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Quantification of the Significance of M&C variables on Pavement Performance Using Neural Network.

Quantification of the Significance of M&C variables on Pavement Performance Using Neural Network. Jae-ho Choi CS539 Civil & Environmental Engineering. Background. Pavement Performance – Single variable to represent overall condition of pavement surface. Specification development

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Quantification of the Significance of M&C variables on Pavement Performance Using Neural Network.

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  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 • Method-type specification • End-result specification • 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. Neural Network Modeling (Top-down approach) IRI RV LTC PD FC SN AC Stiff AC AV Stabil. …. ….

  6. Input and Output Data to MLP Input & Output variables Exterior factors Structural factors Material testing factors Output variable

  7. Binary Representation of IRI for data output

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

  9. Average Correct Classification Rate 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

  11. Input Node Share of Output Connections

  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.

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