Nondestructive testing of asphalt pavements for structural condition evaluation: a state of the art

Goel, Amit; Das, Animesh

doi:10.1080/10589750701848697

Cited by 55 publications

(19 citation statements)

References 74 publications

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“…Many applications of this technique for pavements are reported in literature (e.g. [26][27][28]), although it has been demonstrated that an effective characterization is achieved with datasets exhibiting a frequency of few tens of kHz, which is something not achieved by conventional geophysical instrumentation (with geophones up to 100 Hz).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Pavement testing by integrated geophysical methods: Feasibility, resolution and diagnostic potential

Orlando

Cardarelli

Cercato

et al. 2017

Journal of Applied Geophysics

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This work is focused on the assessment of the diagnostic potential of several geophysical methods when applied to the investigation of a rigid airport pavement. The potential and limit of each technique are evaluated as well as the added value deriving from their integration. Firstly, we reconstruct a high-resolution image of the pavement by a large electromagnetic and georadar screening. An advanced processing of georadar data, implemented through the picking of the arrival times of reflections for each profile, provides a quantitative estimation of the deviation between the design and the as-built thickness of layers. Additionally, electrical tomography has been applied to unequivocally identify the anomalous zones, where higher values of resistivity would be associated to porous zones that are prone to degradation and failure. The seismic tomographic survey had the additional purpose to recover the mechanical properties of the pavement in terms of both P- and S-waves and consequently of elastic constants (Poisson's ratio), whose values were consistent with those recovered in literature. The anomalies detected by each technique are consistent in their indications and they can be correlated to failure phenomena occurring at layer interfaces within the pavement structure or to unexpected variations of the layer thicknesses. The cost-effective geophysical campaign has validated the four-layered system deduced from the original design and has been used to reconstruct a high-resolution map of the pavement in order to discriminate fractures, crack-prone areas or areas where the as-built differs from the original design

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Section: Accepted Manuscriptmentioning

confidence: 99%

Pavement testing by integrated geophysical methods: Feasibility, resolution and diagnostic potential

Orlando

Cardarelli

Cercato

et al. 2017

Journal of Applied Geophysics

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“…4 External evaluation technology is used to detect pavement stress, damage, and surface function and includes deflection, acoustic emission, ground-penetrating radar, ultrasound, infrared thermal imaging, and image technology. 5 However, external evaluation technology lacks spontaneity and hysteresis, and the detection process requires considerable manpower, equipment, and so on. The external evaluation technology cannot monitor microdamage and damage development.…”

Section: Introductionmentioning

confidence: 99%

Detecting concealed damage in asphalt pavement based on a composite lead zirconate titanate/polyvinylidene fluoride aggregate

Chen

Hou

et al. 2019

Struct Control Health Monit

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As an important research topic, the utilization of wireless sensors has attracted widespread attention in pavement engineering recently. In this study, a composite lead zirconate titanate/polyvinylidene fluoride aggregate (CPA) was embedded into asphalt pavement to monitor the concealed damage. A CPAbased method for monitoring concealed damage in asphalt pavement cracks was established. According to the analysis of the testing results of the CPAbased method, it was found that (a) for monitoring changes in the crack width, the amplitude attenuation of the signal is more sensitive than the frequency shift. Thus, the amplitude attenuation is selected and recommended as a monitoring indicator. (b) The root mean square deviation (RMSD) of the amplitude, as a more universal indicator, was proposed for increasing the sensitivity. It is independent of the asphalt mixture properties and grows approximately parabolically with the crack width in asphalt pavement. (c) A relationship model between the crack width and the RMSD was established. The maximum absolute and relative errors between the measured and monitored crack widths are 0.18 mm and 4.2 %, respectively, according to the laboratory investigation. This study provides a reference for the development and application of intelligent monitoring for concealed damage in asphalt mixtures.

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“…In the technical literature, a number of procedures to backcalculate layer moduli from deflection data have been discussed (Goktepe et al, 2005;Goel and Das, 2008). Statics-and dynamicsbased approaches (Chatti et al, 2004;Loizos et al, 2005;Seo et al, 2013) provide both closed-form solutions via layer-elasticity theory and numerical solutions via finite element methods.…”

Section: Traditional Estimation Of Layer Modulimentioning

confidence: 99%

Assessing artificial neural network performance in estimating the layer properties of pavements

Beltrán¹,

Romo²

2014

ing.inv.

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A major concern in assessing the structural condition of existing flexible pavements is the estimation of the mechanical properties of constituent layers, which is useful for the design and decision-making process in road management systems. This parameter identification problem is truly complex due to the large number of variables involved in pavement behavior. To this end, non-conventional adaptive or approximate solutions via Artificial Neural Networks -ANNs -are considered to properly map pavement response field measurements. Previous investigations have demonstrated the exceptional ability of ANNs in layer moduli estimation from non-destructive deflection tests, but most of the reported cases were developed using synthetic deflection data or hypothetical pavement systems. This paper presents further attempts to back-calculate layer moduli via ANN modeling, using a database gathered from field tests performed on three-and four-layer pavement systems. Traditional layer structuring and pavements with a stabilized subbase were considered. A three-stage methodology is developed in this study to design and validate an "optimum" ANN-based model, i.e., the best architecture possible along with adequate learning rules. An assessment of the resulting ANN model demonstrates its forecasting capabilities and efficiency in solving a complex parameter identification problem concerning pavements.Keywords: Artificial neural networks, pavements, non-destructive testing, deflections, layer moduli. RESUMENUna de las principales preocupaciones en la evaluación de la condición estructural de pavimentos flexibles en servicio, es la estimación de propiedades mecánicas de las capas, útiles para el diseño y toma de decisiones en los sistemas de gestión de carreteras. Este problema de identificación de parámetros es realmente complejo, debido al gran número de variables involucradas en el comportamiento de los pavimentos. Para esos fines, se ha considerado modelar la respuesta del pavimentos ante pruebas de campo, mediante soluciones adaptativas o aproximadas no convencionales usando Redes Neuronales Artificiales -RNAs. Las investigaciones previas han demostrado la capacidad excepcional de las RNAs para estimar módulos de capas, a partir de pruebas no destructivas de deflexión; sin embargo, la mayoría de los casos reportados han utilizado datos sintéticos de deflexión, o sistemas de pavimento hipotéticos. En este trabajo se presentan nuevos intentos para retrocalcular los módulos de capa con modelos de RNAs, a partir de una base de datos obtenida de pruebas de deflexión realizadas en campo sobre sistemas de pavimento de tres y cuatro capas; se consideraron estructuras tradicionales y pavimentos con capas de subbase rigidizada. Para el diseño y validación del modelo "óp-timo" de RNAs, es decir, la mejor arquitectura posible y el algoritmo de aprendizaje más adecuado, se desarrolló una metodología en tres etapas. La evaluación del modelo neuronal resultante, muestra su capacidad y eficiencia de predicción para resolver un probl...

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Nondestructive testing of asphalt pavements for structural condition evaluation: a state of the art

Cited by 55 publications

References 74 publications

Pavement testing by integrated geophysical methods: Feasibility, resolution and diagnostic potential

Pavement testing by integrated geophysical methods: Feasibility, resolution and diagnostic potential

Detecting concealed damage in asphalt pavement based on a composite lead zirconate titanate/polyvinylidene fluoride aggregate

Assessing artificial neural network performance in estimating the layer properties of pavements

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