The Study of Artificial Neural Networks Applied in Engineering Cost Estimation of High-Tech Manufacturing Facility Project
|Keywords:||工程成本估算;類神經網路;線性複迴歸;化學品相關系統;engineering cost estimation;multiple liner regression;artificial neural networks;chemical related system|
The chemical related systems of the most high-tech factories were built by the third-party engineering construction companies via the competitive bidding. In order to acquire the project, the engineering construction companies have to accurately estimate the project cost. Traditionally, most domestic companies of the engineering construction in Taiwan usually estimate the project cost of the chemical related systems based on the professional experience of employees. In fact, the project cost could not be easily estimated because the employees might not have enohgh experience of the project cost estimation. In this paper, the real project data of an engineering construction company from 2003 to 2012 were taken for 79 real cases. Firstly, the stepwise multiple liner regression was used to identify the significant variables. After that, the significant variables were regarded as input data, the estimation model of project cost was established by using the artificial neural networks. According the results, the accuracy rate of the project cost estimation is about 97.22%. In other words, the artificial neural networks can generate the reasonable project cost. Therefore, they can accurately estimate the project cost via the estimation model established by the artificial neural networks.
|Appears in Collections:||Thesis|