Abstract:
This report presents a unique approach to Structural Health Monitoring (SHM) using Artificial
Neural Networks (ANNs) and Building Information Modelling (BIM), designed to provide
real-time damage indices as indicators of structural health. First and foremost, this research is
aimed at developing resilient infrastructure in regions susceptible to natural calamities,
specifically earthquakes and floods. The approach presented herein integrates advanced sensor
technology and machine learning to innovate traditional SHM methods.
The study first explores the data acquisition process, where an ADXL345 sensor is employed
to collect acceleration data from a structure. The raw data undergoes various stages of feature
extraction including detrending, filtering, Fourier Transform, frequency extraction, and
calculation of mode shape coefficients. The processed data then feeds into an ANN model,
which predicts the damage index, an essential parameter in SHM.
The validity and robustness of the proposed methodology were confirmed through
comprehensive validation, involving a case study of a bridge. Furthermore, the SHM system's
integration with Autodesk Revit software allows for intuitive visualization of damage indices
on the building model, thus paving the way for informed decision-making.
In summary, this research has laid down a strong and versatile foundation for real-time SHM.
While the focus in this research has been restricted to buildings and bridges, the developed
methodology and system are adaptable to other structures, marking a significant advancement
in the future of SHM.
