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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/34617

    Title: 鋪面糙度預測模式之建立-以LTPP資料庫為例
    Other Titles: Development of performance prediction models for pavement roughness-using LTPP database
    Authors: 莊凱驛;Chuang, Kai-yi
    Contributors: 淡江大學土木工程學系碩士班
    李英豪;Lee, Ying-haur
    Keywords: 鋪面;糙度;長程鋪面績效LTPP;績效;預測模式;Pavement;Roughness;LTPP;Performance;Prediction Model
    Date: 2007
    Issue Date: 2010-01-11 05:26:16 (UTC+8)
    Abstract: 績效預測模式普遍應用於鋪面設計、評估、維修及路網管理各個方面。當鋪面設計從純經驗法向力學經驗法發展時,估計交通荷重之單軸軸重當量(ESAL)概念在力學經驗法設計手冊中已不再採用。新的設計手冊成功與否有相當大的程度需要依賴準確的鋪面績效預測模式。因此本研究中利用美國長程鋪面績效(LTPP)資料庫(http://www.datapave.com)來探討與驗證現有鋪面糙度預測模式並改善之。
    Performance predictive models have been used in various pavement design, evaluation, rehabilitation, and network management activities. As pavement design evolves from traditional empirically based methods toward mechanistic-empirical, the equivalent single axle load (ESAL) concept used for traffic loads estimation is no longer adopted in the recommended Mechanistic-Empirical Pavement Design Guide. The success of the new design guide considerably depends upon the accuracy of pavement performance predictions. Thus, this study will first investigate its goodness of fit and strive to develop improved performance prediction models for pavement roughness using the Long-Term Pavement Performance (LTPP) database (http://www.datapave.com or LTPP DataPave Online).
    Exploratory data analysis (EDA) of the response variables indicated that the normality assumption with random errors and constant variance using conventional regression techniques might not be appropriate for prediction modeling. Therefore, generalized linear model (GLM) and general additive model (GAM) along with Poisson distribution were adopted in the subsequent analysis. Box-Cox power transformation technique, visual graphical techniques, as well as the systematic statistical and engineering approach proposed by Lee were frequently adopted during the prediction modeling process.
    By keeping only those parameters with significant effects and reasonable physical interpretations in the model, various tentative performance prediction models were developed. The goodness of the model fit was further examined through the significant testing and various sensitivity analyses of pertinent explanatory parameters. The tentatively proposed predictive models appeared to reasonably agree with the pavement performance data, although their further enhancements are possible and recommended.
    Appears in Collections:[土木工程學系暨研究所] 學位論文

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