Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning

Malekjafarian, Abdollah; Moloney, Callum; Golpayegani, Fatemeh

doi:10.1007/978-3-030-64594-6_67

Cited by 3 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks consist of incoming data, hidden data, outgoing data, weights and bias, an activation function, and a summing node [36,57]. Each level incorporates several units of calculation called a neuron [30,41], which takes its input data from the previous level and provides output data for the next level. The input level provides the input data of the network, which are fed to the hidden level.…”

Section: Ann Backgroundmentioning

confidence: 99%

“…There are many researchers who have used cluster-based and data-driven approaches for bridge health monitoring [37][38][39][40] and have shown promising results for effective monitoring systems. Malekjafarian et al [31,41] recently applied the concepts of machine learning on drive-by monitoring of the structures. They proposed the use of an ANN model using vehicle data, which can detect bridge frequencies and cracks on the deck.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train

Malekjafarian,

Sarrabezolles,

Khan

et al. 2023

Sensors

View full text Add to dashboard Cite

In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed.

show abstract

Section: Ann Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train

Malekjafarian,

Sarrabezolles,

Khan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Data-driven and machine-learning methods have emerged as effective alternatives to modal-parameter-based approaches for damage localization and quantification, with various damage-sensitive features being proposed [7]. Nevertheless, one main limitation of these methods is the requirement for a sufficient labeled training dataset, which can be difficult to obtain due to the scarcity of damaged bridges in the real world.…”

Section: Introductionmentioning

confidence: 99%

Enabling Robustness to Vehicle-Bridge Variability in Drive-by Bridge Health Monitoring Through Physics-Informed Signal Decomposition

XU,

AGGARWAL,

NOH

et al. 2023

Proceedings of the 14th International Workshop on Structural Health Monitoring

View full text Add to dashboard Cite

Bridge health monitoring (BHM) is important due to its benefits in detecting and diagnosing potential damages to bridges, providing early warning signals, and guiding maintenance decisions. Compared to manual inspection and fixed-sensors-based monitoring, drive-by BHM leverages vibration responses measured from vehicles passing over bridges to indirectly diagnose bridge damages, offering a rapid, mobile, and economical complementary solution. However, vehicle-bridge interaction (VBI) systems have large variations in bridge configurations, vehicle suspension systems, driving speeds, and so on, making it challenging to develop a damage diagnosis algorithm that is robust to vehicle-bridge variability. Moreover, existing approaches often require vehicle speed within a specific range to provide both informative and reliable signals, limiting their practical applications. To address these challenges, we introduce a damage diagnosis approach that extracts damage-sensitive features and enhances their robustness to vehicle-bridge variability through physics-informed signal decomposition. Our approach first pre-processes and decomposes the vehicle vibration signal using the synchro-squeezed wavelet transform (SWT) because of its anti-noise property and its ability to represent the non-stationary and time-varying signals from drive-by vehicles. Then, a damage-sensitive signal is reconstructed using the inverse SWT within a physics-informed frequency band which excludes the vehicle and bridge resonances while keeping the damage-sensitive information. Peak features, such as peak location and energy, are input to the Gaussian Mixture Model clustering algorithm for diagnosing bridge damage in an unsupervised fashion. The performance of the proposed approach is evaluated on a numerical VBI model, which includes three vehicle types, six bridge lengths, and three bridge cross-sections, and takes into consideration the variability in vehicle properties and speed. The results validate that the extracted features are damage-sensitive and robust to various vehiclebridge systems, achieving an overall mean absolute percentage error of 0.78% for damage localization and 13.13% for damage quantification.

show abstract

“…(i) A straightforward method originates from processing the data acquired by a single passage of an instrumented vehicle. (ii) A second method is based on processing multiple passages of an instrumented vehicle on the same deck [16]. (iii) A hybrid approach includes measuring a reference sensor fixed on the bridge deck synchronized with the moving one [17,18].…”

Section: Introductionmentioning

confidence: 99%

Experimental Estimation of the Elastic Modulus of Concrete Girders from Drive‐By Inspections with Force‐Balance Accelerometers

Aloisio

Alaggio

2021

Shock and Vibration

View full text Add to dashboard Cite

Parametric identification of bridges using instrumented vehicles can be challenging, mainly due to the reduced length of the time series associated with the bridge span under test. This research discusses the practicability of a time-domain identification method based on the use of an instrumented vehicle. The highest cross-correlation between the bridge response from an elementary analytical model and the experimental one, acquired by a moving force-balance accelerometer, yields the unknown model parameter. The effect of vehicle-bridge interaction is removed by proper filtering of the signals. Specifically, the authors estimate the elastic moduli of seven prestressed concrete bridges and compare a subset of the results to the outcomes of static load tests carried out on the same bridges. There is a good correlation between the elastic moduli from the instrumented vehicle and those from static load tests: the method grasps the approximate value of the elastic modulus of concrete. Still, the data do not return an excellent match due to the bias in the estimation of the deflection shape—the paper remarks on the issues faced during the experimental tests and proposes possible enhancements of these procedures.

show abstract

Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning

Cited by 3 publications

References 13 publications

A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train

A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train

Enabling Robustness to Vehicle-Bridge Variability in Drive-by Bridge Health Monitoring Through Physics-Informed Signal Decomposition

Experimental Estimation of the Elastic Modulus of Concrete Girders from Drive‐By Inspections with Force‐Balance Accelerometers

Contact Info

Product

Resources

About