Modulus Inversion Layer by Layer of Different Asphalt Pavement Structures

Cao, Mingming; Huang, Wenze; Zou, Yun; Liu, Guomin

doi:10.1155/2021/1928383

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Based on the displacement measurement of the soft body impact of aluminum plate, Yu et al [9] used BP neural network to invert the bird composition model and showed that the proposed inversion model is more reasonable. Cao et al [10] improved the accuracy of inversion of modulus of each structural layer of asphalt by layer-by-layer inversion method to provide a basis for research. Luo et al [11] used the measured data, combined with GA-BP neural network, to calculate the mechanical parameters of the surrounding rock by displacement inverse analysis method.…”

Section: Introductionmentioning

confidence: 99%

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Wang

Cui

et al. 2023

Advances in Civil Engineering

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Based on similar model tests and finite element simulations, inversion study of the mechanical parameters of the tunnel surrounding rock by combining artificial fish swarm algorithm (AFSA) and digital scattering correlation method is carried out. The inversion of the mechanical parameters of the tunnel surrounding rock is carried out by iterating the optimization algorithm to minimize the objective function value through similar model test with the displacement measurements obtained by the digital scattering correlation method as the known quantity and the nodal displacement simulation values obtained by numerical simulation as the unknown quantity. The results show the following: (1) The new research idea proposed in this paper is effective and feasible and can perform the inversion calculation of the parameters of the soft and weak tunnel surrounding rock. (2) Through the inversion calculation, the parameters related to the surrounding rock show a wave-like change: the parameters increase in the initial and final compaction stages due to the small deformation rate of the tunnel and decrease in the continuous loading stage due to the increasing deformation rate of the tunnel. (3) The displacement field calculated by DSCM in the test is basically the same as the displacement distribution characteristics of the numerical simulation displacement cloud map, and the error of nodal displacement value is within 3%.

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Section: Introductionmentioning

confidence: 99%

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Wang

Cui

et al. 2023

Advances in Civil Engineering

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“…Generally, central deflection can characterize the overall strength of asphalt pavement. Distal deflection can characterize the subgrade strength, and the difference between the maximum deflection 2 of 14 and distal deflection can characterize the tensile stress of the bottom structural layers of asphalt pavement [4].…”

Section: Introductionmentioning

confidence: 99%

Deflection Prediction of Rehabilitation Asphalt Pavements through Deep Forest

Wu¹,

Chen²,

Jiang³

2022

Coatings

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The deep forest is a powerful deep-learning algorithm that has been applied in certain fields. In this study, a deep forest (DF) model was developed to predict the central deflection measured by a falling weight deflectometer (FWD). In total, 11,075 samples containing information related to pavement structure, traffic conditions, and weather conditions were extracted from the LTPP dataset. The performance of the DF model with custom backend settings was compared with that of models random forest (RF), multilayer perceptron (MLP), and DF built on the sklearn backend. All four deep-learning algorithms could identify the complex relationship between central deflection and relevant feature variables with high accuracy and stability. The learning and generalization abilities of DF was stronger than those of MLP and RF. The predictive performance and computation time of DF (custom) were better than those of DF (sklearn), indicating that the custom model was superior to the highly encapsulated model with sklearn as the backend. Feature importance analysis indicated that the drop load of FWD was the key factor influencing deflection. In addition, structural number, annual precipitation, and annual kilo equivalent standard axle load (kESAL) are very important features related with deflection. The feature importance of rehabilitation improvement thickness was less than the drop load, climatic factors, kESAL, structural number, and layer thickness.

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“…Sargand et al [10] relied on Ohio test track permanent asphalt road interview section WAY-30 to test the effects of speed, load (biaxial 179 kN and uniaxial 125 kN), and load offset on the dynamic response of asphalt pavement at high and low temperatures, but the axle load and pavement structure form are relatively single. Cao et al [11,12] analyzed the deflection and modulus characteristics of inverted asphalt pavement and semirigid asphalt pavement based on FWD measured deflection basin data. Assogba et al [13] studied the strain response law of the bottom of each asphalt layer in the pavement structure by combining numerical simulation and field test, and overloading can lead to extremely unfavorable fatigue life of semirigid asphalt pavements.…”

Section: Introductionmentioning

confidence: 99%

Influence of Axle Load and Asphalt Layer Thickness on Dynamic Response of Asphalt Pavement

Cao¹,

Huang²,

Wu³

2022

Geofluids

Self Cite

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Based on the test data of vehicle load test on asphalt pavement, the significance of dynamic response index of different asphalt pavement to axle load change of single and double rear axle trucks is analyzed. The changes of horizontal strain at the bottom of asphalt layer, vertical compressive stress at the top of subgrade, and vertical displacement of transition layer and bottom compressive stress before and after laying of middle and upper layers are revealed. The test results show that reducing the thickness of asphalt layer or increasing the vehicle axle load can lead to the more unfavorable stress-strain distribution in asphalt pavement under driving load. The influence of the fluctuation of truck axle load or asphalt layer thickness on the stress-strain distribution in asphalt pavement varies with the type of pavement structure, pavement response index, vehicle driving speed, vehicle type, and the horizon of the measuring point. The transverse strain at the bottom of the asphalt layer is more affected by the axle load and the thickness of the asphalt layer than the longitudinal strain at the bottom of the asphalt layer. The measured value of the dynamic response index of the semirigid asphalt pavement is more affected by the axle load than that of the inverted asphalt pavement. However, the measured value of the dynamic response index of the semi-rigid asphalt pavement is less affected by the thickness of the asphalt layer when the thickness of the asphalt layer changes within a certain range. The tensile strain part of the longitudinal strain at the bottom of the asphalt layer and the compressive strain part of the transverse strain at the bottom of the asphalt layer are greatly affected by the thickness of the asphalt layer. The sensitivity of fatigue life of asphalt pavement of inverted structure to driving speed and axle load is less than that of semirigid structure. The research results can provide reference for the analysis of the asphalt pavement disease mechanism and provide guidance for asphalt pavement structural design and service life analysis.

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Modulus Inversion Layer by Layer of Different Asphalt Pavement Structures

Cited by 5 publications

References 19 publications

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Deflection Prediction of Rehabilitation Asphalt Pavements through Deep Forest

Influence of Axle Load and Asphalt Layer Thickness on Dynamic Response of Asphalt Pavement

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