Real-time drive-by bridge damage detection using deep auto-encoder

Li, Zhenkun; Lin, Weiwei; Zhang, Youqi

doi:10.1016/j.istruc.2022.11.094

Cited by 24 publications

(16 citation statements)

References 49 publications

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“…The data processing operations are performed on them. It is deemed that the minimum time period required for the drive-by measurement can be met as long as the chosen time period is longer than one full vibration cycle of the bridge [ 43 ]. This can be calculated by

, or 0.03 s in this study, where

is the natural vibration period of the bridge and

is the bridge’s first natural frequency.…”

Section: Results and Analysismentioning

confidence: 99%

“…The effects of

will be also discussed below. It should be noted that the speed difference in such a method should not be too large; some earlier research [ 41 , 42 , 43 ] found an empirically acceptable velocity difference of roughly 40%. The pooling process can further mitigate noise, equalize data size, and reduce computational costs.…”

Section: Methodsmentioning

confidence: 99%

“…Acceleration data obtained from the vehicle sensor were utilized to construct the dataset. In this study, damage cases were simulated by adding weights to the bridge model, known as “artificial damage”, to avoid permanent or irreversible destruction to the material, which has been proven feasible by many studies [ 31 , 35 , 43 , 50 ]. The data acquisition system in this study was driven by a PC, connected to the sensor by wires, and had a sampling rate of 2 kHz; the sensor was manufactured by Bruel and Kjaer (TYPE 4371) [ 51 ].…”

Section: Experimental Programmentioning

confidence: 99%

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A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study

Lan

Lin

2023

Materials

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Constructional material deterioration and member damage can cause changes in the dynamic characteristics of bridge structures, and such changes can be tracked in the responses of passing vehicles via the vehicle-bridge interaction (VBI). Though data-driven methods have shown promising results in damage inspection for drive-by methods, there is still much room for improvement in their performance. Given this background, this paper proposes a novel time-domain signal processing algorithm for the raw vehicle acceleration data of data-driven drive-by inspection methods. To achieve the best data processing performance, an optimizing strategy is designed to automatically search for the optimal parameters, tuning the algorithm. The proposed method intentionally overcomes the difficulties in the application of drive-by methods, such as measurement noise, speed variance, and enormous data volumes. Meanwhile, the use of this method can greatly improve the accuracy and efficiency of Machine Learning (ML) models in vehicle-based damage detection. It consists of a filtering process to denoise the data, a pooling process to reduce data redundancy, and an optimizing procedure to maximize algorithm performance. A dataset is obtained to validate the proposed algorithm through laboratory experiments with a scale truck model and a steel beam. The results show that, compared to using raw data, the present algorithm can increase the average accuracy by 12.2–15.0%, and the average efficiency by 35.7–96.7% for different damaged cases and ML models. Additionally, the functions of filtering and pooling operations, the influence of window function parameters, as well as the performance of different sensor locations, are also investigated in the paper. The goal is to present a signal processing algorithm for data-driven drive-by inspection methods to improve their detection performance of bridge damage caused by material deterioration or structural change.

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, or 0.03 s in this study, where

is the natural vibration period of the bridge and

is the bridge’s first natural frequency.…”

Section: Results and Analysismentioning

confidence: 99%

“…The effects of

Section: Methodsmentioning

confidence: 99%

Section: Experimental Programmentioning

confidence: 99%

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A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study

Lan

Lin

2023

Materials

Self Cite

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“…Although these studies demonstrate the ability to detect the presence of damage and its progression over time, they cannot quantify the extent of the damage or identify the type and location of the damage. Zhenkun Li et al (2023b) trained a deep auto-encoder model with the frequency components of measured signals from a passing vehicle.…”

Section: 3mentioning

confidence: 99%

“…Zhenkun Li et al. (2023b) trained a deep auto‐encoder model with the frequency components of measured signals from a passing vehicle. The method was tested experimentally in the laboratory, and the auto‐encoder was trained using measurements from the vehicle crossing the healthy bridge.…”

Section: Introductionmentioning

confidence: 99%

A deep‐learning framework for classifying the type, location, and severity of bridge damage using drive‐by measurements

Corbally,

Malekjafarian

2023

Computer aided Civil Eng

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This paper proposes a new deep‐learning framework for drive‐by bridge condition monitoring. The proposed approach represents a bridge monitoring regime that enables the presence, type, location, and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre‐measured training data. The computational framework adopts a numerical vehicle–bridge interaction (VBI) model, which is automatically calibrated using only the vehicle vibration measurements. The calibrated model is used to generate labeled training data, eliminating the practical difficulties associated with collecting training data for damaged bridge scenarios. The numerically simulated data are used to train a convolutional neural network that can then classify the actual damage characteristics of a bridge. The method is tested using a laboratory‐scale VBI model, and results show that the algorithm can accurately identify the presence of seized bearings and cracking in the bridge beams. The algorithm shows good accuracy when identifying the type and extent of damage. The location of bearing damage can generally be identified; however, the location of cracking is less accurate at lower damage levels. The proposed framework represents a significant improvement on existing techniques for indirect bridge monitoring.

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Using Contact Residual Responses of a 3-DOF Scooter to Identify First Few Frequencies of the Footbridge

Li,

Lan,

Lin

2023

Lecture Notes in Civil Engineering

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Real-time drive-by bridge damage detection using deep auto-encoder

Cited by 24 publications

References 49 publications

A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study

A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study

A deep‐learning framework for classifying the type, location, and severity of bridge damage using drive‐by measurements

Using Contact Residual Responses of a 3-DOF Scooter to Identify First Few Frequencies of the Footbridge

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