- 學位論文
應用LTPP資料庫於剛性鋪面績效預測模式之建立
Development of Performance Prediction Models for Rigid Pavements Using LTPP Database
摘要
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。鋪面設計發展是以傳統經驗為基礎朝向力學經驗法，在力學經驗鋪面設計手冊中，建議估算交通量時不再使用等效單軸載重(EASL)概念。新設計方法的成功取決於鋪面績效預測的準確性，因此本研究使用美國長程鋪面績效資料庫LTPP (http://www.datapave.com或LTPP線上資料庫)，對剛性鋪面現有預測模式進行評估並嘗試改善現有模式。 將預測模式的反應變數進行資料探索分析，發現隨機誤差與自變數為常態分配的假設，並不適用一般傳統迴歸技術。因此，在不假設反應變數為任何誤差分佈情形下，本研究採用概似估計法與柏松分配，配合廣義線性模式(GLM)與廣義相加模式(GAM) 於後續分析。並以Box-Cox power transformation轉換法、視覺圖的技術，以及李英豪教授建議的系統化之統計與工程分析方法應用於構建預測模式中。 研究中構建了各種績效預測模式，所用參數對鋪面破壞皆有重要的影響且符合物理解釋。藉由統計檢定與相關參數的敏感度分析，可進一步檢查模式的適合度。本研究提出的績效預測模式，除了接縫碎裂模式外，其餘似乎與鋪面績效資料一致，未來亦可作更進一步之改進，使其更為完善。
並列摘要
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 rigid pavements 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, without assuming the error distribution of the response variable, generalized linear model (GLM) and general additive model (GAM) along with quasi-likelihood estimation method and 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 with the exception of spalling models, although their further enhancements are possible and recommended.