• Korean Journal of Construction Engineering and Management
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• v.14 no.5
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• pp.175-187
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• 2013

Each participant in building construction project requires their own workspace to execute their activities. In this environment, inappropriate workspace planning in construction site causes workspace conflicts which result in a loss of productivity, safety hazard and poor-quality issues. Therefore, workspace should be regarded as one of the most important resources and constraints have to be managed at construction site. However, current construction planning techniques such as Gantt chart, network diagram and critical path method have proven to be insufficient to workspace planning. This paper contains formalized process for workspace planning in 4D BIM environment to prevent workspace related problems in construction project. The proposed process in this paper represents workspace occupation status for each activity and suitable solutions for identified workspace conflicts by integrating workspace attributes and activity execution plan. Based on the result of this study, project manager will be able to prevent probable workspace conflicts and negative effect on project performance by devising appropriate workspace plan during preconstruction phase.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.