Pub Date : 2023-10-16DOI: 10.1007/s42947-023-00377-w
M. Sajeevan, D. N. Subramaniam, R. Rinduja, J. Pratheeba
{"title":"Investigation of Compaction on Compressive Strength and Porosity of Pervious Concrete","authors":"M. Sajeevan, D. N. Subramaniam, R. Rinduja, J. Pratheeba","doi":"10.1007/s42947-023-00377-w","DOIUrl":"https://doi.org/10.1007/s42947-023-00377-w","url":null,"abstract":"","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1007/s42947-023-00383-y
Arulanantham Anburuvel, H. M. J. L. Priyadarshana, R. M. S. T. Kulathunga
{"title":"Assessment of Mechanical Characteristics of Crushed Rock Substituted with Tyre Crumb for the Application of Road Base or Subbase Layers of Road Pavement","authors":"Arulanantham Anburuvel, H. M. J. L. Priyadarshana, R. M. S. T. Kulathunga","doi":"10.1007/s42947-023-00383-y","DOIUrl":"https://doi.org/10.1007/s42947-023-00383-y","url":null,"abstract":"","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1007/s42947-023-00382-z
Nicolet DeVine, Sarah L. Gassman, Charles E. Pierce
{"title":"Segmentation of Long Concrete Pavement Sections Based on Concrete Strength","authors":"Nicolet DeVine, Sarah L. Gassman, Charles E. Pierce","doi":"10.1007/s42947-023-00382-z","DOIUrl":"https://doi.org/10.1007/s42947-023-00382-z","url":null,"abstract":"","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Study on Pore Structure-Strength Model of Aeolian Sand Concrete Based on Grey Entropy Analysis","authors":"Huimei Zhang, Shihang Zheng, Chao Yuan, Shiguan Chen, Panyuan Jing, Yugen Li","doi":"10.1007/s42947-023-00381-0","DOIUrl":"https://doi.org/10.1007/s42947-023-00381-0","url":null,"abstract":"","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135895987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1007/s42947-023-00380-1
Dona Lavanya Ravikumar, Sundeep Inti, Veeraragavan Amirthalingam
{"title":"Use of Coconut Coir Geotextiles, a Green Material for Sustainable Low-Volume Roads","authors":"Dona Lavanya Ravikumar, Sundeep Inti, Veeraragavan Amirthalingam","doi":"10.1007/s42947-023-00380-1","DOIUrl":"https://doi.org/10.1007/s42947-023-00380-1","url":null,"abstract":"","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135895787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1007/s42947-023-00376-x
Huu Tran, Dilan Robert, Prageeth Gunarathna, Sujeeva Setunge
Abstract Multi-time step deterioration prediction of road pavements can provide more options for effective maintenance and rehabilitation decision under limited resources as compared to single time step prediction. This paper presents the 1–4 time step ahead prediction of the pavement cracking, rutting and roughness using their past values together with climate and traffic data as model inputs. Three prediction models were adopted, including the simple multiple linear regression (MLR) model and two sophisticated machine learning models, namely, support vector regression (SVR) and genetic programming (GP) models. An industry dataset of spray seal freeways is used to demonstrate the application of the methodology developed in this study. After calibration with observed data, the three prediction models are tested with unseen datasets using three performance indicators, namely, mean squared error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R squared). Among many results, all three models are unable to predict cracking with acceptable prediction accuracy. On the other hand, the rutting and roughness can be predicted with relatively good accuracy up to 4 time steps ahead. The sensitivity analysis shows that roughness and rutting prediction depends significantly on their previous or lagged values and not on remaining inputs such as annual average daily traffic (AADT) and rainfall. The methodology developed in this study is also applied to another dataset of asphalt freeways, which have similar model inputs. Similar findings are found with this dataset of asphalt freeways to that of spray seal freeways. The simple MLR model can produce similar prediction performance to the sophisticated SVR and GP models for rutting and roughness, suggesting the use of the MLR model as a benchmark for any development of prediction models for pavement deterioration.
{"title":"Multi-time Step Deterioration Prediction of Freeways Using Linear Regression and Machine Learning Approaches: A Case Study","authors":"Huu Tran, Dilan Robert, Prageeth Gunarathna, Sujeeva Setunge","doi":"10.1007/s42947-023-00376-x","DOIUrl":"https://doi.org/10.1007/s42947-023-00376-x","url":null,"abstract":"Abstract Multi-time step deterioration prediction of road pavements can provide more options for effective maintenance and rehabilitation decision under limited resources as compared to single time step prediction. This paper presents the 1–4 time step ahead prediction of the pavement cracking, rutting and roughness using their past values together with climate and traffic data as model inputs. Three prediction models were adopted, including the simple multiple linear regression (MLR) model and two sophisticated machine learning models, namely, support vector regression (SVR) and genetic programming (GP) models. An industry dataset of spray seal freeways is used to demonstrate the application of the methodology developed in this study. After calibration with observed data, the three prediction models are tested with unseen datasets using three performance indicators, namely, mean squared error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R squared). Among many results, all three models are unable to predict cracking with acceptable prediction accuracy. On the other hand, the rutting and roughness can be predicted with relatively good accuracy up to 4 time steps ahead. The sensitivity analysis shows that roughness and rutting prediction depends significantly on their previous or lagged values and not on remaining inputs such as annual average daily traffic (AADT) and rainfall. The methodology developed in this study is also applied to another dataset of asphalt freeways, which have similar model inputs. Similar findings are found with this dataset of asphalt freeways to that of spray seal freeways. The simple MLR model can produce similar prediction performance to the sophisticated SVR and GP models for rutting and roughness, suggesting the use of the MLR model as a benchmark for any development of prediction models for pavement deterioration.","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1007/s42947-023-00355-2
Jeremy Frankel, Farzaneh Tahmoorian
Abstract Gravel surfacing is a cost-effective approach for constructing roads in sparsely populated regions. However, maintaining the shape and usability of gravel roads requires regular upkeep to ensure road user safety. This study focuses on the significant gravel material specifications for wearing courses and highlights the findings of the Department of Transport and Main Roads (TMR) in Australia regarding the crucial role of material specifications in road maintenance routines. The Goondiwindi area in Queensland, featuring approximately 2000 km of gravel roads, serves as a case study for investigating the viability of granular stabilization techniques in enhancing re-sheeting materials for this network. In this research, gravel samples from ten gravel pits in the region were characterized through a range of tests, including particle-size distribution, Atterberg limit, California bearing ratio (CBR), and capillary rise. These laboratory investigations facilitated the development of a desktop analysis tool that predicts the engineering properties of gravel blends obtained from different pits. The validity of this analysis tool was assessed by comparing its results with comprehensive laboratory investigations of gravel samples and their blends. The verification process demonstrated that the results obtained from the desktop analysis tool aligned well with the test results. The study concludes that the analysis tool can effectively identify suitable gravel blends that meet target specifications, provided that the shrinkage product and CBR values of the parent pits are acceptable. The findings of this research can enhance confidence in designing gravel blends for wearing courses based on the properties of individual gravel pits, eliminating the need for additional testing on the gravel blends, and thus reducing costs.
{"title":"Improving Gravel Material Specifications for Unpaved Roads: Australian Case Study","authors":"Jeremy Frankel, Farzaneh Tahmoorian","doi":"10.1007/s42947-023-00355-2","DOIUrl":"https://doi.org/10.1007/s42947-023-00355-2","url":null,"abstract":"Abstract Gravel surfacing is a cost-effective approach for constructing roads in sparsely populated regions. However, maintaining the shape and usability of gravel roads requires regular upkeep to ensure road user safety. This study focuses on the significant gravel material specifications for wearing courses and highlights the findings of the Department of Transport and Main Roads (TMR) in Australia regarding the crucial role of material specifications in road maintenance routines. The Goondiwindi area in Queensland, featuring approximately 2000 km of gravel roads, serves as a case study for investigating the viability of granular stabilization techniques in enhancing re-sheeting materials for this network. In this research, gravel samples from ten gravel pits in the region were characterized through a range of tests, including particle-size distribution, Atterberg limit, California bearing ratio (CBR), and capillary rise. These laboratory investigations facilitated the development of a desktop analysis tool that predicts the engineering properties of gravel blends obtained from different pits. The validity of this analysis tool was assessed by comparing its results with comprehensive laboratory investigations of gravel samples and their blends. The verification process demonstrated that the results obtained from the desktop analysis tool aligned well with the test results. The study concludes that the analysis tool can effectively identify suitable gravel blends that meet target specifications, provided that the shrinkage product and CBR values of the parent pits are acceptable. The findings of this research can enhance confidence in designing gravel blends for wearing courses based on the properties of individual gravel pits, eliminating the need for additional testing on the gravel blends, and thus reducing costs.","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135436903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Measurement of the Performances of Various Asphalt Mixtures on Suspended Steel Deck Bridge Pavements","authors":"Çağlar Eren, Halit Özen, Onur Şahin, Yurdakul Aygörmez","doi":"10.1007/s42947-023-00378-9","DOIUrl":"https://doi.org/10.1007/s42947-023-00378-9","url":null,"abstract":"","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135733603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-06DOI: 10.1007/s42947-023-00374-z
Welbeck Oppong Adu, G. Dumedah, Anum Charles Adams
{"title":"Surface Condition Assessment of Unpaved Roads Through the Use of Unmanned Aerial Vehicle","authors":"Welbeck Oppong Adu, G. Dumedah, Anum Charles Adams","doi":"10.1007/s42947-023-00374-z","DOIUrl":"https://doi.org/10.1007/s42947-023-00374-z","url":null,"abstract":"","PeriodicalId":53602,"journal":{"name":"International Journal of Pavement Research and Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47496356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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