Recently, the award of overseas construction projects to Korean construction companies has substantially increased, and the number of Korean bids to win such projects has grown significantly also. Overseas project bidding characteristically requires a higher resource input cost in the initial preparation phase than does domestic project bidding. However, the bidding process of construction companies is frequently suspended for various reasons, and hence the opportunity cost associated with the bidding process becomes a sunk cost which, in turn, puts the bidder at a further loss. Therefore, by identifying projects with the highest chance of proceeding up to the final bid submission in the early phase of bidding, the sunk cost that results from a bid drop or suspension can be reduced and the projects with high bid probabilities can be targeted to enhance order possibility and performance capability. Unfortunately, many contractors tend to rely on qualitative assessments based on their past experiences and intuition, or on the Chief Executive Officers' (CEO) subjective instructions, when making a bid decision. In this respect, this study proposes a model utilizing the logistic regression method, analyzing the correlation between various factors of project and bid decision making to increase the effectiveness of future decision making. If factors relating to the internal decision-making process of a construction company and overseas bidding process are coordinated, the bidders can be expected to enhance the reliability of their decisions using this model, and the cost incurred during a bidding process may be reduced.
Recently Building Information Modeling (BIM) has been widely used to manage building information throughout the project life-cycle more effectively and efficiently. Particularly in quantity take-off and estimation, BIM-based process is getting more and more attention, and even BIM-based quantity take-off at the most detailed level has been performed in several building projects in South Korea. The practitioners involved in those projects have pointed out that modeling rough and finish interior of a building occupies a big portion of time in the whole modeling process and the manual modeling for interior is error-prone, which could cause serious result, such as wrong cost estimation and disputes. To resolve the problem, this research proposes an automated modeling method that model a building interior automatically after selecting an interior method by a room or space basis. This research develops a interior modeling method and a system that can model various rough and finish interior components automatically at once after a user select the type of interior for a given space or room. To do so, this research identifies typical interior types of buildings and a house built in South Korea, and develops a mechanism that can support modeling both typical and non-typical types of interior by allowing the flexibility in selecting interior material and components and the order of construction process for the selected items. The automated modeling system for building interior has been preliminarily tested at a typical condominium building project and found that the system could improve dramatically the productivity of BIM-based quantity take-off and estimation process.
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