Journal ArticleDOI
Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network
Sunil Sharma,Animesh Das +1 more
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TLDR
In this paper, the elastic moduli of asphalt pavement from synthetically derived falling weight deflectometer (FWD) deflections at seven equidistant points were backcalculated.Abstract:
Efforts have been made in this paper to backcalculate the in situ elastic moduli of asphalt pavement from synthetically derived falling weight deflectometer (FWD) deflections at seven equidistant p...read more
Citations
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Journal ArticleDOI
Artificial Intelligence in Civil Engineering
TL;DR: Recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, are summarized.
Journal ArticleDOI
Neural Networks Applications in Pavement Engineering: A Recent Survey
TL;DR: In this paper, the authors provide a state-of-the-art survey of NN applications in pavement engineering over the last three decades, including prediction of pavement condition and performance, management and maintenance strategies, pavement distress forecasting, structural evaluation of pavement systems, image analysis and classification, pavement materials modeling, and other miscellaneous transportation infrastructure applications.
Journal ArticleDOI
Neural Network Modeling of Resilient Modulus Using Routine Subgrade Soil Properties
TL;DR: In this paper, Artificial Neural Network (ANN) models are developed to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application, and a database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulation results for 97 soils from 16 different counties in Oklahoma.
Journal ArticleDOI
Nondestructive testing of asphalt pavements for structural condition evaluation: a state of the art
Amit Goel,Animesh Das +1 more
TL;DR: In this article, the authors present some of the major conventional as well as emerging nondestructive evaluation methods for in situ structural assessment of asphalt pavements, primarily directed towards the estimation of layer moduli and thickness values which are direct indicators of the structural strength of pavement.
Journal ArticleDOI
Backcalculation of pavement layer moduli and Poisson's ratio using data mining
TL;DR: Backcalculation of pavement layer elastic modulus and Poisson's ratio with DM has been carried out for the first time, giving fine results with respect to other DM methods.
References
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Book
Introduction to artificial neural systems
TL;DR: Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.
Book
Pavement analysis and design
TL;DR: In this paper, the authors present the theory of pavement design and review the methods developed by several organizations, such as the American Association of State Highway and Transportation Officials (AASHTO), the Asphalt Institute (AI), and the Portland Cement Association (PCA).
Journal ArticleDOI
Advances in backcalculating the mechanical properties of flexible pavements
TL;DR: In current practice, the evaluation of the performance of existing road pavements has become a priority issue for many highway engineers and innovations and advances in backcalculating flexible pavements are considered.
Book ChapterDOI
Multilayer elastic program for backcalculating layer moduli in pavement evaluation
TL;DR: In this paper, an accurate and efficient solution to the multilayer problem was developed for use on a personal computer that included routines for backcalculating pavement layer moduli from measured surface deflections.
Journal ArticleDOI
Neural Network Modeling of Resilient Modulus Using Routine Subgrade Soil Properties
TL;DR: In this paper, Artificial Neural Network (ANN) models are developed to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application, and a database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulation results for 97 soils from 16 different counties in Oklahoma.
Related Papers (5)
Advances in backcalculating the mechanical properties of flexible pavements
Backcalculation of flexible pavement moduli from dynamic deflection basins using artificial neural networks
Roger W. Meier,Glenn J. Rix +1 more