Sustainable Life Cycle Analysis in Built Environment: Application to Rail Network

Yang Yao

Abstract

The rapid national infrastructure development consumes abundant natural/ non-renewable resources, generates wastes, and releases potentially hazardous emissions into atmosphere affecting the environment, social economic and technological aspects throughout their whole life cycle. Carrying out necessitates assessment of the degree of sustainability of building life cycle, in order to facilitate developer to make right decision in improvement of built environment. In terms of the built environment, “life cycle” refers to a product, building or service over the course of its whole life. For instance, relation to a building, this would include its design, construction, operation and maintenance, and retirement.

Development specialists agree that promoting built environment in the world has the potential to reduce resources consumption and make polluted cities more habitable while partially mitigating the impacts of global warming. Therefore, studying Singapore rail network life cycle is attempting to improve its operational feature that is related to how a rail network was designed, constructed, operated and maintained, and retired properly.

The defined objectives for this research are involving: (1) To determine how does the Analytic Hierarchy Process (AHP) models can assist us to analysis the life cycle of Singapore’s rail network – Thomson East Coast Line (TEL); (2) Implement AHP technique to identify the core factors which influence the sustainability of the TEL and observe their trend; (3) Through the AHP measurements to determine most sustainable methods to be carried out during each stage of the TEL life cycle.

Limitations of this study include the scope of study which is only limited to Singapore MRT line of TEL and hence it could not be extended to all rail network projects. Secondly, during the AHP model creation, the intensity of importance inputs of each factor are only subjected to opinions of certain number of experts or historical data. Lastly, the lack of quantitative data due to the sensitivity of such information makes it hard to quantify the trade-off between sustainability and cost.