Nondestructive flexible pavement evaluation using ILLI-PAVE based artificial neural network models

O. Pekcan; E. Tutumluer; M. R. Thompson

doi:10.1061/40803(187)227

Nondestructive flexible pavement evaluation using ILLI-PAVE based artificial neural network models

O. Pekcan, E. Tutumluer, M. R. Thompson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Artificial neural networks (ANNs) were used in this paper to develop an improved and more accurate approach for backcalculating pavement layer moduli from Falling Weight Deflectometer (FWD) test data collected in the field. For this purpose, critical pavement responses were computed by the ILLI-PAVE finite element program widely used and proven to be effective for the analysis of flexible pavement systems with the considerations of the nonlinear aggregate base and subgrade soil behavior. The ANN models were then trained to map the nonlinear functional relationships between the FWD deflections, layer properties, and the critical pavement responses. Copyright ASCE 2006.

Original language	English (US)
Title of host publication	GeoCongress 2006
Subtitle of host publication	Geotechnical Engineering in the Information Technology Age
Pages	227
Number of pages	1
DOIs	https://doi.org/10.1061/40803(187)227
State	Published - 2006
Externally published	Yes
Event	GeoCongress 2006 - Atlanta, GA, United States Duration: Feb 26 2006 → Mar 1 2006

Publication series

Name	GeoCongress 2006: Geotechnical Engineering in the Information Technology Age
Volume	2006

Other

Other	GeoCongress 2006
Country/Territory	United States
City	Atlanta, GA
Period	2/26/06 → 3/1/06

ASJC Scopus subject areas

General Engineering

Online availability

10.1061/40803(187)227

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Pekcan, O., Tutumluer, E., & Thompson, M. R. (2006). Nondestructive flexible pavement evaluation using ILLI-PAVE based artificial neural network models. In GeoCongress 2006: Geotechnical Engineering in the Information Technology Age (pp. 227). (GeoCongress 2006: Geotechnical Engineering in the Information Technology Age; Vol. 2006). https://doi.org/10.1061/40803(187)227

Nondestructive flexible pavement evaluation using ILLI-PAVE based artificial neural network models. / Pekcan, O.; Tutumluer, E.; Thompson, M. R.
GeoCongress 2006: Geotechnical Engineering in the Information Technology Age. 2006. p. 227 (GeoCongress 2006: Geotechnical Engineering in the Information Technology Age; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Pekcan, O, Tutumluer, E & Thompson, MR 2006, Nondestructive flexible pavement evaluation using ILLI-PAVE based artificial neural network models. in GeoCongress 2006: Geotechnical Engineering in the Information Technology Age. GeoCongress 2006: Geotechnical Engineering in the Information Technology Age, vol. 2006, pp. 227, GeoCongress 2006, Atlanta, GA, United States, 2/26/06. https://doi.org/10.1061/40803(187)227

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abstract = "Artificial neural networks (ANNs) were used in this paper to develop an improved and more accurate approach for backcalculating pavement layer moduli from Falling Weight Deflectometer (FWD) test data collected in the field. For this purpose, critical pavement responses were computed by the ILLI-PAVE finite element program widely used and proven to be effective for the analysis of flexible pavement systems with the considerations of the nonlinear aggregate base and subgrade soil behavior. The ANN models were then trained to map the nonlinear functional relationships between the FWD deflections, layer properties, and the critical pavement responses. Copyright ASCE 2006.",

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AB - Artificial neural networks (ANNs) were used in this paper to develop an improved and more accurate approach for backcalculating pavement layer moduli from Falling Weight Deflectometer (FWD) test data collected in the field. For this purpose, critical pavement responses were computed by the ILLI-PAVE finite element program widely used and proven to be effective for the analysis of flexible pavement systems with the considerations of the nonlinear aggregate base and subgrade soil behavior. The ANN models were then trained to map the nonlinear functional relationships between the FWD deflections, layer properties, and the critical pavement responses. Copyright ASCE 2006.

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Nondestructive flexible pavement evaluation using ILLI-PAVE based artificial neural network models

Abstract

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Other

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