| dc.description |
At the very outset I like to express my deep gratitude to Almighty Allah for making me
able to complete this research work overcoming all the impediments. His blessings paved
my path to bring this thesis into its authenticity.
I like to express my gratitude to my supervisor Associate Professor Lt Col Mohammed
Russedul Islam, PhD for his continuous and relentless supervision, guidance, advice,
encouragement, and useful critiques of the research work. I like to thank my supervisor
for his advice and assistance in keeping me updated regarding the recent instrument
needed for the research. I acknowledge the dedicated focus and valuable time that my
supervisor spared for my research work. I like to express my gratitude and thanks for my
co-supervisor, Assistant Professor Dr Tanvir Mustafy for his valuable advice and
assistance in developing software for the research.
I like to extend my thanks and regards for Captain Md. Farhan Talib Rizbee, a graduate of
MIST and Assistant Engineer Md Abdul Jabbar of BUP who helped me exploring data
base for my research work. |
en_US |
| dc.description.abstract |
Finding the layer thicknesses and pavement moduli are essential to evaluate construction
quality and pavement life. Destructive test like core sampling provides limited
information about pavement characteristics. Nowadays, Falling Weight Deflectometer
(FWD), a nondestructive test, is famous for determining pavement health and predicting
layer moduli using back-calculated software. However, the accuracy of that backcalculated software is not always acceptable. The accuracy depends on the exactness of
layer moduli's seed values and the layer thicknesses values. This research aims to
determine the relationship between FWD deflection basin parameters (DBPs) and asphalt
pavement layer properties from a database and to predict the layer properties from that
relationship. Machine learning models are developed to find the relationship. Due to the
scarcity of field FWD data of Roads and Highways Department (RHD) this study uses
layered elastic system-based software, General Analysis of Multilayered Elastic System
(GAMES), to simulate FWD test on flexible pavement and generate synthetic data base.
Validation of the GAMES software’s capability to simulate FWD test is conducted
beforehand. For developing models, a total of two thousand FWD tests were conducted
using GAMES software to generate deflection data and DBPs with the randomly selected
layer properties of the national highways of Bangladesh. Random selection process is
applied within the data range to nullify the issue of biasness. Prediction models for
pavement layer property are developed embracing Machine Learning (ML) technique like
Support Vector Regression (SVR), Random Forest (RF) and multilinear regression using
the FWD data base. Thicknesses and moduli were predicted with reasonable accuracy by
all three methods. RF, SVR and multilinear regression models establish good relationship
between FWD deflection data, DBPs and layer properties. The accuracy co-efficient
Mean Absolute Error (MAE), Root Mean Square (RMSE), and Goodness of fit (R2) show
that the proposed formulation can predict the thicknesses and moduli with reasonable
accuracy. The thickness of the HMA layer is predicted well with R2 values of 0.98, 0.65,
and 0.91 with SVR, RF, and multilinear regression models, respectively. Additionally, the
modulus of the HMA layer is predicted with R2 values of 0.95, 0.87, and 0.87 with RF,
SVR, and multilinear regression models, respectively. SVR method predicts layer
iii
parameter with lowest MAE and RMSE values. The results indicate that SVR
classification produce more accurate results than Random Forest and linear regression.
Developed prediction models utilizing FWD deflection data may be applied to predict
layer thicknesses and moduli of pavement layers, minimizing the need for destructive
tests in a busy roadway and overcoming the dependencies of the back-calculated
software. |
en_US |
