  Structural health monitoring is important to make sure bridges do not fail.
Since direct monitoring can be complicated and expensive, indirect methods have
been a focus on research. Indirect monitoring can be much cheaper and easier to
conduct, however there are challenges with getting accurate results. This work
focuses on damage quantification by using accelerometers. Tests were conducted
on a model bridge and car with four accelerometers attached to to the vehicle.
Different weights were placed on the bridge to simulate different levels of
damage, and 31 tests were run for 20 different damage levels. The acceleration
data collected was normalized and a Fast-Fourier Transform (FFT) was performed
on that data. Both the normalized acceleration data and the normalized FFT data
were inputted into a Non-Linear Principal Component Analysis (separately) and
three principal components were extracted for each data set. Support Vector
Regression (SVR) and Gaussian Process Regression (GPR) were used as the
supervised machine learning methods to develop models. Multiple models were
created so that the best one could be selected, and the models were compared by
looking at their Mean Squared Errors (MSE). This methodology should be applied
in the field to measure how effective it can be in real world applications.
