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Machine Learning for Pavement Performance Modelling in Warm Climate Regions

  • Research Article-Civil Engineering
  • Published:
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Abstract

Accurate pavement performance modelling is an essential requirement for cost-effective pavement design and enhances pavement management decision making. Due to the complexity of the pavement structure and pavement response, dominant failure mechanisms may vary depending on the climate region: cold or warm. This study investigated the significance of pavement design factors on pavement performance in warm regions and compared them to set of factors previously identified for cold regions. An artificial neural network (ANN) supported by a forward sequential feature selection algorithm was employed to identify the most significant design factors prevailing in warm climate regions using data extracted from the Long-Term Pavement Performance database. In addition, five machine learning techniques were utilized to model the pavement performance in warm regions, namely: regression tree, support vector machine, ensembles, Gaussian process regression, and ANN. Moreover, conventional regression modelling was used for comparison assessment. The analysis revealed seven design factors that are significantly impacting asphalt pavement performance in warm regions: initial roughness, relative humidity, average wind velocity, average albedo, average emissivity, traffic volume, and pavement structural capacity. The results indicate that pavement performance in warm climate regions is dominated by different environmental factors than those found for cold climate regions. The ANN modelling technique produced the most accurate asphalt pavement performance models.

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Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Waleed Zeiada, Saleh Abu Dabous, Khaled Hamad & Rami Al-Ruzouq

  2. Sustainable Civil Infrastructure Research Group, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Waleed Zeiada, Saleh Abu Dabous, Khaled Hamad, Rami Al-Ruzouq & Mohamad A. Khalil

  3. Public Works Department, College of Engineering, Mansoura University, Mansoura, Egypt

    Waleed Zeiada

Authors
  1. Waleed Zeiada
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  2. Saleh Abu Dabous
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  4. Rami Al-Ruzouq
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  5. Mohamad A. Khalil
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Corresponding author

Correspondence to Waleed Zeiada.

Appendix

Appendix

Model

Source

Remarks

ANN-FSFS MATLAB code

https://1drv.ms/t/s!AsNDdOuA1FmykDER609orxyac13P

–

ANN-30-Neuron (Selected Features)

https://1drv.ms/u/s!AsNDdOuA1FmykG34mQTUJcRBgeUL

–

Complex Tree Model MATLAB code

https://1drv.ms/f/s!AsNDdOuA1FmykGJexCIXDrefSZyG

To make predictions on a new predictor column matrix, X:

yfit = Complex_Tree.predictFcn(X)

SVM Medium Gaussian

https://1drv.ms/f/s!AsNDdOuA1FmykGVL0XeFPpOqsx-n

yfit = SVM_Medium_Gaussian.predictFcn(X)

Ensemble Boosted Tree

https://1drv.ms/f/s!AsNDdOuA1FmykGPDzC9SRQuyUxnU

yfit = Ensemble_Boosted_Trees.predictFcn(X)

Gaussian Process Regression (Exponential GPR)

https://1drv.ms/f/s!AsNDdOuA1FmykGQuwhOIawcRq3Ab

yfit = Exponential_GPR.predictFcn(X)

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Zeiada, W., Dabous, S.A., Hamad, K. et al. Machine Learning for Pavement Performance Modelling in Warm Climate Regions. Arab J Sci Eng 45, 4091–4109 (2020). https://doi.org/10.1007/s13369-020-04398-6

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  • DOI: https://doi.org/10.1007/s13369-020-04398-6

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