Synchronized identification of dynamic load magnitude and location based on convolutional neural network

Most of the existing research in structural health monitoring and status assessment requires accurate load action locations or detailed dynamic load schedules. To simultaneously obtain the size and location of the dynamic load,a two-branch convolutional neural network with both classification and regression capabilities is constructed and trained. A loss function that integrates classification and regression problems is established to capture the mapping relationship between structural response and load magnitude,as well as between structural response and load location. The identification accuracy of load magnitude and location is demonstrated through numerical cantilever beam examples and a three-layer experimental framework. Results show that under noisy conditions,the error in load magnitude identification of the numerical model is within 8%,and the accuracy of load location identification is above 95% . For real structures,the error in load magnitude identification is within 18%,and the accuracy of load location identification is 100% . The two-branch convolutional neural network can effectively identify both the magnitude and location of dynamic loads. © 2024 Southeast University. All rights reserved.