Structural characterization of a suspension bridge by mapping the temperature effects on strain response based on neural network models

Mapping the structural responses based on main loads to characterize signature of complex structures with high-dimensional features is a determinant factor for structural health monitoring (SHM). Current technological advances contribute to the optimization of data analysis, aiming to make the process less demanding in terms of time and computational demand. Machine learning (ML) models became popular due to their capacity to estimate structural behaviour based on the measurements gathered by the SHM systems. This work proposes a methodology supported by Neural Networks (NN) for the characterization and prediction of the structural behaviour based on thermal loads and structural responses. By comparing the observed values and predicted outcomes from the NN, it is possible to identify measuring errors, new trends or pattern variations that need further assessment. A sensitivity analysis is also proposed to confirm the model robustness and to characterize the influence of the temperature on the structural responses. The case study is the 25 de Abril’s bridge, located in Lisbon, Portugal.