Viscoelastic Response Modelling of a Pavement under Moving Load

Ahmed, Abubeker W; Erlingsson, Sigurður

doi:10.1016/j.trpro.2016.05.343

Cited by 19 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rapidity of the proposed method warrants its application to quasi-real time problems, which could include regional traffic flow management, incident or anomaly detection, and policy testing in (rural) localities where traffic cameras are scarce or absent. Even in urban environments with dense traffic camera coverage, DAS-based traffic monitoring is still a valuable asset, as the deformation patterns induced by vehicles themselves encode the state of the road through effective elastic (and viscous [42]) properties of the tarmac. Longterm variations in the deformation patterns might reveal road degradation, in a similar manner as railway track and train wheel condition is evaluated with DAS [36], [43], [44].…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Even in urban environments with dense traffic camera coverage, DAS-based traffic monitoring is still a valuable asset, as the deformation patterns induced by vehicles themselves encode the state of the road through effective elastic (and viscous; [42]) properties of the tarmac. Long-term variations in the deformation patterns might reveal road degradation, in a similar manner as railway track and train wheel condition is evaluated with DAS [36], [43], [44].…”

Section: Perspectives For Smart Citiesmentioning

confidence: 99%

Deep Deconvolution for Traffic Analysis with Distributed Acoustic Sensing Data

Ende¹,

André²,

Sladen³

et al. 2022

Preprint

View full text Add to dashboard Cite

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 heavy 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.

show abstract

“…The mechanical parameters needed in this model, in which all layers in the system are considered homogeneous and isotropic, are the elasticity modulus (E) and Poisson's ratio (υ) of the layers (Huang, 2004;Jeong, 2005;Singh & Sahoo, 2020). However, under real field conditions, HMA layers act as a viscoelastic material, and their mechanical responses depend on temperature and loading time (Ahmed & Erlingsson, 2016;Chen, Pan, & Green, 2009;Jeong, 2005;Koohmishi, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Flexible pavements consist of surface, base and, sub-base layers whose mechanical properties are quite different from each other. In this layered structure, the bituminous surface layer exhibits a viscoelastic behavior (Ahmed & Erlingsson, 2016;Mistry & Roy, 2020;Safi et al, 2018), while the granular base and sub-base layers can be characterized as linear elastic or non-linear elastic.…”

Section: Introductionmentioning

confidence: 99%

Functional grading of viscoelastic defined hot mix asphalt layers

Bostancıoğlu

2020

rdlc

View full text Add to dashboard Cite

In the design of layered flexible pavements with the mechanistic-empirical (M-A) method, the material characterization of layers is critical for the correct calculation of mechanical responses. The surface layers of flexible pavements produced as hot mix asphalt (HMA) behave as a visco-elastic material under real field conditions. In this study, in which the HMA surface layer was defined viscoelastic by creep-compliance method, functional grading was applied to the surface layer at the same time to increase the fatigue strength of the pavement. Functional grading application was performed in the form of 3, 5, 7, and 9 sub-layers with linear and exponential functions. The pavement life values due to the mechanical responses that occurred in the pavement were determined by the Asphalt Institute and Shell methods. The study results show that the fatigue strength of the viscoelastic surface layer increases significantly as a result of functional grading. Especially in sections with exponential grading, an improvement of more than 10% was achieved even when a small number of sub-layers used. These results show that functional grading can also be successfully applied in field studies, and even with three sub-layers, significant improvements can be provided

show abstract

Viscoelastic Response Modelling of a Pavement under Moving Load

Cited by 19 publications

References 15 publications

Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Deep Deconvolution for Traffic Analysis with Distributed Acoustic Sensing Data

Functional grading of viscoelastic defined hot mix asphalt layers

Contact Info

Product

Resources

About