Road deformation monitoring and event detection using asphalt‐embedded distributed acoustic sensing (DAS)

Hubbard, Peter G.; Ou, Ruonan; Xu, Tengfang; Luo, Linqing; Nonaka, Hayato; Karrenbach, Martin; Soga, Kenichi

doi:10.1002/stc.3067

Cited by 18 publications

(8 citation statements)

References 41 publications

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“…These "dark fibres" are not necessarily optimally deployed for new applications that DAS has to offer. As these new applications are being developed, advanced deployment practices like micro-trenching (as was performed for our cable segment of study) and other forms of fibre-ground coupling could help to improve the signalto-noise ratio of DAS measurements, and subsequently the performance of any data analysis workflow [45]. Suggested future work can also target the interplay between existing (spatially sparse) instrumentation and (spatially dense) DAS analyses, for instance for deeper verification of DAS-based speed estimates and the role of non-constant vehicle speeds.…”

Section: Perspectives For Smart Citiesmentioning

confidence: 99%

Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Ende¹,

André²,

Sladen³

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Distributed Acoustic Sensing (DAS) is a novel vibration sensing technology that can be employed to detect vehicles and to analyse traffic flows using existing telecommunication cables. DAS therefore has great potential in future "smart city" developments, such as real-time traffic incident detection. Though previous studies have considered vehicle detection under relatively light traffic conditions, in order for DAS to be a feasible technology in real-world scenarios, detection algorithms need to also perform robustly under a wide range of traffic conditions. In this study we investigate the potential of roadside DAS for the simultaneous detection and characterisation of the velocity of individual vehicles. To improve the temporal resolution and detection accuracy, we propose a self-supervised Deep Learning approach that deconvolves the characteristic car impulse response from the DAS data, which we refer to as a Deconvolution Auto-Encoder (DAE). We show that deconvolution of the DAS data with our DAE leads to better temporal resolution and detection performance than the original (non-deconvolved) data. We subsequently apply our DAE to a 24-hour traffic cycle, demonstrating the feasibility of our proposed method to process large volumes of DAS data, potentially in near-real time.

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Section: Perspectives For Smart Citiesmentioning

confidence: 99%

Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Ende¹,

André²,

Sladen³

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Pavement health monitoring interprets the connection between external loads and internal response by placing sensors within the pavement structure and analyzing the monitoring data collected. It has provided many new methods for pavement mechanics verifcation, structural design, and maintenance decisions [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Measurement Accuracy Analysis of Distributed Fiber Optic Sensors for Asphalt Mixture Based on the DEM-FDM Coupled Method

Dong

Zhang

et al. 2023

Structural Control and Health Monitoring

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Distributed fiber optic sensors (DFOSs) have been effectively used for pavement health monitoring. However, the inhomogeneity of the asphalt mixture and the characteristics of the sensor affect the measurement accuracy, which in turn affects the performance evaluation of asphalt pavement. In this study, the strain of nonembedded DFOS specimens was used as a reference and compared with the strain of embedded DFOS specimens to analyze the accuracy of DFOS based on the four-point bending test. To further improve accuracy, a numerical simulation model was established by coupling the discrete element method (DEM) and the finite difference method (FDM), and feasibility of the model was verified by comparing it with the load-displacement curves obtained from laboratory tests. The results of the laboratory tests and numerical simulations showed a linear relationship between the reference strain and the DFOS strain. Therefore, a strain correction method was proposed for the DFOS based on the DEM-FDM method, and the strain correction coefficient was used as the evaluation index. In addition, an orthogonal test was performed to analyze the influence of design parameters, including elastic modulus, section height, and section width, on the accuracy of the DFOS. Through variance and range analysis, it was found that elastic modulus has a significant effect on the strain measurement accuracy, followed by the section height and the section width. In summary, this study proposed an efficient strain correction method suitable for the application of DFOS in pavements considering the material characteristics of the asphalt mixture and the embedded sensor.

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“…DAS technology can be applied for traffic monitoring using either purpose-installed optical fiber cables [ 9 , 10 , 11 , 12 , 13 ] or pre-existing optical fiber cables owned by public administrations or telecommunication operators that are already installed along roads [ 14 , 15 , 16 ]. Using pre-existing optical cables, which are usually installed inside a duct, can help avoid installation and maintenance costs while speeding up the implementation time of this technology.…”

Section: Introductionmentioning

confidence: 99%

Long-Range Traffic Monitoring Based on Pulse-Compression Distributed Acoustic Sensing and Advanced Vehicle Tracking and Classification Algorithm

Corera

Piñeiro

Navallas

et al. 2023

Sensors

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We introduce a novel long-range traffic monitoring system for vehicle detection, tracking, and classification based on fiber-optic distributed acoustic sensing (DAS). High resolution and long range are provided by the use of an optimized setup incorporating pulse compression, which, to our knowledge, is the first time that is applied to a traffic-monitoring DAS system. The raw data acquired with this sensor feeds an automatic vehicle detection and tracking algorithm based on a novel transformed domain that can be regarded as an evolution of the Hough Transform operating with non-binary valued signals. The detection of vehicles is performed by calculating the local maxima in the transformed domain for a given time-distance processing block of the detected signal. Then, an automatic tracking algorithm, which relies on a moving window paradigm, identifies the trajectory of the vehicle. Hence, the output of the tracking stage is a set of trajectories, each of which can be regarded as a vehicle passing event from which a vehicle signature can be extracted. This signature is unique for each vehicle, allowing us to implement a machine-learning algorithm for vehicle classification purposes. The system has been experimentally tested by performing measurements using dark fiber in a telecommunication fiber cable running in a buried conduit along 40 km of a road open to traffic. Excellent results were obtained, with a general classification rate of 97.7% for detecting vehicle passing events and 99.6% and 85.7% for specific car and truck passing events, respectively.

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Road deformation monitoring and event detection using asphalt‐embedded distributed acoustic sensing (DAS)

Cited by 18 publications

References 41 publications

Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Measurement Accuracy Analysis of Distributed Fiber Optic Sensors for Asphalt Mixture Based on the DEM-FDM Coupled Method

Long-Range Traffic Monitoring Based on Pulse-Compression Distributed Acoustic Sensing and Advanced Vehicle Tracking and Classification Algorithm

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