scholarly journals A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage

2021 ◽  
Vol 11 (16) ◽  
pp. 7271
Author(s):  
Shuo Wang ◽  
Eugene J. OBrien ◽  
Daniel P. McCrum
Keyword(s):  
Interaction Model ◽  
Sensitivity Study ◽  
Identification Algorithm ◽  
Force Identification ◽  
Damage Level ◽  
Moving Force ◽  
Weigh In Motion ◽  
Vehicle Velocity ◽  
Gross Vehicle Weight ◽  
Bridge Damage

This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level.

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.

Investigating the Use of Moving Force Identification Theory in Bridge Damage Detection

2013 ◽  
Vol 569-570 ◽  
pp. 215-222 ◽  
Author(s):  
Ciaran H. Carey ◽  
Eugene J. O'Brien ◽  
Jennifer Keenahan
Keyword(s):  
Damage Detection ◽  
Interaction Model ◽  
Force Identification ◽  
Moving Force ◽  
Damage Location ◽  
Damage Indicator ◽  
Direct Measurements ◽  
Bridge Damage ◽  
The Mean ◽  
Identification Theory

This paper investigates the use of Moving Force Identification as a method of bridge damage detection. It identifies changes in the predicted axle force histories that occur as a result of loss in bridge element stiffness, i.e. as a result of bridge damage. A 2-dimensional Vehicle-Bridge Interaction model is used in numerical simulations to assess the effectiveness of the method in detecting changes in stiffness. Fleets of similar vehicles are simulated and the mean force pattern is used as the damage indicator. Results show that the method is more sensitive to damage than direct measurements of displacement. The paper also explores the use of the force history as an indicator of damage location.

scholarly journals Bridge weigh-in-motion using a moving force identification algorithm

2017 ◽  
Vol 199 ◽  
pp. 2955-2960 ◽  
Author(s):  
Paul C. Fitzgerald ◽  
Enrique Sevillano ◽  
Eugene J. OBrien ◽  
Abdollah Malekjafarian
Keyword(s):  
Identification Algorithm ◽  
Force Identification ◽  
Moving Force ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion

scholarly journals Experimental Testing of a Moving Force Identification Bridge Weigh-in-Motion Algorithm

2008 ◽  
Vol 49 (5) ◽  
pp. 743-746 ◽  
Author(s):  
C. W. Rowley ◽  
E. J. OBrien ◽  
A. Gonzalez ◽  
A. Žnidarič
Keyword(s):  
Experimental Testing ◽  
Force Identification ◽  
Moving Force ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion

Moving force identification for real-time bridge weigh-in-motion

2019 ◽  
Vol 14 (4) ◽  
pp. 139-145 ◽  
Author(s):  
Yahya M. Mohammed ◽  
Nasim Uddin ◽  
Eugene J. Obrien
Keyword(s):  
Real Time ◽  
Force Identification ◽  
Moving Force ◽  
Weigh In Motion ◽  
Bridge Weigh In Motion

scholarly journals Bridge damage detection using ambient traffic and moving force identification

2015 ◽  
Vol 22 (12) ◽  
pp. 1396-1407 ◽  
Author(s):  
Eugene OBrien ◽  
Ciaran Carey ◽  
Jennifer Keenahan
Keyword(s):  
Damage Detection ◽  
Force Identification ◽  
Moving Force ◽  
Bridge Damage

Moving force identification based on redundant concatenated dictionary and weighted l1-norm regularization

2018 ◽  
Vol 98 ◽  
pp. 32-49 ◽  
Author(s):  
Chu-Dong Pan ◽  
Ling Yu ◽  
Huan-Lin Liu ◽  
Ze-Peng Chen ◽  
Wen-Feng Luo
Keyword(s):  
L1 Norm ◽  
Force Identification ◽  
Moving Force

Virtual Monitoring of orthotropic steel deck using bridge weigh-in-motion algorithm: Case study

2018 ◽  
Vol 18 (2) ◽  
pp. 610-620 ◽  
Author(s):  
Longwei Zhang ◽  
Hua Zhao ◽  
Eugene J OBrien ◽  
Xudong Shao
Keyword(s):  
Field Tests ◽  
Life Assessment ◽  
Dynamic Strain ◽  
Orthotropic Steel Deck ◽  
Remaining Service Life ◽  
Fatigue Life Assessment ◽  
Weigh In Motion ◽  
Gross Vehicle Weight ◽  
Bridge Weigh In Motion ◽  
Steel Deck

This article outlines a Virtual Monitoring approach for fatigue life assessment of orthotropic steel deck bridges. Bridge weigh-in-motion was used to calculate traffic loads which were then used to calculate “virtual” strains. Some of these strains were checked through long-term monitoring of dynamic strain data. Field tests, incorporating calibration with pre-weighed trucks and monitoring the response to regular traffic, were conducted at Fochen Bridge, which has an orthotropic steel deck and is located in Foshan City, China. In the calibration tests, a 45-t 3-axle truck ran repeatedly across Lane 2, the middle lane in a 3-lane carriageway. The results show that using an influence surface to weigh vehicles can improve the accuracy of the weights and, by inference, of remaining service life calculations. The most fatigue-prone position was found to be at the cutout in the diaphragms. Results show that many vehicles are overweight—the maximum gross vehicle weight recorded was 148 t, nearly 3.6 times heavier than the fatigue design truck.

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