Computer Software

Flexible Pavement Condition Evaluation Using Deflection Basin Parameters and Dynamic Finite Element Analysis Implemented by Artificial Neural Networks

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This paper presents a methodology based on deflection basin parameters and artificial neural networks (ANNs) for processing dynamic falling weight deflectometer (FWD) measurements to estimate layer moduli and condition. Two-dimensional, dynamic, finite element analysis using the ABAQUS program was employed to develop the deflection information for this study. Unlike the majority of the existing backcalculation programs that iteratively adjust all the layer moduli to match the measured deflections, the proposed method first determines the subgrade modulus by means of two deflection basin parameters, Base Damage Index and Shape Factor F2, and then applies the estimated subgrade modulus and other parameters as input variables to a trained ANN to estimate the upper layers’ moduli. Procedures in predicting layer moduli for both two- and three-layer pavement systems are presented. Field FWD measurements were analyzed both by this method and by the MODULUS program, the results of which assess the capability of the proposed method. Effects of discontinuities in the asphalt layer on the resulting FWD deflections were also studied using the finite element method. It was discovered that distresses in the asphalt layer may be detected using two deflection basin parameters, Shape Factor F2 and AREA.

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Ketersediaan

Informasi Detail

Judul Seri

Nondestructive Testing Of Pavements And Backcalculation Of Moduli Third Volume. Astm Stp 1375

No. Panggil

625.7(063) Kim f

Penerbit

: ASTM., 2000

Deskripsi Fisik

pp. 514-530

Bahasa

Indonesia

ISBN/ISSN

-

Klasifikasi

625.7(063)

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ARTIFICIAL NEURAL NETWORKS

Info Detail Spesifik

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Pernyataan Tanggungjawab

-

Versi lain/terkait

Lampiran Berkas

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