Eco-friendly materials have been developed recently that have made it possible to significantly reduce the maintenance cost of buildings when they are appropriately used in renovation. Indeed, it became extremely important to consider the eco-friendly energy-saving effects on design alternatives during renovation. The present study proposes a framework for the optimum maintenance decision-making model for considering eco-friendly energy to help people interested in making decisions concerning renovation; it requires that both the environmental friendliness and economic feasibility of the target building be simultaneously considered. Several studies mainly cover the structural aspects for energy improvements based on innovation and technology. However, energy simulation in existing buildings needs some additional consideration regarding the economic analysis of energy savings and the recovery period of construction costs. A case study was conducted as a research method by utilizing the proposed framework, which aims to: (1) make energy simulations with different basic design assumptions; (2) perform the energy simulations through building information modeling (BIM) technology; and (3) analyze the economic feasibility of the alternatives. As a result, an alternative combination that can save the net maximum energy cost during the life cycle period and invest the lowest renovation costs has been recommended. Furthermore, effective guidelines were proposed on which items the building owner values, depending on his economic investment conditions in decision-making regarding the level of design, through a comprehensive review of the energy savings by design variable. It is expected that the research findings will be utilized in the decision-making process and for conducting further relevant research in future.

Decisions made in the early stages of construction projects significantly influence the costs incurred in subsequent stages. Therefore, such decisions must be based on the life-cycle cost (LCC), which includes the maintenance, repair, and replacement (MRR) costs in addition to construction costs. Furthermore, as uncertainty is inherent during the early stages, it must be considered in making predictions of the LCC more probabilistic. This study proposes a probabilistic LCC prediction model developed by applying the Monte Carlo simulation (MCS) to an LCC prediction model based on case-based reasoning (CBR) to support the decision-making process in the early stages of construction projects. The model was developed in two phases: first, two LCC prediction models were constructed using CBR and multiple-regression analysis. Through k-fold validation, one model with superior prediction performance was selected; second, a probabilistic LCC model was developed by applying the MCS to the selected model. The probabilistic LCC prediction model proposed in this study can generate probabilistic prediction results that consider the uncertainty of information available at the early stages of a project. Thus, it can enhance reliability in actual situations and be more useful for clients who support both construction and MRR costs, such as those in the public sector.

Abstract:Program management is the structured and strategic process of managing multiple projects at a high level to maximize benefits. The essentials of programs include high costs and long implementation periods, and thus, the negative impacts caused by the failure of program management are more significant and greater than that of a project. Therefore, to achieve high program performance, it is essential for program management to be well defined during the early stages. However, the existing research is mainly focused on the performance prediction methodologies for projects, while the research pertaining to programs has concentrated on identifying the qualitative critical success factors (CSFs). Thus, this study developed a methodology for predicting the program performance. Forty-five CSFs were identified herein from literature review and expert interviews, then grouped through factor analysis. In addition, the Program Definition Rating Index (PgDRI) was developed by calculating the weights of the proposed CSFs through structured equation modeling in order to evaluate the quantitative program performance. For validation, the PgDRI was applied to three in-progress cases, and the PgDRI scores were compared with the actual performance of each case. The PgDRI developed in this study can contribute to the body of knowledge pertaining to program management by quantifying the performance management of a program. In addition, the PgDRI can be utilized in the performance management of a program in terms of the cost and schedule by allowing practitioners to apply the PgDRI repeatedly to the major decision-making processes during the early stages of a program.