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Abstract
The long-term management of critical infrastructure requires moving beyond costly manual inspections to the adoption of continuous structural health monitoring(SHM) systems. This project successfully developed a necessary link between SHM sensor data and a building information model(BIM). The aim was to create a workflow that processes large sensor data to determine structural performance and automate the process of updating the processed data into the corresponding digital elements with the BIM environment. The data collection from the case study was done using a locally made cost-effective accelerometer sensor. The processing of the raw sensor data was carried out using Python script to determine the peak lateral frequencies at ambient and loaded conditions, as well as the percentage shift in the frequency, which indicated a percentage shift in structural
stiffness. Virtual sensors were created in the model at the location of the sensor during data collection. With the aid of Dynamo in Revit software, the processed data were automatically imported to the correct element within the model, instead of manually inputting them. At ambient conditions, the bridge deck and staircase had a peak lateral frequency of 0.467Hz and 0.867Hz respectively, while at pedestrian loading conditions, the bridge deck and staircase had a peak lateral frequency of 0.413Hz and 0.829Hz respectively. This processed data showed that the lateral frequency of the bridge deck and staircase decreased
by 11.6% and 4.38% respectively, indicating a 21.85% and 8.57% reduction in the effective stiffness of the elements respectively. The solution will improve large sensor data management by reducing data interpretation time. This allows structural engineers and stakeholders to gain faster, more confident insights needed for timely maintenance decisions and ensuring structural safety.
stiffness. Virtual sensors were created in the model at the location of the sensor during data collection. With the aid of Dynamo in Revit software, the processed data were automatically imported to the correct element within the model, instead of manually inputting them. At ambient conditions, the bridge deck and staircase had a peak lateral frequency of 0.467Hz and 0.867Hz respectively, while at pedestrian loading conditions, the bridge deck and staircase had a peak lateral frequency of 0.413Hz and 0.829Hz respectively. This processed data showed that the lateral frequency of the bridge deck and staircase decreased
by 11.6% and 4.38% respectively, indicating a 21.85% and 8.57% reduction in the effective stiffness of the elements respectively. The solution will improve large sensor data management by reducing data interpretation time. This allows structural engineers and stakeholders to gain faster, more confident insights needed for timely maintenance decisions and ensuring structural safety.
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