoring the structural performance of built assets is one of the primary tasks addressed by what is known as structural health monitoring (SHM), which is essentially an assessment of structural performance and damage identification (Farrar and Worden 2007). SHM systems use sensors to measure parameters that indicate the structural performance of a built asset; these parameters are usually related to the loads that the structure is subject to and its response. Loads caused by other conditions, e.g. loads caused by vehicle traffic on a bridge, have to be derived from parameters that measure the response of the structure to the applied loads. Interest is rapidly increasing within the construction industry for leveraging Big Data to support decision making (Bilal et al 2016); as it has been done before in –for example– the financial, marketing, healthcare, and manufacturing sectors (Hashem et al 2015). There are many challenges that limit the effective use of data in the construction and every other industry. The difficulties to compile, organise, and analyse data represent an obstacle to adopt advanced data-driven strategies (Lavalle et al 2011)
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