Sensitivity analysis of the mainline travel lane pavement service life when utilizing part-time shoulder use with full depth paved shoulders

Coffey, Sean; Park, Sehhoon; McCarthy, Leslie Myers

doi:10.1016/j.ijprt.2017.09.003

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Geographic and environmental factors may also be considered. In the Long-term Pavement Performance (LTPP) program, the annual total amount of precipitation and the freezing index [14][15][16] are used to divide the United States into 4 climatic regions, including dry no Freeze, dry freeze, wet no Freeze and wet freeze. However, there are still several challenges for climatic regionalization for pavement infrastructure.…”

Section: Climatic Regionsmentioning

confidence: 99%

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Dong¹,

Chen

Dong

et al. 2021

Preprint

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This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snow fall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

show abstract

Section: Climatic Regionsmentioning

confidence: 99%

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Dong¹,

Chen

Dong

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Geographic and environmental factors may also be considered. In the Long-term Pavement Performance (LTPP) program, the annual total amount of precipitation and the freezing index [14][15][16] are used to divide the United States into four climatic regions, including dry no Freeze, dry freeze, wet no Freeze and wet freeze. However, there are still several challenges for climatic regionalization for pavement infrastructure.…”

Section: Development Of Climatic Regionsmentioning

confidence: 99%

Classification of pavement climatic regions through unsupervised and supervised machine learnings

Dong

Chen

Dong

et al. 2021

J Infrastruct Preserv Resil

View full text Add to dashboard Cite

This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snowfall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Around one third of the 800 weather stations record variation of freeze and precipitation classifications and a few of them show significant change of classifications over time based on the results of logistic regression analyses. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

show abstract

Partial-depth paved shoulder effect on part-time shoulder use pavement bearing capacity

Coffey¹,

Park

McCarthy

2019

Int. J. Pavement Res. Technol.

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Sensitivity analysis of the mainline travel lane pavement service life when utilizing part-time shoulder use with full depth paved shoulders

Cited by 4 publications

References 2 publications

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Classification of pavement climatic regions through unsupervised and supervised machine learnings

Partial-depth paved shoulder effect on part-time shoulder use pavement bearing capacity

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