Cover Image

Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network

Pavement modulus is believed as one of the important features to characterize the pavement condition, specifically the pavement stiffness. The value of pavement modulus may be calculated using the existing Witczak mathematical dynamic pavement modulus prediction formulae. However, the equation d...

Full description

Saved in:
Bibliographic Details
Main Authors: Abdul Ghani, Nur Aina Farahana, Ismail, Norfarah Nadia, Wan Azahar, Wan Nur Aifa, Abd Rahman, Faridah, Wong Azman, Amelia
Format: Article
Language:English
Published: UKM Press 2022
Subjects:
TE Highway engineering. Roads and pavements
TE210 Construction details. Including foundation, maintenance, equipment
TE250 Pavement and paved roads
Online Access:http://irep.iium.edu.my/99581/7/99581_Affirmation%20of%20elastic%20modulus%20derived.pdf
http://irep.iium.edu.my/99581/
https://www.ukm.my/jkukm/wp-content/uploads/2022/3405/18.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.99581
record_format dspace
spelling my.iium.irep.995812022-10-13T02:24:56Z http://irep.iium.edu.my/99581/ Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network Abdul Ghani, Nur Aina Farahana Ismail, Norfarah Nadia Wan Azahar, Wan Nur Aifa Abd Rahman, Faridah Wong Azman, Amelia TE Highway engineering. Roads and pavements TE210 Construction details. Including foundation, maintenance, equipment TE250 Pavement and paved roads Pavement modulus is believed as one of the important features to characterize the pavement condition, specifically the pavement stiffness. The value of pavement modulus may be calculated using the existing Witczak mathematical dynamic pavement modulus prediction formulae. However, the equation developed by Witczak is heavily impacted by temperature while underestimating the impact of other mixing factors thus, only offering an adequate approximation for the circumstances for which they were designed. In this study, the Spectral Analysis of Surface Wave (SASW) test data was used to develop an Artificial Neural Network (ANN) that accurately backcalculates pavement profiles in real-time. The pavement modulus calculated from the equation was validated by using ANN developed in Matlab software to avoid any mistakes during calculation based on the equation. Three parameters, shear wave velocity, depth and thickness from SASW test data were used as inputs and elastic modulus calculated using Witczak pavement modulus equation was used as an output to train the models developed in ANN. Five segments of pavement are presented in this paper where almost compromise that the greater the depth, the lesser the shear wave velocity as well as pavement modulus. Nine neural network models were developed in this study. The network architecture of 4-80-4 is the most optimized network with the highest correlation coefficient of 0.9992, 0.9994, 1.0, 0.9996 for validation, testing, training and all respectively. The created ANN models’ final outputs were reasonable and relatively similar to the real output. UKM Press 2022-09-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/99581/7/99581_Affirmation%20of%20elastic%20modulus%20derived.pdf Abdul Ghani, Nur Aina Farahana and Ismail, Norfarah Nadia and Wan Azahar, Wan Nur Aifa and Abd Rahman, Faridah and Wong Azman, Amelia (2022) Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network. Jurnal Kejuruteraan, 34 (5). pp. 905-913. ISSN 0128-0198 E-ISSN 2289-7526 https://www.ukm.my/jkukm/wp-content/uploads/2022/3405/18.pdf 10.17576/jkukm-2022-34(5)-18
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TE Highway engineering. Roads and pavements
TE210 Construction details. Including foundation, maintenance, equipment
TE250 Pavement and paved roads
spellingShingle TE Highway engineering. Roads and pavements
TE210 Construction details. Including foundation, maintenance, equipment
TE250 Pavement and paved roads
Abdul Ghani, Nur Aina Farahana
Ismail, Norfarah Nadia
Wan Azahar, Wan Nur Aifa
Abd Rahman, Faridah
Wong Azman, Amelia
Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network
description Pavement modulus is believed as one of the important features to characterize the pavement condition, specifically the pavement stiffness. The value of pavement modulus may be calculated using the existing Witczak mathematical dynamic pavement modulus prediction formulae. However, the equation developed by Witczak is heavily impacted by temperature while underestimating the impact of other mixing factors thus, only offering an adequate approximation for the circumstances for which they were designed. In this study, the Spectral Analysis of Surface Wave (SASW) test data was used to develop an Artificial Neural Network (ANN) that accurately backcalculates pavement profiles in real-time. The pavement modulus calculated from the equation was validated by using ANN developed in Matlab software to avoid any mistakes during calculation based on the equation. Three parameters, shear wave velocity, depth and thickness from SASW test data were used as inputs and elastic modulus calculated using Witczak pavement modulus equation was used as an output to train the models developed in ANN. Five segments of pavement are presented in this paper where almost compromise that the greater the depth, the lesser the shear wave velocity as well as pavement modulus. Nine neural network models were developed in this study. The network architecture of 4-80-4 is the most optimized network with the highest correlation coefficient of 0.9992, 0.9994, 1.0, 0.9996 for validation, testing, training and all respectively. The created ANN models’ final outputs were reasonable and relatively similar to the real output.
format Article
author Abdul Ghani, Nur Aina Farahana
Ismail, Norfarah Nadia
Wan Azahar, Wan Nur Aifa
Abd Rahman, Faridah
Wong Azman, Amelia
author_facet Abdul Ghani, Nur Aina Farahana
Ismail, Norfarah Nadia
Wan Azahar, Wan Nur Aifa
Abd Rahman, Faridah
Wong Azman, Amelia
author_sort Abdul Ghani, Nur Aina Farahana
title Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network
title_short Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network
title_full Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network
title_fullStr Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network
title_full_unstemmed Affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network
title_sort affirmation of elastic modulus derived from spectral analysis of surface waves method using artificial neural network
publisher UKM Press
publishDate 2022
url http://irep.iium.edu.my/99581/7/99581_Affirmation%20of%20elastic%20modulus%20derived.pdf
http://irep.iium.edu.my/99581/
https://www.ukm.my/jkukm/wp-content/uploads/2022/3405/18.pdf
_version_ 1748180265828614144
score 13.244199

Similar Items