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Bridge Damage Detection with Sensing Technology

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 August 2019) | Viewed by 35912

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Prof. Dr. Eugene J. OBrien

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Guest Editor

School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: bridge health monitoring and assessments; weigh-in-motion; sensor-based monitoring; structural dynamics
Special Issues, Collections and Topics in MDPI journals

Dr. Mustafa Gül

E-Mail Website
Guest Editor

Civil and Environmental Engineering, University of Alberta, Edmonton AB T6G 2R3, Canada
Interests: Novel technologies for sustainable smart cities, crowdsensing based monitoring, solar PV systems and their integration to buildings, interaction of solar PV Systems with smart grid, smart space heating/cooling systems integrated with renewable energy systems, energy efficient smart buildings, net-zero energy buildings

Special Issue Information

Dear Colleagues,

There is considerable interest in bridge damage detection and bridge health monitoring and considerable progress has been made in this field in recent years. However, several challenges remain, and road/rail infrastructure owners are hesitant to invest in the levels of instrumentation often envisaged by researchers. Many bridges are small and/or located in remote regions and there may not be access to electrical power. There is considerable interest in damage detection methods that can overcome these issues. With traditional visual inspection, monitoring only occurs occasionally—there is a need to improve on this with systems that monitor frequently or continuously. But perhaps one of the greatest challenges is sensitivity—there is a need for systems and methods that are sensitive to low levels of damage and that can distinguish between damage and other sources of interference such as environmental changes (e.g. temperature changes), operational changes and surface profile deterioration. This Special Issue will address such challenges, publishing papers on bridge damage detection methods that require no electrical power, monitor frequently/continuously, or are highly sensitive to low levels of damage. It will establish the current state-of-the-art and will identify future challenges in bridge damage detection.

Prof. Eugene OBrien
Prof. Mustafa Gül
Guest Editors

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Keywords

bridge
health monitoring
SHM
BHM
damage detection
drive-by
low-energy

Published Papers (7 papers)

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Research

19 pages, 8262 KiB

Open AccessArticle

Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications

by Nima Shirzad-Ghaleroudkhani and Mustafa Gül

Sensors 2020, 20(4), 1190; https://doi.org/10.3390/s20041190 - 21 Feb 2020

Cited by 24 | Viewed by 3029

Abstract

This paper puts forward a novel methodology of employing inverse filtering technique to extract bridge features from acceleration signals recorded on passing vehicles using smartphones. Since the vibration of a vehicle moving on a bridge will be affected by various features related to the vehicle, such as suspension and speed, this study focuses on filtering out these effects to extract bridge frequencies. Hence, an inverse filter is designed by employing the spectrum of vibration data of the vehicle when moving off the bridge to form a filter that will remove the car-related frequency content. Later, when the same car is moving on the bridge, this filter is applied to the spectrum of recorded data to suppress the car-related frequencies and amplify the bridge-related frequencies. The effectiveness of the proposed methodology is evaluated with experiments using a custom-built robot car as the vehicle moving over a lab-scale simply supported bridge. Nine combinations of speed and suspension stiffness of the car have been considered to investigate the robustness of the proposed methodology against car features. The results demonstrate that the inverse filtering method offers significant promise for identifying the fundamental frequency of the bridge. Since this approach considers each data source separately and designs a unique filter for each data collection device within each car, it is robust against device and car features. Full article

(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)

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24 pages, 13300 KiB

Open AccessArticle

Wavelet Packet Singular Entropy-Based Method for Damage Identification in Curved Continuous Girder Bridges under Seismic Excitations

by Dayang Li, Maosen Cao, Tongfa Deng and Shixiang Zhang

Sensors 2019, 19(19), 4272; https://doi.org/10.3390/s19194272 - 02 Oct 2019

Cited by 17 | Viewed by 2654

Abstract

Curved continuous girder bridges (CCGBs) have been widely adopted in the civil engineering field in recent decades for complex interchanges and city viaducts. Unfortunately, compared to straight bridges, this type of bridge with horizontal curvature is relatively vulnerable to earthquakes characterized by large energy and short duration. Seismic damage can degrade the performance of CCGBs, threatening their normal operation and even resulting in collapse. Detection of seismic damage in CCGBs is thus significantly important but is still not well resolved. To this end, a new method based on wavelet packet singular entropy (WPSE) is proposed to identify seismic damage by analyzing the dynamic responses of CCGBs to seismic excitation. This WPSE-based approach features characterizing damage using synergistic advantage of the wavelet packet transform, singular value decomposition, and information entropy. To testify the algorithm, a finite element model of a typical CCGB with two types of seismic damage is built, in which the seismic damage is individually modeled by stiffness reductions at the bottom of piers and at pier-girder connections. The displacement responses of the model to El Centro seismic excitation is used to identify the damage. The results show that damage indices in the WPSE-based approach can correctly locate the seismic damage in CCGBs. Furthermore, the WPSE-based method is competent to identify damage with higher accuracy in comparison with the wavelet packet energy based method, and has a strong immunity to noise revealed by robustness analysis. An array of responses used in this approach paves the way of developing practical technologies for detecting seismic damage using advanced distributed sensing techniques, typically the optical sensors. Full article

(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)

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25 pages, 2647 KiB

Open AccessArticle

Measurement of Three-Dimensional Structural Displacement Using a Hybrid Inertial Vision-Based System

by Xinxiang Zhang, Yasha Zeinali, Brett A. Story and Dinesh Rajan

Sensors 2019, 19(19), 4083; https://doi.org/10.3390/s19194083 - 21 Sep 2019

Cited by 15 | Viewed by 3697

Abstract

Accurate three-dimensional displacement measurements of bridges and other structures have received significant attention in recent years. The main challenges of such measurements include the cost and the need for a scalable array of instrumentation. This paper presents a novel Hybrid Inertial Vision-Based Displacement Measurement (HIVBDM) system that can measure three-dimensional structural displacements by using a monocular charge-coupled device (CCD) camera, a stationary calibration target, and an attached tilt sensor. The HIVBDM system does not require the camera to be stationary during the measurements, while the camera movements, i.e., rotations and translations, during the measurement process are compensated by using a stationary calibration target in the field of view (FOV) of the camera. An attached tilt sensor is further used to refine the camera movement compensation, and better infers the global three-dimensional structural displacements. This HIVBDM system is evaluated on both short-term and long-term synthetic static structural displacements, which are conducted in an indoor simulated experimental environment. In the experiments, at a 9.75 m operating distance between the monitoring camera and the structure that is being monitored, the proposed HIVBDM system achieves an average of 1.440 mm Root Mean Square Error (RMSE) on the in-plane structural translations and an average of 2.904 mm RMSE on the out-of-plane structural translations. Full article

(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)

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19 pages, 3498 KiB

Open AccessArticle

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

by Abdollah Malekjafarian, Fatemeh Golpayegani, Callum Moloney and Siobhán Clarke

Sensors 2019, 19(18), 4035; https://doi.org/10.3390/s19184035 - 19 Sep 2019

Cited by 80 | Viewed by 6127

Abstract

This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels. Full article

(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)

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22 pages, 8209 KiB

Open AccessArticle

A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation

by Chuan-Zhi Dong, Ozan Celik, F. Necati Catbas, Eugene OBrien and Su Taylor

Sensors 2019, 19(14), 3197; https://doi.org/10.3390/s19143197 - 20 Jul 2019

Cited by 32 | Viewed by 4362

Abstract

Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference. Full article

(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)

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20 pages, 8846 KiB

Open AccessArticle

Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer

by Ahmed Elhattab, Nasim Uddin and Eugene OBrien

Sensors 2019, 19(14), 3143; https://doi.org/10.3390/s19143143 - 17 Jul 2019

Cited by 25 | Viewed by 7424

Abstract

Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency. Full article

(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)

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21 pages, 7713 KiB

Open AccessArticle

Scour Damage Detection and Structural Health Monitoring of a Laboratory-Scaled Bridge Using a Vibration Energy Harvesting Device

by Paul C. Fitzgerald, Abdollah Malekjafarian, Basuraj Bhowmik, Luke J. Prendergast, Paul Cahill, Chul-Woo Kim, Budhaditya Hazra, Vikram Pakrashi and Eugene J. OBrien

Sensors 2019, 19(11), 2572; https://doi.org/10.3390/s19112572 - 06 Jun 2019

Cited by 50 | Viewed by 6390

Abstract

A vibration-based bridge scour detection procedure using a cantilever-based piezoelectric energy harvesting device (EHD) is proposed here. This has an advantage over an accelerometer-based method in that potentially, the requirement for a power source can be negated with the only power requirement being the storage and/or transmission of the data. Ideally, this source of power could be fulfilled by the EHD itself, although much research is currently being done to explore this. The open-circuit EHD voltage is used here to detect bridge frequency shifts arising due to scour. Using one EHD attached to the central bridge pier, both scour at the pier of installation and scour at another bridge pier can be detected from the EHD voltage generated during the bridge free-vibration stage, while the harvester is attached to a healthy pier. The method would work best with an initial modal analysis of the bridge structure in order to identify frequencies that may be sensitive to scour. Frequency components corresponding to harmonic loading and electrical interference arising from experiments are removed using the filter bank property of singular spectrum analysis (SSA). These frequencies can then be monitored by using harvested voltage from the energy harvesting device and successfully utilised towards structural health monitoring of a model bridge affected by scour. Full article

(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)

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