  In this paper, we present an application of 2-D convolutional neural networks
(2-D CNNs) designed to perform both feature extraction and classification
stages as a single organism to solve the highlighted problems. The method uses
a network of lighted CNNs instead of deep and takes raw acceleration signals as
input. Using lighted CNNs, in which every one of them is optimized for a
specific element, increases the accuracy and makes the network faster to
perform. Also, a new framework is proposed for decreasing the data required in
the training phase. We verified our method on Qatar University Grandstand
Simulator (QUGS) benchmark data provided by Structural Dynamics Team. The
results showed improved accuracy over other methods, and running time was
adequate for real-time applications.
