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Cited by 295Infrastructure Maintenance and MonitoringAsphalt Pavement Performance EvaluationGeotechnical and construction materials studies

The DOI matches its Crossref record; 41 of the 42 references it cites were found in a registry or an index.

Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index

Piryonesi, S. Madeh, El-Diraby, Tamer E.·2020·Journal of Infrastructure Systems
DOI 10.1061/(asce)is.1943-555x.0000512
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Abstract

Understanding the deterioration of roads is an important part of road asset management. In this study, the long-term pavement performance (LTPP) data and machine learning algorithms were used to predict the deterioration in the pavement condition index (PCI) over 2, 3, 5, and 6 years. In selecting the attributes for conducting the analysis, we targeted ones that are freely available. This approach can help smaller municipalities, which could be short on money or required expertise. For larger ones and transportation agencies, this can save the increasingly significant costs for collecting field data and any associated safety or traffic implications. In addition, we used this category of attributes to better examine the role of data analytics in asset management. Without considering a causal model, can trends in data help assess deterioration in the PCI? Several models using combinations of 15 attributes were learned and tested. The algorithms used in this study were two types of decision trees and their boosted models based on gradient boosted trees. The accuracy of the ensemble of boosted classifiers was considerably higher than their base learners, with some reaching over 80% in predicting unseen data. We also found that dividing data into different climatic zones can change the relative importance of attributes and the overall accuracy of the models. Increasing the prediction span reduces accuracy, while reducing the number of prediction classes (levels of deterioration) increases the accuracy. In addition to automating the calculation and prediction of PCI, this study presented informative or important attributes for prediction. Such analyses could help municipalities and departments of transportations with forming a more effective policy for data collection and management.

Overview

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Crossref · 10.1061/(asce)is.1943-555x.0000512

Cites

41 of 41 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 3

  1. Beckwith, 2013, Asset management plan
  2. Chong G. J., 1982, SP-024, Manual for Condition Rating of Flexible Pavements
  3. Provost F., 2013, Data science for business

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Metadata

Authors
Piryonesi, S. M., & El-Diraby, T. E.
Affiliations
University of Toronto (CA)
Corresponding author
Piryonesi, S. Madeh
ORCID
Venue
Journal of Infrastructure Systems
Publisher
American Society of Civil Engineers (ASCE)
Published
2020-03
Volume
26
Issue
1
ISSN
  • 1076-0342 (ISSN-L)
  • 1943-555X
DOI
10.1061/(asce)is.1943-555x.0000512
OpenAlex
W2996338922
Cited by
295
References
42
Citations by year
2026: 28 · 2025: 36 · 2024: 59 · 2023: 65 · 2022: 54 · 2021: 42 · 2020: 10
FWCI
20.01
Type
journal article
Language
en
Open access
closed
Locations
Journal of Infrastructure Systems (publishedVersion)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Engineering › Civil and Structural Engineering › Asphalt Pavement Performance Evaluation
  • Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical and construction materials studies
Keywords
pavement condition index, pavement deterioration prediction, road asset management, decision tree, gradient boosting decision tree, climate zones, attribute importance, municipal road maintenance

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