System and method for improved rail bridge strike detection

A computerized system for detecting structure strikes by vehicles comprising:

• a first convolution neural network trained with a set of first structure data comprising a first vehicle response data, a first structure strike data and a first synthetic structure strike data;
• a second convolution neural network trained with a set of second structure data comprising a second vehicle response data, a second structure strike data and a second synthetic structure strike data;
• and
• one or more processors configured to feed a new first structure data into the first convolution neural network and a new second structure data into the second convolution neural network, automatically extract a set of features from the first convolution neural network and the second convolution neural network, concatenate the set of features extracted from the first convolution neural network and the second convolution neural network, and classify the concatenated set of features as a structure strike or a no structure strike using a back propagation network.

A computer-implemented method for detecting bridge strikes, the method comprising:

• providing a bridge acceleration convolution neural network trained with a set of bridge acceleration data comprising train response acceleration data, bridge strike acceleration data and synthetic bridge strike acceleration data;
• providing a bridge tilt convolution neural network trained with a set of bridge tilt data comprising a train response tilt data, bridge strike tilt data and synthetic bridge strike tilt data;
• feeding new bridge acceleration data into the bridge acceleration convolution neural network and new bridge tilt data into the bridge tilt convolution neural network;
• automatically extracting, by one or more processors, a set of features from the bridge acceleration convolution neural network and the bridge tilt convolution neural network;
• concatenating, by the one or more processors, the set of features extracted from the bridge acceleration convolution neural network and the bridge tilt convolution neural network;
• and
• classifying, by the one or more processors, the concatenated set of features as a bridge strike or a no bridge strike using a back propagation network.

A computerized system for detecting bridge strikes comprising:

• a bridge acceleration convolution neural network trained with a set of bridge acceleration data comprising train response acceleration data, bridge strike acceleration data and synthetic bridge strike acceleration data;
• a bridge tilt convolution neural network trained with a set of bridge tilt data comprising a train response tilt data, bridge strike tilt data and synthetic bridge strike tilt data;
• and
• one or more processors configured to feed new bridge acceleration data into the bridge acceleration convolution neural network and new bridge tilt data into the bridge tilt convolution neural network, automatically extract a set of features from the bridge acceleration convolution neural network and the bridge tilt convolution neural network, concatenate the set of features extracted from the bridge acceleration convolution neural network and the bridge tilt convolution neural network, and classify the concatenated set of features as a bridge strike or a no bridge strike using a back propagation network.