Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia
Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversibl...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-06-01
|
| Series: | Journal of Road Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2097049825000186 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849734208854949888 |
|---|---|
| author | Ayesh Dushmantha Ruixuan Zhang Yilin Gui Jinjiang Zhong Chaminda Gallage |
| author_facet | Ayesh Dushmantha Ruixuan Zhang Yilin Gui Jinjiang Zhong Chaminda Gallage |
| author_sort | Ayesh Dushmantha |
| collection | DOAJ |
| description | Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversible damage to the pavement structure. The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation, air temperature, and relative humidity. This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data. Consequently, there is an increasing demand for advanced, technology-driven methodologies to predict moisture variations based on climatic inputs. Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. Model performance is evaluated using R2, MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. The best-performing algorithms include KNN (the number of neighbours equals to 15), medium regression tree, medium random forest, coarse SVM, and simple GPR, with medium random forest outperforming the others. The study also identifies the optimal hyperparameter combinations for each algorithm, offering significant advancements in moisture prediction tools for pavement technology. |
| format | Article |
| id | doaj-art-bdceb9c806574b51ac46b5effbeb748a |
| institution | DOAJ |
| issn | 2773-0077 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Road Engineering |
| spelling | doaj-art-bdceb9c806574b51ac46b5effbeb748a2025-08-20T03:07:50ZengKeAi Communications Co., Ltd.Journal of Road Engineering2773-00772025-06-015218420110.1016/j.jreng.2024.12.007Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, AustraliaAyesh Dushmantha0Ruixuan Zhang1Yilin Gui2Jinjiang Zhong3Chaminda Gallage4School of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, QLD 4000, AustraliaSchool of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, QLD 4000, AustraliaSchool of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, QLD 4000, AustraliaLogan City Council, Logan City, QLD 4114, AustraliaSchool of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Corresponding author.Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversible damage to the pavement structure. The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation, air temperature, and relative humidity. This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data. Consequently, there is an increasing demand for advanced, technology-driven methodologies to predict moisture variations based on climatic inputs. Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. Model performance is evaluated using R2, MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. The best-performing algorithms include KNN (the number of neighbours equals to 15), medium regression tree, medium random forest, coarse SVM, and simple GPR, with medium random forest outperforming the others. The study also identifies the optimal hyperparameter combinations for each algorithm, offering significant advancements in moisture prediction tools for pavement technology.http://www.sciencedirect.com/science/article/pii/S2097049825000186Pavement technologyUnbound granular materialsMoisture predictionMachine learningClimatic factors |
| spellingShingle | Ayesh Dushmantha Ruixuan Zhang Yilin Gui Jinjiang Zhong Chaminda Gallage Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia Journal of Road Engineering Pavement technology Unbound granular materials Moisture prediction Machine learning Climatic factors |
| title | Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia |
| title_full | Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia |
| title_fullStr | Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia |
| title_full_unstemmed | Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia |
| title_short | Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia |
| title_sort | deploying machine learning for long term road pavement moisture prediction a case study from queensland australia |
| topic | Pavement technology Unbound granular materials Moisture prediction Machine learning Climatic factors |
| url | http://www.sciencedirect.com/science/article/pii/S2097049825000186 |
| work_keys_str_mv | AT ayeshdushmantha deployingmachinelearningforlongtermroadpavementmoisturepredictionacasestudyfromqueenslandaustralia AT ruixuanzhang deployingmachinelearningforlongtermroadpavementmoisturepredictionacasestudyfromqueenslandaustralia AT yilingui deployingmachinelearningforlongtermroadpavementmoisturepredictionacasestudyfromqueenslandaustralia AT jinjiangzhong deployingmachinelearningforlongtermroadpavementmoisturepredictionacasestudyfromqueenslandaustralia AT chamindagallage deployingmachinelearningforlongtermroadpavementmoisturepredictionacasestudyfromqueenslandaustralia |