Estimating Productivity of Highway Construction Tasks based on Fuzzy Theory

• Main Authors: Zi-YuenShen, 沈子淵
• Other Authors: Nang-Fei Pan
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/70606471288611736734

碩士 === 國立成功大學 === 土木工程學系碩博士班 === 98 === Estimation of the construction period is base on each construction activities length time. Generally, the estimation is compute in working productivity or working rate. It is very important that might affect the accuracy in determine construction operation duration. Highway construction is outdoors activities which surround with many uncertainties, such as wher, site condition, machinery performance, labor quality, site management and operation that will affect construction progress. In the past, several studies are using multiple linear regression analysis to establish estimation model of construction working rate. However, the work rate factors measurement is subjective due to the ambiguity of observer or experts in assessment. Therefore, the convention regression analysis is difficult to analysis such vague information or fuzzy variables. In contrast, fuzzy regression analysis has been acquainted as effective method to overcome with those fuzzy problems. This study proposed a more accurate model of working rate estimation, as a reference for department of highway engineering in create a practical working rate assessment model. Pan et al. (2009) model and a proposed model in this study are used to establish a fuzzy regression analysis in highway construction working rate; two projects in highway construction is use as study cases. The proposed model is to predict the relationship between working rate and variable factors and assist schedule management in measure exact construction duration. Moreover, the project manager can determine those operation activities and variable factors necessitate to give more attention in control and improvement since the coefficients size and positive or negative numbers from the regression model can revealed significant effect variables of the operation activities and its impact dimension.