研究生: |
黃日德 Hoang - Nhat Duc |
---|---|
論文名稱: |
ESTIMATE AT COMPLETION USING TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL ESTIMATE AT COMPLETION USING TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL |
指導教授: |
鄭明淵
Min-Yuan Cheng |
口試委員: |
陳鴻銘
Hung-Ming Chen 潘南飛 Nang-Fei Pan |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 營建工程系 Department of Civil and Construction Engineering |
論文出版年: | 2010 |
畢業學年度: | 98 |
語文別: | 英文 |
論文頁數: | 106 |
中文關鍵詞: | Time Series Forecasting 、Fuzzy Logic 、weighted Support Vector Machine 、Estimate at Completion |
外文關鍵詞: | Estimate at Completion, weighted Support Vector Machine, Fuzzy Logic, Time Series Forecasting |
相關次數: | 點閱:393 下載:0 |
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In construction management, successful in cost control during construction stage is critical to the general contractor survival. The reason is that the bottom line of construction management is to ensure the project carried out within the planned budget. Cost overrun may lead to profit damage, occasionally even bring about project failure. To deal with such issue, this research utilizes Estimate at Completion (EAC) and Time-dependent Evolutionary Fuzzy Support Vector Machine (EFSIMT) to form the model (EAC-EFSIMT) uniquely for EAC prediction. In EAC-EFSIMT, the Support Vector Machines is utilized as a supervised learning instrument, in order to infer the causal relationship between multiple attributes in the input space and EAC as the single output in the output space. The fuzzy logic is used to emphasize the approximate reasoning. Moreover, to address the feature of time-dependent data, the inference model employs 3 types of time series functions (Linear, Quadratic, and Exponential) to weight training data points. The effect of each time series function on the model performance is investigated individually. This research work has proved that integration of time series function can meliorate the outcome of EAC prediction. Through the training, testing and results comparison process, the Exponential function has been identified as the preferable time series function for EAC problem. Moreover, the capability of EAC-EFSIMT in real-world situation is demonstrated. It is shown that newly proposed model is an effective replacement for previous methods in construction project cost control.
In construction management, successful in cost control during construction stage is critical to the general contractor survival. The reason is that the bottom line of construction management is to ensure the project carried out within the planned budget. Cost overrun may lead to profit damage, occasionally even bring about project failure. To deal with such issue, this research utilizes Estimate at Completion (EAC) and Time-dependent Evolutionary Fuzzy Support Vector Machine (EFSIMT) to form the model (EAC-EFSIMT) uniquely for EAC prediction. In EAC-EFSIMT, the Support Vector Machines is utilized as a supervised learning instrument, in order to infer the causal relationship between multiple attributes in the input space and EAC as the single output in the output space. The fuzzy logic is used to emphasize the approximate reasoning. Moreover, to address the feature of time-dependent data, the inference model employs 3 types of time series functions (Linear, Quadratic, and Exponential) to weight training data points. The effect of each time series function on the model performance is investigated individually. This research work has proved that integration of time series function can meliorate the outcome of EAC prediction. Through the training, testing and results comparison process, the Exponential function has been identified as the preferable time series function for EAC problem. Moreover, the capability of EAC-EFSIMT in real-world situation is demonstrated. It is shown that newly proposed model is an effective replacement for previous methods in construction project cost control.
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