期刊論文
| 學年 | 109 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2021-07-01 |
| 作品名稱 | Improvement in Estimating Durations for Building Projects Using Artificial Neural Network and Sensitivity Analysis |
| 作品名稱（其他語言） | |
| 著者 | Su-Ling Fan; I-Cheng Yeh; Wei-Sheng Chi |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Journal of Construction Engineering and Management 147(7), 04021050 |
| 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. |
| 關鍵字 | Duration;Prediction; Feature selection;Sensitivity analysis;Artificial neural network |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120662 ) |