教師資料查詢 | 類別: 期刊論文 | 教師: 葉怡成YEH, I-CHENG (瀏覽個人網頁)

標題:Modeling asphalt pavement overlay transverse cracks using the genetic operation tree and Levenberg-€“Marquardt Method
學年100
學期2
出版(發表)日期2012/04/01
作品名稱Modeling asphalt pavement overlay transverse cracks using the genetic operation tree and Levenberg-€“Marquardt Method
作品名稱(其他語言)
著者Machine Hsie; Yueh-Feng Ho; Chih-Tsang Lin; Yeh, I-Cheng
單位淡江大學土木工程學系
出版者Elsevier
著錄名稱、卷期、頁數Expert Systems with Applications 39(5), pp.4874-€“4881
摘要The Artificial Neural Network (ANN) and the nonlinear regression method are commonly used to build models from experimental data. However, the ANN has been criticized for incapable of providing clear relationships and physical meanings, and is usually regarded as a black box. The nonlinear regression method needs predefined and correct formula structures to process parameter search in terms of the minimal sum of square errors. Unfortunately, the formula structures of these models are often unclear and cannot be defined in advance. To overcome these challenges, this study proposes a novel approach, called ‘‘LMGOT,’’ that integrates two optimization techniques: the Levenberg–Marquardt (LM) Method and the genetic operation tree (GOT). The GOT borrows the concept from the genetic algorithm, a famous algorithm for solving discrete optimization problems, to generate operation trees (OTs), which represent the structures of the formulas. Meanwhile, the LM takes advantage of its merit for solving nonlinear continuous optimization problems, and determines the coefficients in the GOTs that best fit the experimental data. This paper uses the LMGOT to investigate the data sets of pavement cracks from a 15-year experiment conducted by the Texas Departments of Transportation. Results show a concise formula for predicting the length of pavement transverse cracking, and indicate that the LMGOT is an efficient approach to building an accurate crack model.
關鍵字Levenberg-€“Marquardt; Genetic operation tree; Asphalt pavement cracking
語言英文(美國)
ISSN0957-4174
期刊性質國外
收錄於SCI;
產學合作
通訊作者Machine Hsie
審稿制度
國別荷蘭
公開徵稿
出版型式紙本;
相關連結
SDGs
Google+ 推薦功能,讓全世界都能看到您的推薦!