Vehicle load identification based on bridge response using deep convolutional neural network

Abstract

Ensuring bridge safety and longevity through precise vehicle load characterization is crucial for maintaining structural integrity and efficiency. This research highlights the efficacy of deep convolutional neural networks (DCNNs) in analyzing bridge responses. The proposed methodology incorporates abridge model developed in ANSYS APDL, integrated subsequently with Universal Mechanism (UM) software for simulating vehicle-bridge interactions, considering factors such as vehicle load, speed, road surface roughness, and ambient noise. To validate the accuracy and robustness of the DCNN models, the study involved training and testing them on diverse datasets, each comprising 250 randomized samples for various parameters such as load, speed, and surface roughness. Two popular deep learning frameworks, ResNet and Inception models, were employed to assess their capability to identify these critical vehicle-bridge interaction characteristics accurately. The results reveal that the Inception model excelled in vehicle load identification, achieving an impressive validation accuracy of 99.9%. In contrast, ResNet outperformed in vehicle speed identification with a99% validation accuracy, surpassing Inception’s 97.9%. Both models exhibited remarkable resilience to noise, with road surface roughness identification demonstrating exceptional performance, including aperfect 100% training accuracy for ResNet and 99.9% for Inception. Furthermore, to validate the noise robustness of the DCNN model, the load identification is conducted under the effects of measurement noise, and the identification results show that the DCNN model can accurately identify vehicle loads with accuracies of 97.9% at 10 dB and 99.7% at 20 dB, respectively. These findings firmly establish the DCNNs’ capability to accurately identify vehicle load, speed, and road surface roughness, emphasizing their potential to significantly improve bridge safety and longevity.

Keywords

• deep convolutional neural network
• bridge response
• vehicle response
• noise detection
• vehicle load identification