Health Evaluation of Frame Structures Based on the Gray Cloud Network Model: Proposal and Experimental Study

The quantification of structural health has become a fundamental issue in structural hazard identification. To quantify the structural health state affected by multiple uncertainties, this paper proposes a structural health evaluation method based on a gray cloud network model (GCNM), which combines uncertainty analysis methods and Dempster–Shafer (DS) evidence theory. Firstly, the GCNM–cloud model front cloud is built by introducing entropy and hyperentropy to measure the fuzziness and randomness of information, on the basis of which the relative entropy is introduced to consider the correlation between the factors, and the GCNM–gray cloud model front cloud is built. Then, the DS evidence theory is used to fuse the affiliations of the two front clouds to construct the GCNM front cloud that considers multisource information. The GCNM front cloud and the GCNM back cloud with quantitative capability together form the gray cloud network. Finally, a structural health evaluation method based on the gray cloud network model is established, and the effectiveness of the method is verified by numerical simulations and experiments of the frame structure. The results showed that the gray cloud network model is more effective in the health evaluation of frame structures.