鋪面績效預測模式之構建與應用(I)

機構典藏 > College of Engineering > Graduate Institute & Department of Civil Engineering > Research Paper > Item 987654321/6809

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Title:	鋪面績效預測模式之構建與應用(I)
Other Titles:	Development and Applications of Pavement Performance Prediction Models (I)
Authors:	李英豪;葛湘瑋
Contributors:	淡江大學土木工程學系
Keywords:	鋪面績效;預測模式;鋪面維修;鋪面管理;地理資訊系統;多層次資料;多層次線性模式;線性混合模式;Pavement Performance;Prediction Model;Pavement;Evaluation;Pavement management;Geographic Information System;Multilevel data;Hierarchical linear models;Linear Mixed-Effects Models
Date:	2004
Issue Date:	2009-03-16 14:46:34 (UTC+8)
Abstract:	鋪面績效預測模式在鋪面設計、鋪面評估與維修、與鋪面管理系統扮演著極為重要的角色，然而在鋪面績效資料分析時，常常發現它違反了傳統迴歸法對於隨機誤差所做的假設。鋪面績效資料屬於多層次資料的一種，分析此類的資料通常採用多層次線性模式/線性混合模式(HLMs/ LMEs)。因為資料的結構具有層級性，多層次模式的資料探索分析、統計模式的建立及模式評估比標準複迴歸複雜。因此，極需研擬如何利用上述方法以改善系統化的統計與工程分析方法來構建鋪面績效預測模式。計畫主持人擬以三年三期的方式，利用美國長程鋪面績效資料庫LTPP DataPave Online (http://www.datapave.com)從事「鋪面績效預測模式的構建與應用」研究，主要的研究內容包括： 1. 鋪面標準損壞調查手冊之研擬。 2. 美國長期鋪面績效資料庫之本土化應用。 3. 研擬系統化的統計與工程分析方法：探討傳統迴歸方法(線性與非線性迴歸)、當代迴歸技術、與線性混合模式之適用性與應用。 4. 建立剛性鋪面績效預測模式：分別建立維修前與維修後鋪面之橫向裂縫、縱向裂縫、接縫破壞、高差、PSR、與IRI 等預測模式。 5. 建立柔性鋪面績效預測模式：分別建立柔性維修前與維修後鋪面之裂縫、車轍、PSR、與IRI 等預測模式。 6. 績效預測模式的本土化應用。 7. 預測模式的本土化應用與系統整合(ICSMART-R, NETDSD, TKUNET)。 8. 查詢網站與相關連結之建立。此外，本研究將於計畫最後以實際案例分析，驗證研究成果的正確性與適用性，並建立回饋機制，以期望此一鋪面管理系統能成為適合於國內使用的決策輔助工具。 Improved performance predictive models are greatly needed for use in various pavement applications including design, evaluation, rehabilitation, and network management. Nevertheless, the performance data often violated the assumptions of random errors and constant variance using conventional regression techniques. Pavement performance data is a very common example of multilevel data. Hierarchical linear models/linear mixed-effects (HLMs/ LMEs) models are often utilized to analyze multilevel data. Because of the hierarchy of data structure, the exploratory analysis, statistical modeling, and examination of model-fit of multilevel data are more complicated than those of standard multiple regressions. Thus, it is very crucial to investigate its possible applications to the existing systematic statistical and engineering approach for the development of pavement performance prediction models. The entire project consists of three phases (I, II, and III) to be completed within three years to conduct 「development and applications of pavement performance prediction models,」 using the well-known Long-Term Pavement Performance (LTPP) database (LTPP DataPave Online) (http://www.datapave.com). The major tasks include: 1. Prepare standard pavement distress identification manuals for domestic use. 2. Domestic applications of the LTPP database. 3. Revise the proposed the systematic statistical and engineering approach including conventional regression techniques (linear and nonlinear), modern regression techniques, and linear mixed-effects models. 4. Develop rigid pavement performance models including transverse cracking, longitudinal cracking, joint deterioration (spalling), faulting, PSR, and IRI for new and rehabilitated pavements separately. 5. Develop flexible pavement performance models including fatigue cracking, rutting, PSR, and IRI for new and rehabilitated pavements separately. 6. Domestic considerations and applications of the aforementioned predictive models. 7. Revise the existing prototype programs for pavement project-level rehabilitation and network-level management purposes including ICSMART-R, NETDSD, and TKUNET programs. 8. Establish a web-site for future communications and experience exchanges. The completion of this study will, hopefully, provide a sound basis for future development and integration of our domestic network pavement databases and network optimization analysis so as to assure the best use of our limited resources.
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