An improved adaptive web sampling method for node deviation inspection of single-layer latticed shells. (June 2021)

• Record Type: Journal Article
• Authors: Li, Yunsong; Luo, Yongfeng; Guo, Xiaonong; Yang, Xu; Qu, Yang; Liu, Xiao; Zhang, Yujian
• Is Part Of: Structures. Volume 31(2021)
• Page Start: 708
• Page End: 720
• Publication Date: 2021-06
• Subjects: Spatial latticed shell -- Node positional deviation -- Spatial local autocorrelation -- Adaptive web sampling method -- Supplementary sampling; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2021.02.022
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: The node positions of single-layer latticed shells in service usually deviate from their design positions, and a few nodes of the shell may exist larger positional deviations than other nodes, which could result in a progressive collapse or overall buckling of the shell. To avoid the significant loss of structure failure, these nodes with large positional deviations should be considered in the structural inspection sampling. However, existing sampling methods in the structural inspection are designed for independent products that ignore the dependence in these nodes and are unsuitable for node positional deviation inspection of single-layer latticed shells. To solve this problem, the node positional deviation aggregation is defined and calculated based on the theory of spatial local autocorrelation, and an improved adaptive web sampling (IAWS) method is proposed in this paper, which consists of three parts: calculation of node connection matrix, adaptive web sampling, and supplementary sampling. Specially, the supplementary sampling is proposed to improve the overall sampling accuracy for adaptive web sampling, and the selection of supplementary samples is based on the relationship between the sample position and the total node deviation reckoning error derived in this paper. Besides, two performance indicators are adopted to evaluate the overall reckoning accuracy of sample methods, and two new performance indicators are proposed to assess the deviation aggregation area sampling accuracy. The numerical analysis results of two Kiewitt shells and a cylinder shell show that IAWS can provide a higher sampling accuracy than random sampling, systematic sampling, and adaptive web sampling.