**1. Introduction**

242 Advanced Topics in Measurements

First, we constructed our system to be similar to the EVMS, which is the existing cost/schedule integrated performance measurement method. It is therefore possible to conduct three-dimensional integrated performance management using the 18 detailed

Second, we can perform integrated qualitative performance measurement for cost/schedule/risk by measuring the risk-related cost performance index and schedule

Third, we can perform integrated quantitative performance measurement for cost/schedule/risk by measuring the cost impact variance, the schedule impact variance,

Fourth, we can measure the risk response efficiency by comparing the cost impact variance with the actual response cost, and we have proposed a method to analyze the extra project

Furthermore, using the risk performance measurement of 'Low rate of apartment sales' as an example, the theoretical and practical value and validity of our risk performance indexes and measurement system can be summarized as follows: first, because risk is a dynamic phenomenon, the forecasting and reevaluation of risk factors should be performed periodically; second, our risk performance indexes provide the theoretical foundation for an integrated evaluation of cost and scheduling risks inherent in housing redevelopment projects; and third, by using our risk performance indexes and measurement model, a project team is required to forecast and evaluate project uncertainties and risks continually, thereby generating more proactive and diverse analyses than the traditional EVMS model.

This study was performed by the 07 high-tech urban development project (Project No: 07 Urban Regeneration B03) implemented by the KICTEP and sponsored by the Ministry of

Department of Energy Guide (2008). *Earned Value Management System*, U.S. Department of

Fleming, Q. W.; Koppleman, J. M. (1996), *Earned Value Project Management,* Project

Flyvbjerg, B.; Bruzelius, N.; Rothengatter, W. (2003), *Megaprojects and Risk-An Anatomy of* 

Kaplan, R. S.; Norton, D. P. (1993). *Putting the Balanced Scorecard to Work*, Harvard Business

Kim, Seon-Gyoo (2010), Risk Performance Indexes and Measurement Systems for Mega

The KPI Working Group (2000). KPI *Report for the Minister for Construction*, Department of

the Environment, Transportation and the Regions, UK

Construction Projects. *Journal of Civil Engineering and Management,* 2010 16(4), pp.

indexes and variables employed in the risk performance index.

the cost risk response variance, and the schedule risk response variance.

expenses and actual response cost at a particular point during the project.

performance index.

**6. Acknowledgment** 

Energy

Review

**7. References** 

Land, Transportation and Maritime Affairs.

Management Institute

586-594, ISSN 1822-3605

*Ambition*, Cambridge University Press

For the topology and shape optimization of structures, the different realizations of homogenization method are widely used (Arora, 2004; Bendsoe & Sigmund, 2003). This method is highly effective for shell constructions and allows implementing topometry and topography, sizing and shape as well as freeform optimization (Vanderplaats, 2004). However, it is a very time consuming procedure because the number of design parameters can reach a million and more. There is the possibility of taking into account some technological factors, nevertheless, in the case of bulky bodies it frequently produces shapes that are difficult to manufacture. As shown in work (Mullerschon et al., 2010), the Hybrid Cellular Automata method does not allow parallelization of computations and PBS queuing system has been used. At the same time the following resource saving approach (Janushevskis et al., 2010; Janushevskis & Melnikovs, 2010) can be used for shape optimization which includes the following steps: 1) Planning the position of control points of NURBS (see, for example, Saxena & Sahay, 2005) for obtaining a smooth shape. 2) Building geometrical models using CAD software in conformity with design of experiment. 3) Calculation of responses for a complete FEM model using CAE software. 4) Building metamodels (surrogate models) for responses on the basis of computer experiment. 5) Using metamodels for shape optimization. 6) Validating the optimal design using CAE software for the complete FEM model.
