Small-sample damage detection of bleacher structure based on GAN and MSS-CNN models

The damage scales and forms of bleacher structure are diverse, and the training by using neural network models may be inadequate when the data sample is limited, resulting in challenges such as overfitting or the inability to generalize new damage scenarios. In order to address the issue of damage detection in bleacher structures with small samples, this paper proposes a multi-scale stride convolutional neural network (MSS-CNN) model. It is trained as a generator and discriminator within a generative adversarial network (GAN) framework. By utilizing GAN to generate data and integrating real data with generated data, the mixed data is input into the MSS-CNN model for training, ultimately yielding damage detection results. In order to validate the effectiveness of this approach, a series of experimental studies are conducted by using a bleacher simulator at Qatar University as the research subject. Furthermore, the model is compared with ResNet, multi-layer perceptron, and support vector machine under identical experimental conditions by comparing real and mixed data. The experimental results consistently demonstrate the superior performance of the MSS-CNN model across multiple experiments. This paper presents a fresh research approach and perspective for addressing the challenge of small-sample damage detection in bleacher structures.