Jong Wan Hu

Stayed-Cable Bridge Damage Detection and Localization Based on Accelerometer Health Monitoring Measurements

In situ damage detection and localization using real acceleration structural health monitoring technique are the main idea of this study. The statistical and model identification time series, the response spectra, and the power density of the frequency domain are used to detect the behavior of Yonghe cable-stayed bridge during the healthy and damage states. The benchmark problem is used to detect the damage localization of the bridge during its working time. The assessment of the structural health monitoring and damage analysis concluded that (1) the kurtosis statistical moment can be used as an indicator for damage especially with increasing its percentage of change as the damage should occur; (2) the percentage of change of the Kernel density probability for the model identification error estimation can detect and localize the damage; (3) the simplified spectrum of the acceleration-displacement responses and frequencies probability changes are good tools for detection and localization of the one-line bridge damage.

Summary Review of Structural Health Monitoring Applications for Highway Bridges

AbstractThe state-of-the art paper provides an extensive literature review on the work pertaining to structural health monitoring (SHM) systems used to investigate the structural integrity of highway bridges. The focus of this review is on identifying the SHM research efforts that include damage detection, structural capacity evaluation, and remaining service life estimates on such structures. These efforts have spanned a broad range of data processing methods devoted to tracking changes in structural characteristics for damage detection, codified frameworks enabling structural capacity estimating, and reliability analysis to predict remaining life. Our findings are that a large number of studies considered damage detection by data processing methods, whereas a relatively small number of studies were devoted to the estimation of structural capacities and the remaining service life of bridges. We conclude that the critical gaps include a lack of validated SHM systems that use ambient data to examine design...

Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures

The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.

Structural Performance Assessment Based on Statistical and Wavelet Analysis of Acceleration Measurements of a Building during an Earthquake

This study introduces the analysis of structural health monitoring (SHM) system based on acceleration measurements during an earthquake. The SHM system is applied to assess the performance investigation of the administration building in Seoul National University of Education, South Korea. The statistical and wavelet analysis methods are applied to investigate and assess the performance of the building during an earthquake shaking which took place on March 31, 2014. The results indicate that (1) the acceleration, displacement, and torsional responses of the roof recording point on the top floor of the building are more dominant in the X direction; (2) the rotation of the building has occurred at the base recording point; (3) 95% of the energy content of the building response is shown in the dominant frequency range (6.25–25 Hz); (4) the wavelet spectrum illustrates that the roof vibration is more obvious and dominant during the shaking; and (5) the wavelet spectrum reveals the elasticity responses of the structure during the earthquake shaking.

Dynamic Performance Analysis of the Towers of a Long-Span Bridge Based on GPS Monitoring Technique

The present study investigates the parameter identification and the dynamic performance of a long-span bridge tower based on the output of a global positioning system (GPS) health monitoring system. The random decrement (RD) algorithm is used to estimate the tower displacement impulse response. Three methods are applied to extract the dynamic performance including least squares complex exponential (LSCE) method, Hilbert envelope method (HEM), and eigensystem realization algorithm (ERA). Results reveal that the HEM and LSCE method are more suitable to extract fundamental frequency and modal and damping ratios of the tower. Furthermore, the dynamic properties and statistical time series analysis of the GPS measurements illustrate that the traffic loads have a high significant impact on the semistatic and dynamic performances.

Adjustment and Assessment of the Measurements of Low and High Sampling Frequencies of GPS Real-Time Monitoring of Structural Movement

Global Positioning System (GPS) structural health monitoring data collection is one of the important systems in structure movement monitoring. However, GPS measurement error and noise limit the application of such systems. Many attempts have been made to adjust GPS measurements and eliminate their errors. Comparing common nonlinear methods used in the adjustment of GPS positioning for the monitoring of structures is the main objective of this study. Nonlinear Adaptive-Recursive Least Square (RLS), extended Kalman filter (EKF), and wavelet principal component analysis (WPCA) are presented and applied to improve the quality of GPS time series observations. Two real monitoring observation systems for the Mansoura railway and long-span Yonghe bridges are utilized to examine suitable methods used to assess bridge behavior under different load conditions. From the analysis of the results, it is concluded that the wavelet principal component is the best method to smooth low and high GPS sampling frequency observations. The evaluation of the bridges reveals the ability of the GPS systems to detect the behavior and damage of structures in both the time and frequency domains.

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS) technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS) time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1)the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2) the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3) The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

Seismic Response and Evaluation of SDOF Self-Centering Friction Damping Braces Subjected to Several Earthquake Ground Motions

This paper mainly deals with seismic response and performance for self-centering friction damping braces (SFDBs) subjected to several maximum- or design-leveled earthquake ground motions. The self-centering friction damping brace members consist of core recentering components fabricated with superelastic shape memory alloy wires and energy dissipation devices achieved through shear friction mechanism. As compared to the conventional brace members for use in the steel concentrically braced frame structure, these self-centering friction damping brace members make the best use of their representative characteristics to minimize residual deformations and to withstand earthquake loads without member replacement. The configuration and response mechanism of self-centering friction damping brace systems are firstly described in this study, and then parametric investigations are conducted through nonlinear time-history analyses performed on numerical single degree-of-freedom spring models. After observing analysis results, adequate design methodologies that optimally account for recentering capability and energy dissipation according to their comparative parameters are intended to be suggested in order to take advantage of energy capacity and to minimize residual deformation simultaneously.

Influence of Atypical Vehicle Types on Girder Distribution Factors of Secondary Road Steel-Concrete Composite Bridges

This paper investigated the influence of atypical agricultural vehicle parameters on girder distribution factors (GDFs) for two simply supported steel-concrete composite bridges located on secondary roads in the United States. As part of the parametric investigation, field measurements were performed on the bridges under the passage of full-scale agricultural vehicles along each bridge centerline. Strain data obtained from field tests were used to determine field GDFs. Both field data and GDFs were used to calibrate a finite element model of the bridge. Data from over in-use one hundred agricultural vehicles were collected, and then each vehicle was applied to the model to compute analytical GDFs. These vehicles were analytically and statistically investigated to determine the sensitivity of GDFs to vehicle parameters, including maximum vehicle axle load, gage width, and spacing distributions. Based upon field strain investigation for the two bridges, the responses of the central girders were greater than those of the exterior girders, resulting in greater field GDFs for central girders than those of the exterior girders. Results also indicated from the sensitivity study that each girder had different levels of correlation between GDFs and single vehicle parameters.

Recent Advances of Structures Monitoring and Evaluation Using GPS-Time Series Monitoring Systems: A Review

This paper presents the recent development in Structural Health Monitoring (SHM) applications for monitoring the dynamic behavior of structures using the Global Positioning Systems (GPS) technique. GPS monitoring systems for real-time kinematic (RTK), precise point positioning (PPP) and the sampling frequency development of GPS measurements are summarized for time series analysis. Recent proposed time series GPS monitoring systems, errors sources and mitigation, as well as system analysis and identification, are presented and discussed.