A generalized approach to integrate machine learning, finite element modeling and monitoring data for bridges

In the last decades, the structural health monitoring (SHM) of civil structures has been performed arguably based on two approaches: model- and data-based. The former approach tries to identify damage by relating the measured data from the structure to the prediction of physics-based numerical models tailored for the same structure. The latter one is a data-driven modeling approach, where measured data from a given state condition is compared to the baseline condition. The data-based approach has been rooted in the machine learning field, where machine learning algorithms are essential to learn the structural behavior from the past data, and to perform pattern recognition for damage identification. In the SHM field, this approach has been known as the statistical pattern recognition paradigm. Basically, in both approaches, the identification of damage requires data comparison between two state conditions, the baseline and a damaged condition; thus in a general sense, those two approaches make use of pattern recognition techniques. This paper intends to step forward through the combination of machine learning, finite element modeling and monitoring data from the Z-24 Bridge in one unique damage detection approach. To achieve this combination, data from simulated undamaged and damaged scenarios can be introduced into the learning process using predictions from finite element models.