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Different Temperature Prediction Models for
Asphalt Concrete Pavement
Presenter: Saroj Pathak
PhD student,
Department of Civil and Environmental Engineering,
University of Hawaii at Manoa
spathak@hawaii.edu
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Introduction
➢ Mechanical properties of the pavement can vary
significantly with the magnitude of temperature
changes.
➢ Temperature variation of the asphalt pavement,
over a section, is mainly affected by the current and
past histories of the surrounding weather
conditions (time series-based problem) and
material characteristics of the pavement.
➢ In this project, different Artificial
Neural Network (ANN) based models
are developed to predict the
temperature of the asphalt pavement
section by considering climatic
conditions as the input.
➢ The models do not incorporate the
effects of material properties of the
pavement on the temperature variation.
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Objective
➢ To compare the performances and usefulness of the 4 different ANN based models
(Simple feedforward, 1D convolutional, SimpleRNN, and LSTM) to predict the
temperature of the asphalt concrete section at different depths.
➢ To outline possible future research to improve the model performances.
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Data Collection and Processing
➢ Temperature data collected for 8
different depth across a pavement
section, throughout the year (from Aug
2015-Aug 2016).
➢ Collected data merged with the weather
data (surface temperature, precipitation,
wind speed, cloud cover, humidity).
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Model Implementation
For feedforward (simple deep learning) model
• import keras
• from keras.models import Sequential
• from keras.layers import Dense
• model =Sequential( )
• model.add(Dense(20,input_dim=5,activation = 'relu'))
• model.add(Dense(50,activation = 'relu'))
• model.add(Dense(80,activation = 'relu'))
• model.add(Dense(50,activation = 'relu'))
• model.add(Dense(20,activation = 'relu'))
•
model.add(Dense(y.shape[1],activation = 'linear'))
• model.compile(loss='mean_squared_error',optimizer='Adam',metrics=['mea
n_squared_error'])
• model.summary()
For 1D Convolutional model
• model = Sequential()
• model.add(Conv1D(filters=64, kernel_size=10, activation='relu', input_shape
=(ntimesteps,num_features)))
• model.add(MaxPooling1D(pool_size=2))
• model.add(Conv1D(filters=64, kernel_size=10, activation='relu'))
• #model.add(MaxPooling1D(pool_size=2))
• model.add(Flatten())
• model.add(Dense(50, activation='relu'))
• model.add(Dense(8))
• model.compile(optimizer='adam', loss='mse')
• model.summary()
For SimpleRNN model
• import keras
• from keras.models import Sequential
• from tensorflow.keras.layers import SimpleRNN, Dropout, Dense
•
model = Sequential()
•
model.add(SimpleRNN(units=32, input_shape=(ntimesteps,num_fea
tures),stateful=False, activation='relu'))
•
model.add(Dense(8))
• model.compile(loss='mean_squared_error', optimizer='adam')
• model.summary()
For LSTM model
• import keras
• from keras.models import Sequential
• from tensorflow.keras.layers import LSTM, Dense, TimeDistributed
•
model = Sequential()
•
model.add(LSTM(units=16,input_shape=(ntimesteps,num_features),
return_sequences=True))
• model.add(LSTM(units=16,activation='relu'))
• model.add(Dense(8))
• model.compile(loss='mean_squared_error', optimizer='adam')
• model.summary()
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Model Performances
For Feedforward Model:
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Model Performances
For 1D Convolutional Model:
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Model Performances
For SimpleRNN Model:
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Model Performances
For LSTM Model:
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Result, Conclusion and Future Research
Result
➢ As expected, time series-based models outperformed the simple feedforward model (reason: the simple
feedforward model was only trained for 1 time step data).
➢ Within the time series-based models, Simple RNN and LSTM model were relatively more accurate compared
to 1D Convolution model although all of them were trained using same time steps of the data.
➢ LSTM model was slightly more accurate than simple RNN model.
Conclusion
➢ The LSTM or Simple RNN model can be used to predict pavement temperature profile with very good
accuracy.
Future Research
➢ Incorporating pavement thermal and material properties in the model training can enhance model
performances and usefulness.
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References
➢ Arangi, S.R.; Jain, R.K. Review Paper on Pavement Temperature Prediction Model for Indian Climatic Condition. Int. J. Innov.
Res. Adv. Eng. 2015, 2, 1–9.
➢ Matic, B.; Matic, D.; Cosic, D.; Sremac, S.; Tepic, G.; Ranitovic, P. A Model for the Pavement Temperature Prediction at Specified
Depth. Metalurgija 2013, 52, 505–508.
➢ Van Dam, T.J.; Harvey, J.T.; Muench, S.T.; Smith, K.D.; Snyder, M.B.; Al-Qadi, I.L.; Ozer, H.; Meijer, J.; Ram, P.V.; Roesler, J.R.; et al.
Towards Sustainable Pavement Systems; Applied Pavement Technology Inc.: Urbana, IL, USA, 2015.
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Questions ???
Thank You!