Monitoring Neutral Axis Position Using Monthly Sample Residuals as Estimated From a Data Mining Model

Frontiers in Built Environment

Michael J. Chajes¹  Harry W. Shenton¹  Christos Aloupis² 

[1] Newark, DE, United States;null;

关键词: neutral axis;    strain measurement;    residual distribution;    structural health monitoring;    SHM;    data mining;    cable-stayed bridge;   

DOI  :  10.3389/fbuil.2021.625754

来源: Frontiers

【 摘 要 】

Structural Health Monitoring (SHM) has enabled the condition of large structures, like bridges, to be evaluated in real time. In order to monitor behavioral changes, it is essential to identify parameters of the structure that are sensitive enough to capture damage as it develops while being stable enough during ambient behavior of the structure. Research has shown that monitoring the neutral axis (N.A.) position satisfies the first criterion of sensitivity; however, monitoring N.A. location is challenging because its position is affected by the loads applied to the structure. The motivation behind this research comes from the greater than expected impact of various load characteristics on observed N.A. location. This paper develops an indirect way to estimate the characteristics of vehicular loads (magnitude and lateral position of the load) and uses a data mining approach to predict the expected location of the N.A. Instead of monitoring the behavior of the N.A., in the proposed method the residuals between the monitored and predicted N.A. location are monitored. Using actual SHM data collected from a cable-stayed bridge, over a 2-year period, the paper presents the steps to be followed for creating a data mining model to predict N.A. location, the use of monthly sample residuals of N.A. to capture behavioral changes, the ability of the method to distinguish between changes in the load characteristics from behavioral changes of the structure (e.g. change in response due to cracking, bearings becoming frozen, cables losing tension, etc.), and the high sensitivity of the method that allows capturing of minor changes.

【 授权许可】

CC BY