Pavement
- Publication no: AP-T304-16
- ISBN: 978-1-925294-91-0
- Published: 6 January 2016
- PDF (free) Download
This proof of concept study used the data condensation technology of stochastic information packets (SIPs) in MS Excel to allow complete storage of all collected data. This approach, when connected to a pavement management system (PMS), was able to use the measured uncertainties of the variables employed in predicting pavement performance to accurately quantify the risks, in percentile probabilities, of achieving the target level of service (LoS) and of meeting annual targeted maintenance costs.
The SIPs used in this study open opportunities in data collection, storage and analysis. Because large quantities of data can be stored in a relative small space, the full data set can easily be stored, transported and used. Cost estimates, project management, quality control, quality assurance and in general all engineering calculations where currently averages are used as input, can be replaced with the techniques described in this study.
- Summary
- 1. Introduction
- 1.1. Background
- 1.2. Objective
- 1.3. Linkages
- 1.4. Anticipated Benefits
- 2. Scope of Work: Proof of Concept Study
- 3. Methodology
- 3.1. Terminology
- 3.1.1. Distribution
- 3.1.2. Probability
- 3.2. Uncertain Variables in Road Deterioration Modelling
- 3.3. Historical Approaches to Address Uncertain Variables
- 3.3.1. Markov Chain Model
- 3.3.2. Monte Carlo Simulation
- 3.3.3. Limitation of Monte Carlo Implementation in a PMS
- 3.4. Stochastic Information Packet (SIP) Arithmetic
- 3.5. Data Source
- 4. Storing SIPs in a Database
- 5. Implementing SIP Operations in a PMS
- 5.1. Prototype Model
- 5.1.1. Model Calculations
- 5.1.2. Input Data
- 5.2. Deterioration Predictions
- 5.2.1. Rutting
- 5.2.2. Roughness
- 5.2.3. Transfer to dTIMS
- 5.3. Treatment Selection and Works Effect
- 5.4. Optimisation
- 6. Results of the Proof of Concept Study
- 6.1. Proof of Concept
- 6.2. Additional Benefits
- 7. Future Work
- 7.1. Software Development
- 7.1.1. Storage of SIPs
- 7.1.2. SIP Operations in dTIMS
- 7.1.3. Development of SIP Databases
- 7.2. Data Preparation
- 7.3. Training and Education
- 8. Summary and Conclusion
- References
- Appendix A Models in Conventional and SDXL Format
- Appendix B Input Condition Data in SIP Format