Topological mapping and assessment of multiple settlement time series in deep excavation: A complex network perspective. (April 2018)

• Record Type: Journal Article
• Authors: Zhou, Cheng; Ding, Lieyun; Zhou, Ying; Luo, Hanbin
• Is Part Of: Advanced engineering informatics. Volume 36(2018)
• Page Start: 1
• Page End: 19
• Publication Date: 2018-04
• Subjects: Deep excavation -- Settlement time series -- Complex network -- Similarity matrix -- Topological analysis -- Node influence; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.02.005
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Settlement time series in deep excavation are measured into a complex network. The reconstructed network can identify the topological characteristics of settlement monitoring points on site. This approach can extract macro-level and micro-level decision information from settlement monitoring data. A deep excavation case of the metro station project is used to validate the proposed methodology. Abstract: This study proposed a novel methodology that integrates complex network theory and multiple time series to enhance the systematic understanding of the daily settlement behavior in deep excavation. The original time series of ground surface, surrounding buildings, and structure settlement instrumentation data over an excavation time period were measured into a similarity matrix with correlation coefficients. A threshold was then determined and binarized into adjacent matrix to identify the optimal topology and structure of the complex network. The reconstructed settlement network has nodes corresponding to multiple settlement time series individually and edges regarded as nonlinear relationships between them. A deep excavation case study of the metro station project in the Wuhan Metro network, China, was applied to validate the feasibility and potential value of the proposed approach. Results of the topological analysis corroborate a small-world phenomenon with highly compacted interactions and provide the assessment of the significance among multiple settlement time series. This approach, which provides a new way to assess the safety monitoring data in underground construction, can be implemented as a tool for extracting macro- and micro-level decision information from multiple settlement time series in deep excavation from complex system perspectives.