Deep Learning-enhanced Wind Load Identification with Multi-Camera Videos

Accurate and efficient evaluation of wind loads is critical for safe and cost-effective designs of wind-sensitive structures. Wind tunnel testing is considered as one of the most reliable ways to acquire wind loads on structures, however, the limited ability to reproduce transient winds, complex surroundings and high Reynolds number effects in the laboratory is often detrimental to the experimental accuracy. On the other hand, the field measurement of wind loads has the advantage of high accuracy. However, the field-measurement approach is very expensive due to the implementation of a large number of wind pressure sensors. Recent advances in computer vision techniques shed light on an indirect way to acquire wind loads from camera videos. In this study, a multi-camera video-based wind load identification framework is proposed to reliably obtain a large amount of wind load data with low costs. Specifically, a camera array is utilized to simultaneously capture the motion videos of the target structure. The pixel motions related to structural response are extracted through the phase-based motion extraction technique. The motions extracted from camera-array videos is then fused with knowledge-enhanced deep learning to achieve high-accuracy response data. At last, the wind load is identified from the obtained structural response based on the inverse method. A case study is conducted to present the efficacy of the multi-camera video-based, deep learning-enhanced wind load identification framework. The identified wind loads match well with the ground-truth data. With the advantages of low cost, quick deployment, and automatic data processing, the proposed wind load identification scheme presents great promise in engineering applications.