Response pattern analysis‐based structural health monitoring of cable‐stayed bridges

Abstract

Measurement systems using different sensors are currently well established for the safe use of structures in principal infrastructures, such as cable-stayed bridges. However, existing practical technologies that assess structural states by analyzing monitored data are underutilized. Although technologies to identify potential damage using advanced sensors or algorithms are continuously being developed, they have not reached a stage wherein they can be confidently applied. This study presents a methodology for monitoring structural response measurement data for application in current systems. The proposed methodology estimates changes in structural properties through pattern changes in measurement data; it is simple, clear, and easily applicable. An autoregressive model is used to analyze patterns in time series data. Pattern changes are analyzed based on reference data. A procedure for monitoring pattern variations in measurement data is derived, and the influences of variables considered in the monitoring procedure are also analyzed. The applicability of the methodology is examined based on the measurement data of an in-service cable-stayed bridge. As a basis for structural health monitoring based on pattern analysis, the proposed methodology is expected to be applied in various ways, through further studies, to relate the pattern of structural responses to the state of the structures.

Wireless Binocular Stereovision Measurement System Based on Improved Coarse-to-Fine Matching Algorithm

“…Structural displacement is a vital evaluation index in structural health monitoring (SHM) [1]. As a principal evaluation standard of safe structural condition, deformation is directly related to the stifness of the structure [2,3], which can then intuitively represent the soundness of the building structure [4]. Terefore, how to efciently estimate or measure the displacement response is widely studied in the feld of SHM.…”

Long-term structural health monitoring for bridge based on back propagation neural network and long and short-term memory

“…3 Nowadays, there is growing awareness of the necessity to SHM systems of bridges to ensure safeties of bridges and to assist researchers, engineers, managers and owners in a comprehensive understanding about bridge conditions. [4][5][6][7] Lee et al 8 presented a methodology for monitoring structural response measurement data for application in current systems, and applicability of the methodology was examined based on the measurement data of an in-service cable-stayed bridge. Prakash 9 presented a static deflection-based method for SHM of in-service bridges, and the accuracy of the method was verified through experiments and numerical simulations.…”

Smartphone-Based and Data-Driven Superstructure State Prediction Method for Highway Bridges in Service

“…Therefore, it is imminent to carry out safety status assessments and real-time health monitoring of existing bridge structures, especially small- and medium-span bridge structures. At present, most of the assessments and early warnings of the safety status of bridge structures are carried out by installing sensors and other monitoring equipment on the bridge structure to conduct long-term real-time monitoring of the bridge operation status and related physical quantities [ 5 , 6 ]. However, the objects of long-term health monitoring of bridge structures are mostly large, and large bridges, as well as medium and small span bridges with unique shapes and important positions.…”