As the spread of new and renewable power generation facilities, the fixed investment cost CAPEX(Capital Expenditure) of solar power generation facilities decreases due to continuous technological development, and the impact of O&M costs that determine investment success has increased. For this reason, the importance of technologies such as accuracy of O&M cost calculation through ICT, failure prediction, and predictive maintenance have emerged. In the above paper, based on the cost-breakdown structure design and failure rate model design of the solar power generation facility using engineering estimation method, the maintenance cost of the solar power generation facility, which is a renewable power generation facility, is predicted and the maintenance cost used was compared and confirmed. In addition, the cost-breakdown structure and failure rate model of solar power generation facilities were designed and developed by incorporating them into a new program of economic evaluation of new and renewable power generation facilities.

In this paper, we developed a predictive model of maintenance and repair cost based on the element-level condition state by using information collected through the inspection/diagnosis of individual bridges for use in maintenance strategy analysis. The reliability of the prediction model can be improved through of big data analysis using various factors and additional data.

This paper presents the results of a life-cycle cost (LCC) analysis to select an efficient maintenance action for a deteriorated waterfront structure. Three different maintenance actions, a routine maintenance and two types of rehabilitation methods, were assumed and the corresponding the LCCs were calculated using a LC activity profile. The results of the analysis indicate that the LCC analysis can be effectively used to examine the maintenance alternative actions during the decision making process.