  Near real-time damage diagnosis of building structures after extreme events
(e.g., earthquakes) is of great importance in structural health monitoring.
Unlike conventional methods that are usually time-consuming and require human
expertise, pattern recognition algorithms have the potential to interpret
sensor recordings as soon as this information is available. This paper proposes
a robust framework to build a damage prediction model for building structures.
Support vector machines are used to predict the existence as well as the
probable location of the damage. The model is designed to consider
probabilistic approaches in determining hazard intensity given the existing
attenuation models in performance-based earthquake engineering. Performance of
the model regarding accurate and safe predictions is enhanced using Bayesian
optimization. The proposed framework is evaluated on a reinforced concrete
moment frame. Targeting a selected large earthquake scenario, 6,240 nonlinear
time history analyses are performed using OpenSees. Simulation results are
engineered to extract low-dimensional intensity-based features that can be used
as damage indicators. For the given case study, the proposed model achieves a
promising accuracy of 83.1% to identify damage location, demonstrating the
great potential of model capabilities.

Signal Processing;Machine Learning;Machine Learning