This study is to examine the public participation activities in the progress of the design competition, purposed to provide urban public spaces by the reuse of industrial heritages. Through the case studies of Berlin Tempelhof Parkland, Mapo Depot and Seoul Station 7017, following activities are common: citizen idea competition, forum, field trip, survey and winners exhibition. However, those activities are more closely organized to the competition process in Tempelhof Parkland, than other tow projects of Seoul Metropolitan Government. Also, it enables to clear and develop the public ideas more that those programs such as survey, citizen idea competition and field trip, are linked to each other in the case of Tempelhof Parkland. It could be achieved by the unified organization between competition preparation and public participation operation.

Currently whole-house refurbishment for substantial energy efficiency improvement of existing housing stock is needed to achieve the targeted 80% CO2 emission reduction. As whole-house refurbishment requires a larger capital investment for lower CO2 emission, the simultaneous use of Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) methodologies are recommended to generate affordable refurbishment solutions. However, two methodologies are difficult to use due to a lack of proper LCC and LCA datasets. As a response to the current problems, many researchers explore potentials in Building Information Modelling (BIM) to improve current construction practice. As a result, a BIM tool - IES IMPACT (Integrated Material Profile And Costing Tool) - has been introduced to the UK construction industry for simultaneous calculation of LCC and LCA. Thus, this research aims at examining the capability and limitation of the IES VE/IMPACT as a BIM tool for whole-house refurbishment. This research reveals that the IES VE/IMPACT is feasible for whole-house refurbishment by providing LCC and LCA information simultaneously for informed decision on refurbishment solution selection. This research shed lights on the current problems lying on the data exchange between two different BIM tools. It is revealed that additional efforts from construction professionals and industry are required to make reliable BIM objects library with LCC and LCA datasets.

Recently, for the purpose of maintenance of facilities and energy, there have been growing cases of the 3D image scan-based reverse design technology mostly in the manufacturing field. In the MEP field, because of differences between design and physical model, the reverse technology has been utilized in factory facilities such as a semiconductor factory. Because 3D point clouds from scanning include accurate 3D object information, the efficiency of management works related to the complex MEP facilities can be enhanced. In this study, the reverse technology was surveyed, and the MEP facility management based on 3D image scanning was analyzed. Based on the results, a method of 3D image scan-based MEP facility management was proposed.

A Building Information Model (BIM) is an attempt to simulate the process of building structures in a three-dimensional (3D) digital space. While the technology is usually applied to structured buildings, bridges, and underground facilities, it is rarely applied to an unstructured environment of earthwork operations. If a BIM is used for earthworks, the 3D simulation can be used for construction equipment guidance and earthwork management. This paper presents a real-time, 3D earthwork BIM that provides a 3D graphical simulation of excavators in conjunction with geographic modeling. Developing a real-time, 3D earthwork BIM requires handling a variety of factors, such as geographical information and vehicular movement. This paper mainly focuses on the management of these attributes and provides a database design for storing and retrieving data. In an example application, a prototype of the 3D earthwork BIM is presented to understand what it would provide when used during earthwork operations at a construction site.

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.