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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120662

    题名: Improvement in Estimating Durations for Building Projects Using Artificial Neural Network and Sensitivity Analysis
    作者: Fan, Su-Ling;Yeh, I-Cheng;Chi, Wei-Sheng
    关键词: Duration;Prediction;Feature selection;Sensitivity analysis;Artificial neural network
    日期: 2021-07
    上传时间: 2021-04-26 12:10:40 (UTC+8)
    摘要: The duration of a construction project is a key factor to consider before starting a new project. It needs to be accurately estimated from an early stage. Many researchers demonstrated the applicability of regression analysis (RA) in preliminary duration estimation for construction projects; however, RA and similar models fail to simulate the complex behavior of problems in estimating. In contrast, artificial neural networks (ANNs) have several significant benefits that make them powerful and practical for solving complex problems in the field of construction engineering and modeling nonlinearity in the data. Nevertheless, ANNs have constraints because of the absence of structured methodology to decide on various control features and their “black box” nature, which does not explain the underlying input–output process. Moreover, unlike construction cost, construction duration is not determined by the summation of all activities, but only by critical activities. Given these factors, this work presents a feature selection method while applying ANNs for estimating construction duration in the preliminary stage, and proposes a two-stage ANN to take into account the specific nature of construction duration. The results confirm the potential of two-stage ANNs and feature selection by sensitivity analysis to provide a more accurate estimate of construction duration and unlock potential knowledge in the network system to increase user confidence in ANN use.
    關聯: Journal of Construction Engineering and Management 147(7), 04021050
    DOI: 10.1061/(ASCE)CO.1943-7862.0002036
    显示于类别:[土木工程學系暨研究所] 期刊論文


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