Application of Machine Learning for Temperature Prediction in a Test Road in Alberta

Nojumi, Mohamad Molavi; Huang, Yunyan; Hashemian, Leila; Bayat, Alireza

doi:10.1007/s42947-021-00023-3

Cited by 24 publications

(19 citation statements)

References 37 publications

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“…The proposed hybrid model showed an excellent prediction ability compared to others. For instance, the RF showed a 95% better accuracy than Liu et al [37], GPR showed a 99% better accuracy than Nojumi et al [38], and 96% better accuracy than Asefzadeh et al [34], and 81.8% better accuracy than GBELM. These percentages proved that the proposed hybrid algorithm model has a promising potential, high reliability and high accuracy for predicting the pavement temperature.…”

Section: Comparison With Previous Studymentioning

confidence: 90%

Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Milad¹,

Adwan

Majeed³

et al. 2021

IEEE Access

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Section: Comparison With Previous Studymentioning

confidence: 90%

Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Milad¹,

Adwan

Majeed³

et al. 2021

IEEE Access

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“…More details about the test road and its construction can be found in previous work. 4,6,9 Thermistors and CS650 time domain reflectometers (TDRs, Campbell Scientific) were installed in the HMA, GBC, and subgrade layer to collect asphalt and soil temperature at various depths. Figure 1 shows the cross-section of the test road, along with the location of thermistors and TDRs, at depths of 0.02, 0.09, 0.17, 0.25, 0.50, 0.70, 0.80, 1.80, and 2.70 m. Identical sensors were installed at three locations for each depth to provide redundancy in the case of damage during installation or malfunctioning during operation.…”

Section: Data Collectionmentioning

confidence: 99%

“…4 A time-related parameter, DOY, has been shown to affect the temperature of the HMA layer. 9 Thus, DOY is also included as a parameter. DOY ranges from 1 to 365, and with DOY ¼ 1 corresponding to January 1.…”

Section: Data Collectionmentioning

confidence: 99%

Multi-depth temperature prediction using machine learning for pavement sections

Huang,

Nojumi,

Ansari

et al. 2024

J. Appl. Rem. Sens.

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The temperature of hot mix asphalt (HMA), base, and subgrade layers plays a significant role in pavement performance, because temperature influences the strength of the materials. Therefore, a model to predict temperature throughout the entire pavement structure is desirable. However, most existing models only focus on predicting the temperature of the road surface or the HMA layer, and these models usually need some information related to boundary conditions or material properties that is difficult to obtain. This research aims to demonstrate that machine learning (ML) model can be a powerful generalized approach to predict the temperature within a pavement structure at multiple depths. Data collected from sensors (thermistors and time domain reflectometers) installed in the Integrated Road Research Facility test road in Edmonton, Alberta, Canada, were used to train ML models. Sensitivity analysis was performed to analyze the influence of several input parameters on asphalt and soil temperature. ML models with three input parametersaverage daily air temperature, day of the year, and depth-resulted in better performance compared to ML models based on other combinations of parameters. Three ML models were established to predict the average daily temperature, minimum daily temperature, and maximum daily temperature of the pavement structure. To validate model performance, the three ML models were compared with four existing models, and of these the ML models showed the highest accuracy with the coefficient of determination values above than 0.97 and root mean square error values below than 2.21. These results demonstrate that ML models can be used to give accurate predictions of road temperature at multiple depths with only one environmental predictive parameter, average daily air temperature.

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“…When the meso-structure of material is too complex, it is difficult to determine the above relationship. Neural network is a powerful tool to find the relationship between various variables under complex conditions and may provide an alternative way to predict the effective thermal conductivity of composite construction materials based on their specific heterogeneous structures [30]. Lee et al [31] and Sargam et al [32] developed neural network models to predict the effective thermal conductivity of concrete.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

Zhu

Peng²,

Chen

et al. 2023

Materials

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Thermal conductivity is a fundamental material parameter involved in various infrastructure design guides around the world. This paper developed an innovative neural network (NN) aided homogenization approach for predicting the effective thermal conductivity of various composite construction materials. The 2-D meso-structures of dense graded asphalt mixture, porous asphalt mixture, and cement concrete were generated and divided into 2n × 2n square elements with specific thermal conductivity values. A two-layer feed-forward neural network with sigmoid hidden neurons and linear output neurons was built to predict the effective thermal conductivity of the 2 × 2 block. The Levenberg-Marquardt backpropagation algorithm was used to train the network. By repeatedly using the neural network, the effective thermal conductivities of 2-D meso-structures were calculated. The accuracy of the above NN aided homogenization approach was validated with experiment, and various factors affecting the effective thermal conductivity were analyzed. The analysis results show that the accuracy of the NN aided approach is acceptable with relative errors of 1.92~4.34% for the dense graded asphalt mixture, 1.10~6.85% for the porous asphalt mixture, and 1.13~3.14% for the cement concrete. The relative errors for all the materials are lower than 5% when the heterogeneous structures are divided into 512 × 512 elements. Ignoring the actual material meso-structures may lead to significant errors (134.01%) in predicting the effective thermal conductivity of materials with high heterogeneity such as porous asphalt mixture. While proper simplification is acceptable for dense construction composite materials. The effective thermal conductivity of composite cement-asphalt mixtures increases with higher saturation of grouted material. However, the improvement effect of the high-conductive cement paste on the composite cement-asphalt mixtures could be significantly reduced when the cement paste concentrates at the bottom of the mixture. Cracked aggregates and segregation of material components tend to decrease the effective thermal conductivity of construction materials. The NN aided homogenization approach presented in this paper is useful for selecting the effective thermal conductivity of construction materials.

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Application of Machine Learning for Temperature Prediction in a Test Road in Alberta

Cited by 24 publications

References 37 publications

Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Multi-depth temperature prediction using machine learning for pavement sections

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials

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