Abstract
Performance modelling of gravel roadways is required to predict the conditions in the future and provide information for optimal maintenance interventions. Roads are expensive asset and should be properly maintained regardless of their class or function to enhance their performance. Optimal maintenance interventions at the appropriate time to preserve the asset value are required. As improvement over previous methodologies, this paper modifies the logistic regression model by incorporating the ordinal nature of a dependent variable through defining the probabilities differently to develop improved gravel road performance models based on site specific data. The models reflect the history of gravel loss conditions to predict future performance for gravel roads in Botswana as a threshold to trigger optimal maintenance interventions. The input data for the models were generated from the triennial condition survey for Botswana carried out in 2002, 2005, and 2008. The developed improved gravel road performance methodologies are long term plans for preservation and maintenance as gravel road management systems in Botswana.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Transportation and Development 2016 |
| Subtitle of host publication | Projects and Practices for Prosperity - Proceedings of the 2016 International Conference on Transportation and Development |
| Editors | Kelvin C. P. Wang |
| Publisher | American Society of Civil Engineers (ASCE) |
| Pages | 1142-1150 |
| Number of pages | 9 |
| ISBN (Electronic) | 9780784479926 |
| DOIs | |
| Publication status | Published - Jan 1 2016 |
| Event | International Conference on Transportation and Development 2016: Projects and Practices for Prosperity - Houston, United States Duration: Jun 26 2016 → Jun 29 2016 |
Other
| Other | International Conference on Transportation and Development 2016: Projects and Practices for Prosperity |
|---|---|
| Country | United States |
| City | Houston |
| Period | 6/26/16 → 6/29/16 |
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All Science Journal Classification (ASJC) codes
- Transportation
- Civil and Structural Engineering
- Mechanics of Materials
- Safety, Risk, Reliability and Quality
- Geography, Planning and Development
Cite this
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Performance Modelling for Botswana Gravel Roadways : Outcomes and Conclusions. / Oladele, A. S.
International Conference on Transportation and Development 2016: Projects and Practices for Prosperity - Proceedings of the 2016 International Conference on Transportation and Development. ed. / Kelvin C. P. Wang. American Society of Civil Engineers (ASCE), 2016. p. 1142-1150.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Performance Modelling for Botswana Gravel Roadways
T2 - Outcomes and Conclusions
AU - Oladele, A. S.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Performance modelling of gravel roadways is required to predict the conditions in the future and provide information for optimal maintenance interventions. Roads are expensive asset and should be properly maintained regardless of their class or function to enhance their performance. Optimal maintenance interventions at the appropriate time to preserve the asset value are required. As improvement over previous methodologies, this paper modifies the logistic regression model by incorporating the ordinal nature of a dependent variable through defining the probabilities differently to develop improved gravel road performance models based on site specific data. The models reflect the history of gravel loss conditions to predict future performance for gravel roads in Botswana as a threshold to trigger optimal maintenance interventions. The input data for the models were generated from the triennial condition survey for Botswana carried out in 2002, 2005, and 2008. The developed improved gravel road performance methodologies are long term plans for preservation and maintenance as gravel road management systems in Botswana.
AB - Performance modelling of gravel roadways is required to predict the conditions in the future and provide information for optimal maintenance interventions. Roads are expensive asset and should be properly maintained regardless of their class or function to enhance their performance. Optimal maintenance interventions at the appropriate time to preserve the asset value are required. As improvement over previous methodologies, this paper modifies the logistic regression model by incorporating the ordinal nature of a dependent variable through defining the probabilities differently to develop improved gravel road performance models based on site specific data. The models reflect the history of gravel loss conditions to predict future performance for gravel roads in Botswana as a threshold to trigger optimal maintenance interventions. The input data for the models were generated from the triennial condition survey for Botswana carried out in 2002, 2005, and 2008. The developed improved gravel road performance methodologies are long term plans for preservation and maintenance as gravel road management systems in Botswana.
UR - http://www.scopus.com/inward/record.url?scp=84988350516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988350516&partnerID=8YFLogxK
U2 - 10.1061/9780784479926.102
DO - 10.1061/9780784479926.102
M3 - Conference contribution
SP - 1142
EP - 1150
BT - International Conference on Transportation and Development 2016
A2 - Wang, Kelvin C. P.
PB - American Society of Civil Engineers (ASCE)
ER -