Mosbeh R. Kaloop

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.

GPS-structural health monitoring of a long span bridge using neural network adaptive filter

Abstract The movement of bridge deck bearings plays a significant role in the safety of bridges. Real time kinematic global positioning system (GPS) continuous health monitoring using relative deformations was carried out on a long span Zhujiang Huangpu Bridge. The neural network aided adaptive filter is used to predict and adjust the GPS monitoring data. The statistical moments in time and frequency domains were used to analyse the movement of the bridge deck. The results indicate that (1) the proposed neural network with the adaptive filter model can be used to de-noise the GPS health monitoring signals, (2) the GPS is highly sensitive for bridge deck movements, (3) the statistical moments can be used to detect the movements and errors of the GPS observations, and (4) the bridge is very safe under different loads.

De-noising of GPS structural monitoring observation error using wavelet analysis

In the process of the continuous monitoring of the structures state properties such as static and dynamic responses using Global Positioning System (GPS), there are unavoidable errors in the observation data. These GPS errors and measurement noises have their disadvantages in the precise monitoring applications because these errors cover up the available signals that are needed. The current study aims to apply three methods, which are used widely to mitigate sensor observation errors. The three methods are based on wavelet analysis, namely principal component analysis method, wavelet compressed method, and the de-noised method. These methods are used to de-noise the GPS observation errors and to prove its performance using the GPS measurements which are collected from the short-time monitoring system designed for Mansoura Railway Bridge located in Egypt. The results have shown that GPS errors can effectively be removed, while the full-movement components of the structure can be extracted from the original signals using wavelet analysis.

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.

The use of minimum curvature surface technique in geoid computation processing of Egypt

According to the wide spread use of satellite-based positioning techniques, especially Global Navigation Satellite Systems (GNSS), a greater attention has been paid to the precise determination of geoid models. As it is known, leveling measurements require high cost and long time in observation process that make it not convenient for the practical geodetic purposes. Thus obtaining the orthometric heights by GNSS is the most conventional way of determining these heights. Verifying this goal was the main objective behind the current research. The current research introduces a numerical solution of geoid modeling by applying a surface fitting for a few sparse data points of geoid undulation using minimum curvature surface (MCS). The MCS is presented for deriving a system of linear equations from boundary integral equations. To emphasize the precise applicability of the MCS as a tool for modeling the geoid in an area using GPS/leveling data, a comparison study between EGM2008 and MCS geoid models, is performed. The obtained results showed that MCS technique is a precise tool for determining the geoid in Egypt either on regional and/or local scale with law distortion at check points.

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.

Sea Level Change Analysis and Models Identification Based on Short Tidal Gauge Measurements in Alexandria, Egypt

ABSTRACT Sea level change analysis and models identification are important factors used for coastal engineering applications. Moreover, sea level change modeling is used widely to evaluate and study shoreline and climate changes. This study intends to analyze and model Alexandria, Egypt sea level change by investigating yearly tide gauge data collected in a short duration (2008–2011). The time-frequency method was used to evaluate the meteorological noise frequencies. Two models were used to predict the time series data: Neural Network Autoregressive Moving Average (NNARMA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The time-frequency analysis and models identification results showed that no extreme events were detected for Alexandria point during the monitoring period. Therefore, the NNARMA and ANFIS models can be used to identify the sea level change. The estimates of the models were compared with the three different statistics, determination coefficient, root mean square errors, and auto-correlation function. Comparison of these results revealed that the NNARMA model performs better than the ANFIS model for the study area.