Bridge Health Monitoring Using Accelerometer Responses to Passing Traffic

2019 ◽  
Author(s):  
Eugene Obrien ◽  
Daniel McCrum ◽  
Muhammad Arslan Khan
Keyword(s):  
Statistical Analysis ◽  
Health Monitoring ◽  
Traffic Control ◽  
The Other ◽  
Bridge Structure ◽  
Acceleration Response ◽  
Bridge Health Monitoring ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion ◽  
Strain Responses

<p>This paper develops a method of bridge structure health monitoring using bridge midspan acceleration response and the concept of Bridge Weigh-in-Motion (BWIM). This method does not require any traffic control and works solely with the responses to regular passing truck traffic. Traditional BWIM systems use bridge strain responses to infer vehicle axle weights, but the strain responses are not damage sensitive. Bridge accelerations, on the other hand, vary with the change in the bridge condition at any location. Therefore, this paper focuses on a statistical analysis of the acceleration-based BWIM results to monitor bridge condition. The acceleration-based BWIM system has been found to be very effective in detection change in the bridge condition by showing significant change in the statistical properties of the BWIM results with different damage percentage of the bridge.</p>

scholarly journals Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

2019 ◽  
Vol 76 (2) ◽  
pp. 932-947 ◽  
Author(s):  
Aiping Guo ◽  
Ajuan Jiang ◽  
Jie Lin ◽  
Xiaoxiao Li
Keyword(s):  
Neural Network ◽  
Health Monitoring ◽  
Short Term Memory ◽  
Bridge Engineering ◽  
Normal Operation ◽  
Massive Data ◽  
Health State ◽  
Bridge Structure ◽  
Continuous Growth ◽  
Bridge Health Monitoring

Abstract In recent years, bridge health monitoring system has been widely used to deal with massive data produced with the continuous growth of monitoring time. However, how to effectively use these data to comprehensively analyze the state of a bridge and provide early warning of bridge structure changes is an important topic in bridge engineering research. This paper utilizes two algorithms to deal with the massive data, namely Kohonen neural network and long short-term memory (LSTM) neural network. The main contribution of this study is using the two algorithms for health state evaluation of bridges. The Kohonen clustering method is shown to be effective for getting classification pattern in normal operating condition and is straightforward for outliers detection. In addition, the LSTM prediction method has an excellent prediction capability which can be used to predict the future deflection values with good accuracy and mean square error. The predicted deflections agree with the true deflections, which indicate that the LSTM method can be utilized to obtain the deflection value of structure. What’s more, we can observe the changing trend of bridge structure by comparing the predicted value with its limit value under normal operation.

scholarly journals Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 663 ◽  
Author(s):  
Eugene OBrien ◽  
Muhammad Arslan Khan ◽  
Daniel Patrick McCrum ◽  
Aleš Žnidarič
Keyword(s):  
Statistical Analysis ◽  
Damage Detection ◽  
Interaction Model ◽  
Statistical Properties ◽  
Vehicle Weight ◽  
Motion System ◽  
Weigh In Motion ◽  
Gross Vehicle Weight ◽  
Bridge Damage ◽  
Bridge Weigh In Motion

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

scholarly journals Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements

2020 ◽  
Vol 10 (21) ◽  
pp. 7485
Author(s):  
Hua Zhao ◽  
Chengjun Tan ◽  
Eugene J. OBrien ◽  
Nasim Uddin ◽  
Bin Zhang
Keyword(s):  
Field Trials ◽  
Correlation Factor ◽  
Vehicle Speed ◽  
The Road ◽  
Bridge Health Monitoring ◽  
Axle Detection ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion ◽  
Health Monitoring Systems ◽  
On The Road

Accurate vehicle configurations (vehicle speed, number of axles, and axle spacing) are commonly required in bridge health monitoring systems and are prerequisites in bridge weigh-in-motion (BWIM) systems. Using the ‘nothing on the road’ principle, this data is found using axle detecting sensors, usually strain gauges, placed at particular locations on the underside of the bridge. To improve axle detection in the measured signals, this paper proposes a wavelet transform and Shannon entropy with a correlation factor. The proposed approach is first verified by numerical simulation and is then tested in two field trials. The fidelity of the proposed approach is investigated including noise in the measurement, multiple presence, different vehicle velocities, different types of vehicle and in real traffic flow.

Wavelet De-noising of GNSS Based Bridge Health Monitoring Data

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Oluropo Ogundipe ◽  
Jae Kang Lee ◽  
Gethin Wyn Roberts
Keyword(s):  
Wavelet Transform ◽  
Health Monitoring ◽  
Three Dimensional ◽  
Monitoring Systems ◽  
Accurate Information ◽  
Bridge Structure ◽  
Bridge Health Monitoring ◽  
Cable Stayed Bridge ◽  
Multiple Paths ◽  
Health Monitoring Systems

AbstractGNSS signal multipath occurs when the GNSS signal reflects of objects in the antenna environment and arrives at the antenna via multiple paths. A bridge environment is one that is prone to multipath with the bridge structure, as well as passing vehicles providing static and dynamic sources of multipath. In this paper, the Wavelet Transform (WT) is applied to bridge data collected on the Machang cable stayed bridge in Korea. The WT algorithm was applied to the GNSS derived bridge defection data at the mid-span. Up to 41% improvement in RMS was observed afterwavelet shrinkage de-noisingwas applied.Application of this algorithm to the torsion data showed significant improvement with the residual average and RMS decreased by 40% and 45% respectively. This method enabled the generation of more accurate information for bridge health monitoring systems in terms of the analysis of frequency, mode shape and three dimensional defections.

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.

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.

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