Artificial Neural Networks Application in the Backcalculation Process of Flexible Pavement Layers Elasticity Modulus

Šarić, Ammar; Pozder, Mirza

doi:10.1007/978-3-319-71321-2_49

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…Since the 1980s, falling weight deflectometer (FWD) measurements have been extensively used for evaluating pavement situ conditions (Uddin et al., 1985). With FWD testing, the pavement surface deflection is measured to evaluate the overall bearing capability of pavement or to back‐calculate the pavement layer modulus (Goktepe et al., 2006; Saric & Pozder, 2018). In practice, the layer modulus back‐calculation is a matching process combining mechanical analysis with multi‐layered elastic system theory and comparing measured and calculated deflections.…”

Section: Introductionmentioning

confidence: 99%

Modeling and validation of impact forces for back‐calculation of pavement surface moduli

Zhao

Liu

Wang

et al. 2023

Computer aided Civil Eng

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When an impact load is sufficiently small, its influence on the pavement structure is mainly from the surface layer material. To explore the influence depth of an impact load and back‐calculation of the pavement surface modulus, both numerical calculation and experimental testing were conducted, and the results are presented in this paper. The numerical calculation was performed through a DEM‐FDM‐coupled model. After a new modulus back‐calculation algorithm is formed by analyzing the numerical modeling results, experimental tests were also conducted for verification, and the results were analyzed. The max value of the falling weight impact force was closely related to the elastic modulus of the material, and the influence depth was controllable. Finally, it is proved that the method can be used to calculate the surface layer modulus and estimate the surface layer thickness.

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Section: Introductionmentioning

confidence: 99%

Modeling and validation of impact forces for back‐calculation of pavement surface moduli

Zhao

Liu

Wang

et al. 2023

Computer aided Civil Eng

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show abstract

“…The surface defections can be used to characterize the whole bearing capacity of the pavement but may be unsuitable for interpreting the stiffness of each layer. [15][16][17][18][19] Non-uniqueness is a well-known issue for the deflection-based stiffness evaluation method due to the existence of many local optima in the identification search space with multimodal nature for the multi-layered medium. b.…”

Section: Introductionmentioning

confidence: 99%

Stiffness identification method for asphalt pavement layers and interfaces using monitoring data from built-in sensors

Zhang

Dong

2022

Structural Health Monitoring

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The bearing capacity of asphalt pavement gradually deteriorates due to repeated traffic loads. As the crucial mechanical index to evaluate the bearing capacity, the evolution of the stiffness of pavement layers and interfaces is necessary to be mastered. This paper proposes a stiffness identification method for the asphalt pavement layers and interfaces by using monitoring data from built-in sensors. First, the analytical solution of multi-layered elastic/viscoelastic medium with imperfect interlayer subjected to moving load is derived, and the theoretical relationship between stiffness and mechanical response can be obtained. The sensor layout is optimized on the basis of the theoretical relationship. Then, the stiffness identification method is developed, and its feasibility is theoretically explored through an example of three-layered elastic/viscoelastic medium. Finally, the proposed stiffness identification method is applied to a realistic asphalt pavement to validate its reliability. Results show that the proposed method can evaluate the stiffness of pavement layers (including elastic and viscoelastic properties) and interfaces. It is noteworthy that the stiffness identification method by using monitoring data from built-in sensors can be performed in real-time under each passing vehicle load, and is helpful to understand the damage behavior of pavement and guide maintenance decision-making.

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A Back-Analysis Technique for Condition Assessment of Ballasted Railway Tracks

Fathi

Mehravar

2021

Lecture Notes in Civil Engineering

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Artificial Neural Networks Application in the Backcalculation Process of Flexible Pavement Layers Elasticity Modulus

Cited by 5 publications

References 1 publication

Modeling and validation of impact forces for back‐calculation of pavement surface moduli

Modeling and validation of impact forces for back‐calculation of pavement surface moduli

Stiffness identification method for asphalt pavement layers and interfaces using monitoring data from built-in sensors

A Back-Analysis Technique for Condition Assessment of Ballasted Railway Tracks

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