| Summary: | 碩士 === 國立臺灣科技大學 === 營建工程系 === 99 === This study has a two-fold objective. First, it conducts a mechanism enhance time series data of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model is called EFSIMET. The EFSIMET was developed particularly to treat construction management problems that contain time series data. The EFSIMET¬ is an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the author develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has the capacity to address the complex and dynamic nature of time series data; thus, it could improve the model’s performance significantly.
Nowadays, construction management is facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMET to treat two typical problems in construction: forecasting cash-flow and estimate at completion. Through performance’s comparison with previous works, the effectiveness and real world application of EFSIMET are proved. Hence, this model may be use as an intelligent decision support tool to assist the decision-making process to solve the construction management’s difficulties.
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