在考慮分析技術(shù)的同時,分析被認(rèn)為是必要的。人行道暴露的無數(shù)氣候情況影響路面變形機(jī)制及其性能。路面設(shè)計中氣候方面的融合對于機(jī)械-經(jīng)驗設(shè)計實踐的發(fā)展具有重要意義。為了考慮路面設(shè)計中的氣候不一致性,必須在較長時間內(nèi)改進(jìn)包括氣候區(qū)的臨界路面溫度和降水的數(shù)據(jù)庫。在近似連續(xù)函數(shù)的同時,可以采用不同的方法。保持使用該方法的準(zhǔn)確度。從泰勒學(xué)院的系列開始,轉(zhuǎn)向Guass-Jacobian方法。每種方法都有其實用性。在本研究中,將用于python語言的Runge Kutta方法的算法應(yīng)用于用于預(yù)測路面溫度的數(shù)據(jù)集。假設(shè)系列是連續(xù)的,并且可以在將其移動到曲線上時預(yù)測其值。首先,制定了一個合適的函數(shù),并將其應(yīng)用于數(shù)據(jù),以便對特定日期進(jìn)行預(yù)測。在時間序列分析期間,在給定的參考術(shù)語中使用具有適當(dāng)精確度的圖表在校準(zhǔn)和數(shù)據(jù)解釋中起重要作用。路面設(shè)計中的氣候變量組合對于開發(fā)機(jī)械-經(jīng)驗設(shè)計敘事非常重要。由于氣候?qū)嶓w的變化而評估變化是非常重要的以及準(zhǔn)確度。本文包含了從多項式的角度進(jìn)行分析的所有算法,從線性擴(kuò)展到四次。論文介紹了使用圖形用戶界面生成圖形的算法。 

【文章頁數(shù)】：82 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
Abstract
Chapter1-Historical Background
    1.0 MERRA-Modern-Era Retrospective Analysis for Research and Application
        1.0.1 Horizontal Structure
        1.0.2 Vertical Structure
        1.0.3 MERRA-2 data collections
        1.0.4 Conventional Observations
        1.0.5 Satellite Observations of Wind
    1.1 Elucidation of EICM
    1.2 Research Background for the Enhanced Integrated Climatic Model(EICM)
    1.3 Input Parameters Needed to Run and Evaluate The EICM
    1.4 Climate and Pavement Distress
        1.4.1 Solar Radiation
        1.4.2 Thermal Radiation
        1.4.3 Blackbody Radiation
            1.4.3.1 Planck's Law
            1.4.3.2 Angular Dependence of Radiation
    1.5 Depth
    1.6 Heat Conductivity
    1.7 Reconnaissance and Data Collection
    1.8 Air Temperature Instrumentation Data
    1.9 Asphalt Temperature Instrumentation Data
    1.10 Surface Temperature
    1.11 Time of Temperature Measurements
    1.12 Temperature Depth Data
    1.13 Thermistor Depths
    1.14 Temperature Hole Depths
    1.15 LTPP Website
    1.16 Data Selection link
    1.17 Data Bucket Selection
    1.18 Data Introduction
    1.19 Data Analysis Selection
    1.20 Problem Statement EICM-P
Chapter2-Methodology
    2.1 Linear Regression
    2.2 Single Variate Linear Regression
    2.3 Multivariate Linear Regression
    2.4 Specifying the Model
    2.5 Normalization of Data
        2.5.1 Min-max normalization
        2.5.2 Z-score normalization
        2.5.3 Normalization by decimal scaling
    2.6 R-squared
    2.7 Root Mean Square Error(RMSE)
    2.8 Mean Squared Error
    2.9 Runge-Kutta Methods of Order Two
        2.9.1 Midpoint Method
        2.9.2 Modified Euler Method
        2.9.3 Higher-Order Runge-Kutta Methods
    2.10 PREDICTION MODELS
    2.11 Shading Effect on Infrared Measurements
    2.12 MATLAB Analysis
    2.13 K-Fold Cross Validation
    2.14 Selection of EICM-P
    2.15 Selection of Python
    2.16 Why Coding was necessary?
    2.17 Layer Nomenclature
    2.18 Brief History of GUI…..
    2.19 GUI Development
    2.20 Graphs of the output Results
        2.20.1 Linear Regression Graphs
        2.20.2 Combined Model Summary of all Layers
        2.20.3 Runge-Kutta Graphs
        2.20.4 Histograms
        2.20.5 Temperature Gradients
        2.20.6 Polynomials Fittings
    2.21 10-year Comparison
Chapter3 Deciphering the Literature
    3.1 EICM Paraphernalia
    3.2 Heat Conduction Module for Road Material
    3.3 Linear Model for Maximum Pavement Temperature
    3.4 Conduction heat transfer
Chapter4-Elucidation of Research Findings
Chapter5 Conclusion Summary
Recommendations for Future Work
Acknowledgements
References
List of Figures
List of Tables
Author’s Bibliography



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