Real-time structural damage detection by convolutional neural networks

An apparatus, comprising:

• at least one memory comprising computer program code;
• and
• at least one processor;
• wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus at least to:
• train a one-dimensional convolutional neural network (CNN) applied a joint of a structure;
• send instructions to a modal shaker to induce an input to the structure;
• receive, as a result of the induced input, a raw acceleration signal at the joint;
• compute, based on the trained one-dimensional CNN and the raw acceleration signal, an index value of the joint;
• and
• identify, according to the index value, a presence of a location of structural damage of the structure,
• wherein the index value represents a likelihood of damage at the joint, and
• wherein the apparatus is further caused to, in the computation of the index value,
• divide the acceleration signal to a number of frames that each include a total number of ns samples;
• normalize the frames between −1 to 1;
• feed the normalized frames measured at the joint to the one-dimensional CNN;
• and
• determine a probability of damage (PoD) at the joint by dividing a number of frames classified s damaged by a total number of frames processed by the one-dimensional CNN.

A computer program, embodied on a non-transitory computer readable medium, the computer program, when executed by a processor, causes the processor to:

• train a one-dimensional convolutional neural network (CNN) applied a joint of a structure;
• send instructions to a modal shaker to induce an input to the structure;
• receive, as a result of the induced input, a raw acceleration signal at the joint;
• compute, based on the trained one-dimensional CNN and the raw acceleration signal, an index value of the joint;
• identify, according to the index value, a presence of a location of structural damage of the structure,
• wherein the index value represents a likelihood of damage at the joint, and
• wherein the processor is further caused to, in the computation of the index value,
• divide the acceleration signal to a number of frames that each include a total number of ns samples;
• normalize the frames between −1 to 1;
• feed the normalized frames measured at the joint to the one-dimensional CNN;
• and
• determine a probability of damage (PoD) at the joint by dividing a number of frames classified s damaged by a total number of frames processed by the one-dimensional CNN.