LEARNING-CURVE PREDICTORS FOR CONSTRUCTION FIELD OPERATIONS

被引：47

作者：

EVERETT, JG

FARGHAL, S

机构：

[1] Dept. of Civ. and Envir. Engrg., Univ. of Michigan, Ann Arbor

[2] Dept. of Civ. and Envir. Engrg., Univ. of Michigan, Ann Arbor, MI

来源：

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE | 1994年 / 120卷 / 03期

关键词：

D O I：

10.1061/(ASCE)0733-9364(1994)120:3(603)

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Many repetitive construction field operations exhibit aa learning curve, over which the time or cost per cycle decreases as the cycle number increases. This paper evaluates several mathematical modes to determine which best describes the relationship between the activity time or cost and the cycle number. For completed activities, cubic learning curve models are found to provide the most reliable statistical fit, and linear models provide the least reliable fit. The real potential value of learning curves is their ability to predict the time or cost needed to perform future activities. This paper presents aa methodology for predicting future activity time or cost based on completed activity data. The best predictors of future performance are found to be linear models. The cubic models that best describe completed activities are poor predictors of future performance.

引用

页码：603 / 616

页数：14

共 11 条

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