| dc.description.abstract | The needs of electricity in our daily lives is becoming more and more essential. To accommodate such needs, the development of electrical transmission and distribution system requires to be sped up, mainly on the Extra High Voltage (EHV) Transmission Tower due to the importance of the tower to transmit power throughout the country. In order to construct such tower, Taiwan Power Company (TPC) has a possibility to announce a tender so that an EPC company can carry out the task. The construction of EHV Transmission Tower is divided into two categories: one is to construct the EHV Tower Foundation, and the other is to construct the EHV Tower and the connection. To determine the tender price, TPC has been doing initial analysis for each project to find the optimum forecast price for the tender. However, this process takes a long time and depending on the number of towers, this probably takes a large amount of manpower as well. Therefore, this study aims to determine the critical variables needed to forecast the cost for EHV Tower Foundation project, so that it can reduce the time and manpower needed to accurately predict the said cost. Other than that, this study also investigates the variable’s cluster characteristics, and discovers interesting relationship between the selected variables. To achieve the result, k-means clustering is applied to cluster each variable from the dataset based on the cost. Aside from that, rule-based association is also employed together with Apriori algorithm to find the interesting relationship between the selected variables. From the analysis, it is found that single pile foundation (X2), manual excavation (X5), and common formwork (X7) contribute greatly to the cost of the EHV Tower Foundation from the 13 selected variables found. This is also shown by the high amount of these three variables within the 2nd cluster which has the highest cost among the other clusters. As for rule-based association, with minimum support of 0.1, a total of 1174 rules are found, where 102 rules with 2 antecedents and 1 consequent are selected. From these 102 rules, 62 rules have 1.000 confidence, and from these 62 rules, 6 rules with the highest lift are found. All of the 102 rules have lift value more than one, which means every rule has its own significance with the highest value of lift of 9.2 which has lower number of support, but high confidence, making these 6 rules the most significant out of the other 96 rules. | en_US |