English  |  正體中文  |  简体中文  |  Items with full text/Total items : 57970/91504 (63%)
Visitors : 13687889      Online Users : 32
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/6809

    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.
    Appears in Collections:[Graduate Institute & Department of Civil Engineering] Research Paper

    Files in This Item:

    File Description SizeFormat
    932211E032016.pdf274KbAdobe PDF490View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback