Fatigue Performance Prediction of RC Beams Based on Optimized Machine Learning Technology

Abstract

The development of fatigue damage in reinforced concrete (RC) beams is affected by various factors such as repetitive loads and material properties, and there exists a complex nonlinear mapping relationship between their fatigue performance and each factor. To this end, a fatigue performance prediction model for RC beams was proposed based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). The original database of fatigue loading tests was established by conducting fatigue loading tests on RC beams. The mid-span deflection, reinforcement strain, and concrete strain during fatigue loading of RC beams were predicted and evaluated. The fatigue performance prediction results of the RC beam based on the PSO-DBN model were compared with those of the single DBN model and the BP model. The models were evaluated using the R2 coefficient, mean absolute percentage error, mean absolute error, and root mean square error. The results showed that the fatigue performance prediction model of RC beams based on PSO-DBN is more accurate and efficient.

Endoscopic Image Classification Based on Explainable Deep Learning

“…To overcome this problem, collecting and augmenting the data are essential parts of deep learning before training and testing the model architecture [ 5 ]. In addition, there are a number of useful methods to overcome overfitting and increase the accuracy of the model, which are denoising [ 6 ], initialization and setting momentum [ 7 ], batch normalization [ 8 , 9 ], dropout [ 10 ], and drop connect [ 11 ]. Transfer learning is also a great technique to enhance the deep learning model with a high positive benefit in medical imaging using pre-trained weight in the last part of the convolutional neural network [ 12 , 13 , 14 ].…”