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...
محفوظ في:
| المؤلفون الرئيسيون: | , , , , |
|---|---|
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Penerbit Universiti Kebangsaan Malaysia
2022
|
| الوصول للمادة أونلاين: | http://journalarticle.ukm.my/20594/1/18.pdf http://journalarticle.ukm.my/20594/ https://www.ukm.my/jkukm/volume-3405-2022/ |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| الملخص: | 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. |
|---|