This paper presents algorithms for diagnosing the severity and location of damage in a laboratory bridge model. We use signal processing and machine learning approaches to analyze the vibration responses collected both directly from the bridge model and indirectly from a vehicle passing over the model. Features are selected using principal component analysis (PCA), and a regression is performed using the kernel regression method. Various "damage" severities and positions are simulated on a laboratory bridge model by placing additional mass on the bridge. We perform two experiments; one to measure our ability to detect damage severity (i.e. size of the mass), and a second to measure our ability to detect damage location (i.e. position of the mass). In the first experiment, we vary the magnitude of the mass while keeping its location constant. In the second experiment, we vary the location of the mass while keeping its magnitude constant. In both cases, we use a portion of our data to train the algorithm, and another portion to test its validity. We report the accuracy of correctly quantifying the nature of the mass from the test data as a mean square error (MSE).