Construction Cost and Carbon Emission Computational Model for Office Buildings in Malaysia
Keywords:Carbon emission, Construction, Computational Model, Optimization
A novel embodied carbon emission and construction cost computational optimization model has been developed based on evolutionary Genetic Algorithm (GA) for purpose built offices in the Malaysian construction industry. The GA evaluation was lack of implementation in addressing financial and environmental performances for construction projects in Malaysia. Therefore, the office project was evaluated through the adoption of ISO 14040 framework and evolutionary GA. The model was designed to provide alternative optimal design solutions for office buildings, which can be used at the early project planning and design stages. The assessment of embodied emissions was limited to pre-construction phase with â€œcradle to siteâ€ boundary. The model was tested statically to confirm the accuracy of the generated results. It provides an assessment model for managing carbon emission based on evaluation of environmental and financial performancesand it was validated by an application to an office building and the findings obtained suggest that the it would be suitable for use in practice.
 Gangolells, Marta, Casals, Miquel, GassÃ³, Santiago, Forcada, NÃºria, Roca,Xavier, & Fuertes, Alba. (2009). A methodology for predicting the severity of environmental impacts related to the construction process of residential buildings. Building and Environment, 44(3), 558-571.
 Goicoechea, A, Hansen DR, and Duckstein L, . (1982). Multiobjective Analysis With Engineering and Business Applications. USA: John Wiley & Sons Inc.
 Hong, J., Hong, J., Otaki, M., & Jolliet, O. (2009). Environmental and economic life cycle assessment for sewage sludge treatment processes in Japan. Waste Manag, 29(2), 696-703.
 Ko, Chien-Ho, & Cheng, Min-Yuan. (2007). Dynamic prediction of project success using artificial intelligence. Journal of construction engineering and management, 133(4), 316-324.
 Murty, Katta G. (2009). Optimization for Decision Making: Springer.
 Truitt, P. (2008). Quantifying Greenhouse Gas Emissions from Key Industrial Sectors in the United States. Washington, DC: US Environmental Protection Agency, 1.
 Yulan, Jin, Zuhua, Jiang, & Wenrui, Hou. (2007). Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine. The International Journal of Advanced Manufacturing Technology, 39(9-10), 954-964.
 Zheng, Daisy XM, Ng, S Thomas, & Kumaraswamy, Mohan M. (2004). Applying a genetic algorithm-based multiobjective approach for time-cost optimization. Journal of Construction Engineering and Management, 130(2), 168-176.