Next Article in Journal
Customizable Optical Force Sensor for Fast Prototyping and Cost-Effective Applications
Next Article in Special Issue
Identifying Time Periods of Minimal Thermal Gradient for Temperature-Driven Structural Health Monitoring
Previous Article in Journal
A Strain-Based Method to Detect Tires’ Loss of Grip and Estimate Lateral Friction Coefficient from Experimental Data by Fuzzy Logic for Intelligent Tire Development
Previous Article in Special Issue
Calibration of Elasto-Magnetic Sensors on In-Service Cable-Stayed Bridges for Stress Monitoring
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(2), 491; https://doi.org/10.3390/s18020491

Statistical Analysis of Stress Signals from Bridge Monitoring by FBG System

Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Received: 25 December 2017 / Revised: 22 January 2018 / Accepted: 23 January 2018 / Published: 7 February 2018
(This article belongs to the Special Issue Sensors and Sensor Networks for Structural Health Monitoring)
View Full-Text   |   Download PDF [3895 KB, uploaded 7 February 2018]   |  

Abstract

In this paper, a fiber Bragg grating (FBG)-based stress monitoring system instrumented on an orthotropic steel deck arch bridge is demonstrated. The FBG sensors are installed at two types of critical fatigue-prone welded joints to measure the strain and temperature signals. A total of 64 FBG sensors are deployed around the rib-to-deck and rib-to-diagram areas at the mid-span and quarter-span of the investigated orthotropic steel bridge. The local stress behaviors caused by the highway loading and temperature effect during the construction and operation periods are presented with the aid of a wavelet multi-resolution analysis approach. In addition, the multi-modal characteristic of the rainflow counted stress spectrum is modeled by the method of finite mixture distribution together with a genetic algorithm (GA)-based parameter estimation approach. The optimal probability distribution of the stress spectrum is determined by use of Bayesian information criterion (BIC). Furthermore, the hot spot stress of the welded joint is calculated by an extrapolation method recommended in the specification of International Institute of Welding (IIW). The stochastic characteristic of stress concentration factor (SCF) of the concerned welded joint is addressed. The proposed FBG-based stress monitoring system and probabilistic stress evaluation methods can provide an effective tool for structural monitoring and condition assessment of orthotropic steel bridges. View Full-Text
Keywords: structural health monitoring; orthotropic steel bridge; FBG sensor; wavelet multi-resolution analysis; finite mixture distribution; genetic algorithm; Bayesian information criterion; stress concentration factor structural health monitoring; orthotropic steel bridge; FBG sensor; wavelet multi-resolution analysis; finite mixture distribution; genetic algorithm; Bayesian information criterion; stress concentration factor
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ye, X.-W.; Su, Y.-H.; Xi, P.-S. Statistical Analysis of Stress Signals from Bridge Monitoring by FBG System. Sensors 2018, 18, 491.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top