Study on infrared differential thermal non-destructive testing technology of the permeability of hot mix asphalt pavements

Wang, Duanyi; Shi, Jicun

doi:10.1088/1755-1315/69/1/012109

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…Several NDT methods such as ultrasonic pulse-echo, groundpenetrating radar, infrared differential thermal, and FWD have been proposed to analyze the stress-strain response of asphalt pavement [8][9][10][11]. The authors use pavement deflection measurements under FWD loading for a variety of purposes.…”

Section: Related Workmentioning

confidence: 99%

“…x (10) in which cx t i is the value of chaos variable cx i after the t-th iteration. When x i ∈ [0, 1] and cx i ∈ {0.25, 0.5, 0.75}, chaos will occur.…”

Section: Cpsomentioning

confidence: 99%

“…The solution variable x i ∈ [a, b] can be mapped back and forth with cx i by eqs. ( 9) and (10), where a and b are the maximum and minimum values of the solution space.…”

Section: Cpsomentioning

confidence: 99%

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CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Shi

Cao

et al. 2022

Sci. China Technol. Sci.

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The use of non-destructive testing (NDT) equipment, such as the falling weight deflectometer (FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale field accelerated pavement testing track named RIOHTrack (Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm (CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59. The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Significantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

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Section: Related Workmentioning

confidence: 99%

“…x (10) in which cx t i is the value of chaos variable cx i after the t-th iteration. When x i ∈ [0, 1] and cx i ∈ {0.25, 0.5, 0.75}, chaos will occur.…”

Section: Cpsomentioning

confidence: 99%

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CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Shi

Cao

et al. 2022

Sci. China Technol. Sci.

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“…In this regard, the limit value of the level of damageability should be in the range of values from 0.5 (increase in the speed and intensive growth of defects) to 0.25 (considering structural bonds percolation, when their stability is lost). Figure 6 shows the data of joint post-processing of experimental data (Table 1) and the data obtained earlier (Wang & Shi, 2017), when developing the method for assessing water permeability of road asphalt concrete surfaces.…”

Section: Experimental Studies Of Damageability Of Asphalt Concrete Pamentioning

confidence: 99%

“…Previous studies (Wang & Shi, 2017) have allowed us to develop a methodology for assessing water permeability of road asphalt concrete surfaces based on recording changes in their temperature when drying after wetting, which is still used today for the evaluation in the cold recycling process, as an optional one.…”

Section: Introductionmentioning

confidence: 99%

Prospects for Evaluating the Damageability of Asphalt Concrete Pavements During Cold Recycling

Zankavich¹,

Khroustalev

Лю³

et al. 2020

BJRBE

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The article considers improvement of the methodology for accounting for the degradation of asphalt concrete working in the upper layers of the pavement. Development of recycling technologies for road structures is an ongoing process; it allows reaching a higher quality of reclaimed materials and using them for subsequent construction of structural layers, including the upper layers without the protective ones, as well as during repair and reconstruction of roads of various technical categories. At the same time, the system of pre-project assessment (diagnostics) of the state of asphalt concrete pavements cannot be considered optimal and effective because the determined indicators demonstrate that, firstly, various surface and structural defects are present, and, secondly, that the indicators mentioned above are more relevant to the road structure as a whole. The joint handling of the theoretical and experimental data allows concluding that damageability level depends on the physical, mechanical and structural properties, the main being maximal structural strength and the number of elastic bonds involved in the deformation process. A variant of modelling of asphalt concrete damageability depending on the work capacity is proposed, when the reduced amount of dissipated energy is replaced with sufficient accuracy for practice by the ratio of the actual number of load application cycles (freezing and thawing cycles) to the limit. A correlation between the level of damageability and the kinetics of changes of the interpore space of asphalt concrete under the influence of strain (temperature, climatic factors) has been established. Results allow fixing (predicting) the level of damageability by measuring the level of water permeability. The research methodology and equipment for implementation thereof was developed earlier, it can be effectively used at the stage of pre-project diagnosis.

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The detection and evaluation for the internal defection in industrial pipeline based on the virtual heat source temperature field

Zhang

Yuan

2019

J Therm Anal Calorim

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Study on infrared differential thermal non-destructive testing technology of the permeability of hot mix asphalt pavements

Cited by 4 publications

References 5 publications

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Prospects for Evaluating the Damageability of Asphalt Concrete Pavements During Cold Recycling

The detection and evaluation for the internal defection in industrial pipeline based on the virtual heat source temperature field

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