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
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.
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“One of the significant advantages of the DT algorithm is its simplicity of comprehension and interpretation [66-68].”
From Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach (2022) · cites this paper as [67]
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Sorted by citations
Deep machine learning approach to develop a new asphalt pavement condition index
Majidifard, H., Adu-Gyamfi, Y. O., & Buttlar, W. G. (2020) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2020.118513 · Cited by 297
Enhanced Credit Card Fraud Detection Model Using Machine Learning
Alfaiz, N. S., & Fati, S. M. (2022) · Electronics · doi:10.3390/electronics11040662 · Cited by 240
Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems
Piryonesi, S. M., & El-Diraby, T. E. (2020) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.0000175 · Cited by 208
Self-citationPiryonesi, S. M., & El-Diraby, T. E. (2021) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000602 · Cited by 143
Self-citationXGBoost-SHAP framework for asphalt pavement condition evaluation
Gupta, A., Gowda, S., Tiwari, A., & Gupta, A. K. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.136182 · Cited by 92
Metadata
- Authors
- Piryonesi, S. M., & El-Diraby, T. E.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Piryonesi, S. Madeh
- Venue
- Journal of Infrastructure Systems
- Published
- 2020-03
- Volume
- 26
- Issue
- 1
- ISSN
- 1076-0342 (ISSN-L)
- 1943-555X
- 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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