Advances in backcalculating the mechanical properties of flexible pavements

Göktepe, A. Burak; Ağar, Emine; Lav, Abdullah Hilmi

doi:10.1016/j.advengsoft.2005.10.001

Cited by 93 publications

(42 citation statements)

References 22 publications

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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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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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“…Lack of systematization regarding the depth of subgrade to be analyzed is also visible in foreign standards, catalogues and design guidelines. For example, in France the subgrade is analyzed to a depth of 1.0 m below the surface of ground works [38], and about 2.0 m in [17,24,39] According to [40], the maximum thickness of the subgrade involved in the construction of the pavement structure is c. 0.70 m. The zone of "active" subgrade performance equal to 1.0 m (in the area of direct contact between the subgrade and the structure) is confirmed by field tests carried out on a site consisting of a fragment of actual access road for a storage halls complex [41].…”

Section: Model Of Multiple-layer Pavement Structure-subsoil Systemmentioning

confidence: 99%

“…Modelling the pavement-subgrade system in (2D) state seems highly justified not only due to the simplicity and speed of the calculations, but also due to the nature of the "road" itself, which may be treated as a cross-section cut from infinite space. Application of (2D) state analyses can be found, among others, in [16,17]. In literature, the usage of this continuous model is mostly restricted to scientific research, e.g.…”

Section: Introductionmentioning

confidence: 99%

Model of multiple-layer pavement structure-subsoil system

Kadela¹

2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Progress made in recent years has brought about a high demand for increasingly modern structural and technological solutions. Each structure should in turn be designed and built to show sufficient durability for the intended period of use. The requirement of durability is met if, throughout its intended lifetime, the structure fulfils its roles regarding load-bearing capacity, serviceability limits and stability without excessive, unexpected costs. Due to the above, a need arises to predict the response of an engineering structure to given loads throughout its life. Thus it becomes increasingly common to employ numerical analyses using the finite elements method (FEM), both on the design stage and later, for the purposes of evaluating the state of a specific structure. However, a numerical calculation model may be constructed in different ways. This paper presents the impact of geometry of the model, the choice of a discretization mesh and the choice of a continuous 3D or 2D model corresponding to pavement-subgrade system calculation model. 3-dimensional modelling was carried out in this paper as full modelling of actual engineering problems in 3-dimensional space, and in the form of simplified modelling using axial symmetry. In the model, a traditional multi-layer pavement structure was considered. Criterion values obtained in numerical analyses were compared to values obtained with the use of VEROAD software.Key words: road pavement, mechanical methods, multi-layer structure, calculation model, finite element analysis (FEA).Model of multiple-layer pavement structure-subsoil system M. KADELA * Building Research Institute, 1 Filtrowa St., 00-611 Warsaw, Poland i.e. horizontal or vertical extension, etc. The global trend of transforming issues of reality into problems of mechanics is visible in the approach adopted in Eurocodes. For example, Eurocode 7 introduces a new concept of geotechnical design, which interconnects, among others, the construction law, standards, design and execution instructions, field and laboratory tests, determining loads for the analyzed system and adoption of calculation models. It thus becomes vital to choose the correct procedure which will allow to describe the behavior of the analyzed structure in all predicted phases of its work. Theories of mechanics employed in the evaluation of structure performance involve certain simplifications; they use models which may considerably deviate from the real structure performance, making their practical application difficult. Creation of a full, credible calculation model therefore automatically implies the use of numerical methods in the analyses.It should be noted that the numerical tool in the form of a numerical calculation model may be ambiguous to some extent, as in general a model may be created in different ways [10]. The differences are mostly influenced by:• geometry of the calculation model and the manner of applying boundary conditions, • adoption of constitutive models for materials used for layers of the structures and th...

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“…For example, in non-destructive testing (NDT), the dynamic response of a pavement is measured and then used to assess the mechanical properties of the pavement. This is useful for example for wear prediction and maintenance [2], [3]. Other application areas that make use of the pavement's response to dynamic loads include weigh-in-motion (WIM) systems [4], or, more recently, traffic monitoring systems [5], [6].…”

Section: Introductionmentioning

confidence: 99%

The Pavement as a Waveguide: Modeling, System Identification, and Parameter Estimation

Hostettler

Nordenvaad

Birk

2014

IEEE Trans. Instrum. Meas.

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Abstract-This article presents modeling of wave propagation in pavements from a system identification point of view. First, a model based on the physical structure is derived. Second, experiment design and evaluation is discussed and maximum likelihood estimators for estimating the model parameters are introduced, assuming an error-in-variables setting. Finally, the proposed methods are applied to measurement data from two experiments under varying environmental conditions. It is found that the proposed methods can be used to estimate the dispersion curves of the considered waveguide and the results can be used for further analysis.

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Advances in backcalculating the mechanical properties of flexible pavements

Cited by 93 publications

References 22 publications

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

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

Model of multiple-layer pavement structure-subsoil system

The Pavement as a Waveguide: Modeling, System Identification, and Parameter Estimation

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