  Rockfalls are a hazard for the safety of infrastructure as well as people.
Identifying loose rocks by inspection of slopes adjacent to roadways and other
infrastructure and removing them in advance can be an effective way to prevent
unexpected rockfall incidents. This paper proposes a system towards an
automated inspection for potential rockfalls. A robot is used to repeatedly
strike or tap on the rock surface. The sound from the tapping is collected by
the robot and subsequently classified with the intent of identifying rocks that
are broken and prone to fall. Principal Component Analysis (PCA) of the
collected acoustic data is used to recognize patterns associated with rocks of
various conditions, including intact as well as rock with different types and
locations of cracks. The PCA classification was first demonstrated simulating
sounds of different characteristics that were automatically trained and tested.
Secondly, a laboratory test was conducted tapping rock specimens with three
different levels of discontinuity in depth and shape. A real microphone mounted
on the robot recorded the sound and the data were classified in three clusters
within 2D space. A model was created using the training data to classify the
reminder of the data (the test data). The performance of the method is
evaluated with a confusion matrix.
