Dual Model for International Roughness Index Classification and Prediction
Existing models for predicting the international roughness index (IRI) of a road surface often lack adaptability, struggling to accurately reflect variations in climate, traffic, and pavement distresses—factors critical for effective and sustainable maintenance. This study presents a novel dual-mode...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/1/23 |
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| author | Noelia Molinero-Pérez Laura Montalbán-Domingo Amalia Sanz-Benlloch Tatiana García-Segura |
| author_facet | Noelia Molinero-Pérez Laura Montalbán-Domingo Amalia Sanz-Benlloch Tatiana García-Segura |
| author_sort | Noelia Molinero-Pérez |
| collection | DOAJ |
| description | Existing models for predicting the international roughness index (IRI) of a road surface often lack adaptability, struggling to accurately reflect variations in climate, traffic, and pavement distresses—factors critical for effective and sustainable maintenance. This study presents a novel dual-model approach that integrates pavement condition index (PCI), pavement distress types, climatic, and traffic data to improve IRI prediction. Using data from the Long-Term Pavement Performance database, a dual-model approach was developed: pavements were classified into groups based on key factors, and tailored regression models were subsequently applied within each group. The model exhibits good predictive accuracy, with <i>R</i><sup>2</sup> values of 0.62, 0.72, and 0.82 for the individual groups. Furthermore, the validation results (<i>R</i><sup>2</sup> = 0.89) confirm that the combination of logistic regression and linear regression enhances the precision of IRI value predictions. This approach enhances adaptability and practicality, offering a versatile tool for estimating IRI under diverse conditions. The proposed methodology has the potential to support more effective, data-driven decisions in pavement maintenance, fostering sustainability and cost efficiency. |
| format | Article |
| id | doaj-art-76d7163437f74edba32e1cf5839ffde3 |
| institution | Kabale University |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-76d7163437f74edba32e1cf5839ffde32025-01-24T13:35:26ZengMDPI AGInfrastructures2412-38112025-01-011012310.3390/infrastructures10010023Dual Model for International Roughness Index Classification and PredictionNoelia Molinero-Pérez0Laura Montalbán-Domingo1Amalia Sanz-Benlloch2Tatiana García-Segura3Construction Project Management Research Group, Universitat Politècnica de València, 46022 Valencia, SpainConstruction Project Management Research Group, Universitat Politècnica de València, 46022 Valencia, SpainConstruction Project Management Research Group, Universitat Politècnica de València, 46022 Valencia, SpainConstruction Project Management Research Group, Universitat Politècnica de València, 46022 Valencia, SpainExisting models for predicting the international roughness index (IRI) of a road surface often lack adaptability, struggling to accurately reflect variations in climate, traffic, and pavement distresses—factors critical for effective and sustainable maintenance. This study presents a novel dual-model approach that integrates pavement condition index (PCI), pavement distress types, climatic, and traffic data to improve IRI prediction. Using data from the Long-Term Pavement Performance database, a dual-model approach was developed: pavements were classified into groups based on key factors, and tailored regression models were subsequently applied within each group. The model exhibits good predictive accuracy, with <i>R</i><sup>2</sup> values of 0.62, 0.72, and 0.82 for the individual groups. Furthermore, the validation results (<i>R</i><sup>2</sup> = 0.89) confirm that the combination of logistic regression and linear regression enhances the precision of IRI value predictions. This approach enhances adaptability and practicality, offering a versatile tool for estimating IRI under diverse conditions. The proposed methodology has the potential to support more effective, data-driven decisions in pavement maintenance, fostering sustainability and cost efficiency.https://www.mdpi.com/2412-3811/10/1/23international roughness indexpavement condition indexpavement distressclassification modelprediction model |
| spellingShingle | Noelia Molinero-Pérez Laura Montalbán-Domingo Amalia Sanz-Benlloch Tatiana García-Segura Dual Model for International Roughness Index Classification and Prediction Infrastructures international roughness index pavement condition index pavement distress classification model prediction model |
| title | Dual Model for International Roughness Index Classification and Prediction |
| title_full | Dual Model for International Roughness Index Classification and Prediction |
| title_fullStr | Dual Model for International Roughness Index Classification and Prediction |
| title_full_unstemmed | Dual Model for International Roughness Index Classification and Prediction |
| title_short | Dual Model for International Roughness Index Classification and Prediction |
| title_sort | dual model for international roughness index classification and prediction |
| topic | international roughness index pavement condition index pavement distress classification model prediction model |
| url | https://www.mdpi.com/2412-3811/10/1/23 |
| work_keys_str_mv | AT noeliamolineroperez dualmodelforinternationalroughnessindexclassificationandprediction AT lauramontalbandomingo dualmodelforinternationalroughnessindexclassificationandprediction AT amaliasanzbenlloch dualmodelforinternationalroughnessindexclassificationandprediction AT tatianagarciasegura dualmodelforinternationalroughnessindexclassificationandprediction |