1. Quantification of the Significance of M&C variables on Pavement Performance Using Neural Network. Jae-ho Choi CS539 Civil & Environmental Engineering

2. Background Pavement Performance – Single variable to represent overall condition of pavement surface. Specification development 1. Method-type specification 2. End-result specification 3. Performance-related specification(PRS)

3. Problem statement The payment schedules in end-result specification – based on historical performance of construction industry not on the loss in performance. Pay factor items & pay adjustment schedules are largely based on engineering judgment.

4. Objective Identify Material & Construction (M&C) variables. Find the relative importance of the different variables to the development of any PRS.

5. FC PD SNAC Stiff ACAV Sta bil. RV LTC …. IRI Neural Network Modeling (Top-down approach)

6. Input and Output Data to MLP AgeAADTThicknessLayer_NoPILL P200ACAV IRI Input & Output variables Output variable Exterior factorsStructural factors Material testing factors

7. Binary Representation of IRI for data output IRIBinary Representation 0.00 ~ 1.25 1 0 0 0 0 1.25 ~ 1.5 0 1 0 0 0 1.5 ~ 2.0 0 0 1 0 0 2.0 ~ 2.5 0 0 0 1 0 2.5 ~ 3.0 0 0 0 0 1

8. Three-fold cross-validation Trial 1 Trial 2 Trial 3 Used where there is a scarcity of labeled examples compared with the complexity of the problem Train data setTest data set

9. Average Correct Classification Rate Network Structure Validation one Validation two Validation three 9-3-551.6763.7970.11 9-6-562.2266.6775.86 9-9-553.5560.3475.86 9-18-558.3362.0774.13 Fixed parameters( Learning Rate – 0.1, 0.01, Momentum – 0.1, 0.5, Epoch size - 15000)

10. Connection Weight Analysis using 9-6-5 network structure Hidden Node V1V2V3V9OUT 18.367.4714.06….7.93-3.52 2-1.641.36-4.45….2.04-10.98 30.91-5.15-10.14….-8.10-10.72 45.181.542.34….-0.35-5.47 53.06-10.015.37….2.321.29 6-3.703.126.95…. 9.77-11.81

11. Input Node Share of Output Connections Layer _No Mean Thick. AgeP200….. AC 7.08 (%) 7.60 (%) 14.26 (%) 19.02 (%) ….. 7.32 (%)

12. Discussion P200  Age  AV  AADT  LL  PI  Mean Thickness  Asphalt Content  Layer_no Material characteristics are more significant than pavement structural factors. Ex) P200, AV, LL, PI > Mean Thickness, Layer_no This result can be used to develop new components for PRS. The relative importance of the different variables is important to the design process and is important to the contractor in determining which factors have the largest effect on the bid price.