Application of infrared thermography for debonding detection in asphalt pavements

“…As a result, it is difficult to carry out a large number of sampling inspections, and it is difficult to reflect the overall quality of the road structure using only a small number of results. As a nondestructive testing method, ground-penetrating radar (GPR) [9][10][11] has the advantages of portability, high detection efficiency, high resolution, and strong anti-interference ability compared with ultrasonic testing [12,13], core sampling drilling, and infrared imaging [14]. Chahine et al proposed an algorithm for ultrathin layer thickness estimation based on independent component analysis.…”

Section: Introductionmentioning

confidence: 99%

A BP Neural Network Method for Grade Classification of Loose Damage in Semirigid Pavement Bases

Zhang

Liu

Zhong

et al. 2021

Advances in Civil Engineering

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This study aims to address the problem that loose damage of the pavement base course cannot currently be quantitatively identified, and thus the classification and recognition of the extent of looseness mainly rely on empirical judgments. Based on the finite-difference time-domain (FDTD) method, a backpropagation (BP) neural network identification method for loose damage of a semirigid base is presented. The FDTD method is used to simulate a semirigid base road model numerically with different degrees of looseness, and the eigenvalue parameters for recognition of the presence and extent of the looseness of the base layer are obtained. Then, a BP neural network identification method is used to classify and identify the loose damage of the base course. The results show that the classification and recognition of simulated electromagnetic waves have an accuracy of over 90%; the classification and recognition of radar data from an actual project have a recognition accuracy of over 80%. The good agreement between the classification and recognition results for the simulated data and measured data verifies the feasibility of the classification and recognition method, which can provide a new method for the use of ground-penetrating radar to detect loose damage and the extent of looseness in the base.

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“…These studies focus on vehicle loads, and strain gauges need to be installed between layers of pavements, so more convenient ways are desirable. Vyas et al applied infrared thermography to detect debonding condition [3], but thermographic methods largely depend on temperature.…”

Section: Introductionmentioning

confidence: 99%

Detecting Delamination of Pavement Layers in Airfield Runways Using Local Vibration Testing and Machine Learning

Fujisaku

Naito

Shirai³

et al. 2021

Lecture Notes in Civil Engineering

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Delamination between the layers of asphalt pavement in airfield runways is studied by local vibration testing and machine learning. Debonding between pavement layers is modeled by placing sand or paper between the layers in the field experiment. The vibration testing is able to detect the delamination by the decreasing response of resonant frequency, however, a large variability was produced in the data taken during the summer season. This is likely due to the large variations of asphalt temperature, consequently changing the asphalt properties. For the specific case, machine learning is used to assist in capturing the debonding condition of the layers. The experimental data is first studied by supervised learning using a support vector machine (SVM); however, the damage was not detected well due to the lack of specific examples (i.e., delamination of the layers with exact depth) for input. Instead, machine learning using a principal component analysis (PCA) is applied where only standard data (i.e., without delamination) is given so that when an abnormality such as delamination of the layers is present, it is detected. Using this method, the presence of the delamination of the pavement layers was identified by an accuracy rate of around 90%. This indicates the proposed method, based on local vibration testing, can detect delamination of asphalt pavements with a high accuracy.

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Application of infrared thermography for debonding detection in asphalt pavements

Cited by 30 publications

References 10 publications

A novel technique for asphalt pavement interlayer contact condition diagnosis

A novel technique for asphalt pavement interlayer contact condition diagnosis

A BP Neural Network Method for Grade Classification of Loose Damage in Semirigid Pavement Bases

Detecting Delamination of Pavement Layers in Airfield Runways Using Local Vibration Testing and Machine Learning

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