Non-linear chaotic analysis of bridge health monitoring information based on phase space correlation dimension

2010 ◽  
Author(s):  
Jianxi Yang ◽  
Jianting Zhou
Keyword(s):  
Phase Space ◽  
Health Monitoring ◽  
Correlation Dimension ◽  
Space Correlation ◽  
Bridge Health Monitoring ◽  
Non Linear ◽  
Monitoring Information

Analysis, Prospect on Non-Linearity and Security Evaluation of Bridge Based on Multi-Chaotic Indexes

2011 ◽  
Vol 52-54 ◽  
pp. 1015-1020
Author(s):  
Jian Xi Yang ◽  
Jian Ting Zhou
Keyword(s):  
Health Monitoring ◽  
Correlation Dimension ◽  
Security Evaluation ◽  
Bridge Structure ◽  
Bridge Health Monitoring ◽  
Bridge Safety ◽  
Solid Foundation ◽  
Lyapunov Index ◽  
Further Development ◽  
Monitoring Information

This paper presents an analysis of the nonlinear characteristics of a bridge structure and the chaos of BHM(bridge health monitoring) information. Chongqing Masangxi bridge’s BHM information is analyzed by using the max Lyapunov index with Wolf and correlation dimension with G_P algorithm. The results show:1) all of the max Lyapunov index is nonnegative ;2)the correlation dimension is non-integeral and greater than 2.These proves that the bridge structure is in the chaos. Meanwhile, with the evolution of time, the index of chaos is sensitive with status of structure system and varies in different key sections of bridge structure. These findings lay a solid foundation for the further development of bridge safety assessment and prediction when non-linear chaotic theory is utilized to analyze the bridge health monitoring information.

Prediction of Chaotic Time Series of Bridge Monitoring System Based on Multi-Step Recursive BP Neural Network

2010 ◽  
Vol 159 ◽  
pp. 138-143 ◽  
Author(s):  
Jian Xi Yang ◽  
Jian Ting Zhou
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Bp Neural Network ◽  
Chaotic Time Series ◽  
Effective Theory ◽  
Maximum Lyapunov Exponent ◽  
Space Correlation ◽  
Bridge Monitoring System ◽  
Monitoring Information

BHM is an important means to assess and predict the safety operation of large bridge in service around the world. Given the missing of real-time monitoring information for some time and the lack of effective theory and technique to capture the missing information and even to predict the evolution of structure, this paper made an attempt to predict the evolution of monitoring information using time series and chaotic theory. Firstly, maximum Lyapunov exponent of available monitoring information is calculated to assess the chaos of the bridge structure. The parameters of reconstructed phase space, correlation dimension and time delay, are calculated by C-C algorithm and G-P algorithm respectively. According to empirical formula, one 3-layer BP neural network is established Ten recursions are carried out. The results show that multi-layer recursive BP neural network is able to predict BHM information. Using chaotic time series to reconstruct phase space and applying multi-layer recursive BP neural network to predict BHM information facilitates further estimation and prediction of bridge safety condition by means of chaotic nonlinear characteristics.

scholarly journals Fractal MHD Waves in the Solar Atmosphere

1993 ◽  
Vol 141 ◽  
pp. 538-541
Author(s):  
Luigi Nocera
Keyword(s):  
Phase Space ◽  
Lyapunov Exponents ◽  
Periodic Orbits ◽  
Solar Atmosphere ◽  
Correlation Dimension ◽  
Structural Instability ◽  
Mhd Waves ◽  
Space Correlation ◽  
Attracting Set ◽  
The Waves

AbstractNonlinear MHD waves forced into a bounded cavity exhibit structural instability of their equilibria and periodic orbits as the forcing varies. For some values of the forcing the computed Poincaré maps, Lyapunov exponents and phase-space correlation reveal that the waves become chaotic; the Haussdorff dimension of the waves’ attracting set lies between its correlation dimension (≈ 2.2) and its information dimension (≈ 2.25).

scholarly journals Numerical Parametric Study on the Effectiveness of the Contact-Point Response of a Stationary Vehicle for Bridge Health Monitoring

2021 ◽  
Vol 11 (15) ◽  
pp. 7028
Author(s):  
Ibrahim Hashlamon ◽  
Ehsan Nikbakht ◽  
Ameen Topa ◽  
Ahmed Elhattab
Keyword(s):  
Numerical Model ◽  
Health Monitoring ◽  
Contact Point ◽  
Response Spectra ◽  
Roughness Effects ◽  
Moving Vehicle ◽  
Bridge Health Monitoring ◽  
Vehicle Response ◽  
Instrumented Vehicle ◽  
The Time Domain

Indirect bridge health monitoring is conducted by running an instrumented vehicle over a bridge, where the vehicle serves as a source of excitation and as a signal receiver; however, it is also important to investigate the response of the instrumented vehicle while it is in a stationary position while the bridge is excited by other source of excitation. In this paper, a numerical model of a stationary vehicle parked on a bridge excited by another moving vehicle is developed. Both stationary and moving vehicles are modeled as spring–mass single-degree-of-freedom systems. The bridges are simply supported and are modeled as 1D beam elements. It is known that the stationary vehicle response is different from the true bridge response at the same location. This paper investigates the effectiveness of contact-point response in reflecting the true response of the bridge. The stationary vehicle response is obtained from the numerical model, and its contact-point response is calculated by MATLAB. The contact-point response of the stationary vehicle is investigated under various conditions. These conditions include different vehicle frequencies, damped and undamped conditions, different locations of the stationary vehicle, road roughness effects, different moving vehicle speeds and masses, and a longer span for the bridge. In the time domain, the discrepancy of the stationary vehicle response with the true bridge response is clear, while the contact-point response agrees well with the true bridge response. The contact-point response could detect the first, second, and third modes of frequency clearly, unlike the stationary vehicle response spectra.

scholarly journals Challenges in Bridge Health Monitoring: A Review

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4336
Author(s):  
Piervincenzo Rizzo ◽  
Alireza Enshaeian
Keyword(s):  
United States ◽  
Health Monitoring ◽  
Highway Bridges ◽  
The United States ◽  
Anomalous Behavior ◽  
Existing Structures ◽  
Bridge Health Monitoring ◽  
Using Data ◽  
The Many ◽  
Major Factors

Bridge health monitoring is increasingly relevant for the maintenance of existing structures or new structures with innovative concepts that require validation of design predictions. In the United States there are more than 600,000 highway bridges. Nearly half of them (46.4%) are rated as fair while about 1 out of 13 (7.6%) is rated in poor condition. As such, the United States is one of those countries in which bridge health monitoring systems are installed in order to complement conventional periodic nondestructive inspections. This paper reviews the challenges associated with bridge health monitoring related to the detection of specific bridge characteristics that may be indicators of anomalous behavior. The methods used to detect loss of stiffness, time-dependent and temperature-dependent deformations, fatigue, corrosion, and scour are discussed. Owing to the extent of the existing scientific literature, this review focuses on systems installed in U.S. bridges over the last 20 years. These are all major factors that contribute to long-term degradation of bridges. Issues related to wireless sensor drifts are discussed as well. The scope of the paper is to help newcomers, practitioners, and researchers at navigating the many methodologies that have been proposed and developed in order to identify damage using data collected from sensors installed in real structures.

Sign in / Sign up

Export Citation Format

Share Document