Abstract
The deep learning technologies have transformed many research areas with accuracy
levels that the traditional methods are not comparable with. Recently, they have received
increasing attention in the structural health monitoring (SHM) domain. In this paper,
we aim to develop a new deep learning algorithm for structural condition monitoring
and to evaluate its performance in a challenging case, bolt loosening damage in a frame
structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN)
is introduced. Second, a series of impact hammer tests are conducted on a steel
frame in the laboratory under ten scenarios, with bolts loosened at different locations
and quantities. For each scenario, ten repeated tests are performed to provide enough
training data for the algorithm. Third, the algorithm is trained with different quantities
of training data (from one to seven test data for each scenario), and then is tested with
the rest test data. The results show that the proposed 1D-CNN with three convolutional
layers provide reliable identification results (over 95% accuracy) with sufficient training
data sets. It has the potential to transform the SHM practice.