Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data
Road networks play a significant role in each country’s economy, especially in countries such as Afghanistan, which is strategically located in the international transit path from Europe to East Asia. In such a country, pavement performance models are fundamental for the pavement maintenance plannin...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/9/1/9 |
| _version_ | 1797343418577321984 |
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| author | Samiulhaq Wasiq Amir Golroo |
| author_facet | Samiulhaq Wasiq Amir Golroo |
| author_sort | Samiulhaq Wasiq |
| collection | DOAJ |
| description | Road networks play a significant role in each country’s economy, especially in countries such as Afghanistan, which is strategically located in the international transit path from Europe to East Asia. In such a country, pavement performance models are fundamental for the pavement maintenance planning that provides high-quality infrastructure for transporting goods and travelers. However, due to the lack of a budget for pavement monitoring and maintenance in Afghanistan, transportation networks and pavement condition data have not been widely acquired for the development of a pavement performance model. The main aim of this study is to use a machine learning technique to, for the first time, develop a pavement performance model for Afghanistan that uses simple, cost-effective, and fairly accurate data—collected via smartphones—and that is based on a case study of over 550 km of Afghanistan’s highways. First, the current condition of Afghanistan’s road network is investigated using a smartphone. Then, collected data are prepared and analyzed so as to estimate the pavement condition index (PCI). Finally, a pavement performance model for PCI is developed using pavement age with an adequate coefficient of determination of 0.70 and successfully validated. It is concluded that the proposed approach is efficient and effective when developing a performance model in other developing countries encountering such data and budget limitations. |
| first_indexed | 2024-03-08T10:47:17Z |
| format | Article |
| id | doaj.art-7ddbdf1b97b64a0997907da94f59ba03 |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-08T10:47:17Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-7ddbdf1b97b64a0997907da94f59ba032024-01-26T17:03:54ZengMDPI AGInfrastructures2412-38112024-01-0191910.3390/infrastructures9010009Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited DataSamiulhaq Wasiq0Amir Golroo1Department of Civil and Environmental Engineering, Amirkabir University of Technology, 350, Hafez Ave, Valiasr Square, Tehran 1591634311, IranDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, 350, Hafez Ave, Valiasr Square, Tehran 1591634311, IranRoad networks play a significant role in each country’s economy, especially in countries such as Afghanistan, which is strategically located in the international transit path from Europe to East Asia. In such a country, pavement performance models are fundamental for the pavement maintenance planning that provides high-quality infrastructure for transporting goods and travelers. However, due to the lack of a budget for pavement monitoring and maintenance in Afghanistan, transportation networks and pavement condition data have not been widely acquired for the development of a pavement performance model. The main aim of this study is to use a machine learning technique to, for the first time, develop a pavement performance model for Afghanistan that uses simple, cost-effective, and fairly accurate data—collected via smartphones—and that is based on a case study of over 550 km of Afghanistan’s highways. First, the current condition of Afghanistan’s road network is investigated using a smartphone. Then, collected data are prepared and analyzed so as to estimate the pavement condition index (PCI). Finally, a pavement performance model for PCI is developed using pavement age with an adequate coefficient of determination of 0.70 and successfully validated. It is concluded that the proposed approach is efficient and effective when developing a performance model in other developing countries encountering such data and budget limitations.https://www.mdpi.com/2412-3811/9/1/9pavement performance modelsmachine learningregression modellingsmartphones |
| spellingShingle | Samiulhaq Wasiq Amir Golroo Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data Infrastructures pavement performance models machine learning regression modelling smartphones |
| title | Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data |
| title_full | Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data |
| title_fullStr | Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data |
| title_full_unstemmed | Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data |
| title_short | Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data |
| title_sort | smartphone based cost effective pavement performance model development using a machine learning technique with limited data |
| topic | pavement performance models machine learning regression modelling smartphones |
| url | https://www.mdpi.com/2412-3811/9/1/9 |
| work_keys_str_mv | AT samiulhaqwasiq smartphonebasedcosteffectivepavementperformancemodeldevelopmentusingamachinelearningtechniquewithlimiteddata AT amirgolroo smartphonebasedcosteffectivepavementperformancemodeldevelopmentusingamachinelearningtechniquewithlimiteddata |