A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Minimizing Upgrade Cost to Achieve LEED Certification for Existing Buildings
AbstractAging buildings represent a significant percentage of existing buildings and are often in urgent need of upgrading to improve their operational, economic, and environmental performance. The owners of these buildings often seek to identify and implement building upgrade measures that are capable of improving building sustainability as well as achieving certification under various green building rating systems such as the Leadership in Energy and Environmental Design (LEED). In order to support decision-makers in promoting building sustainability and achieving LEED certification for existing building, this paper presents the development of a novel optimization model that is capable of minimizing the required upgrade cost of achieving a desired LEED certification level such as Silver or Gold. The model is designed to identify cost-effective building measures, plans, and/or performance to achieve a specified LEED certification for existing buildings while keeping required upgrade cost to a minimum. The optimization model is implemented using a genetic algorithm (GA) due to its capabilities of identifying optimal solutions for this type of problem in a reasonable computational time, and efficiently and accurately modeling this specific optimization problem with the least number of decision variables and constraints. A project case study of a rest-area building is used to illustrate the optimization model and to demonstrate its novel and unique capabilities. The primary contribution this research makes to the body of knowledge is its new methodology for optimizing the selection of building upgrade measures to achieve LEED certification for existing building with minimum upgrade cost.
Minimizing Upgrade Cost to Achieve LEED Certification for Existing Buildings
AbstractAging buildings represent a significant percentage of existing buildings and are often in urgent need of upgrading to improve their operational, economic, and environmental performance. The owners of these buildings often seek to identify and implement building upgrade measures that are capable of improving building sustainability as well as achieving certification under various green building rating systems such as the Leadership in Energy and Environmental Design (LEED). In order to support decision-makers in promoting building sustainability and achieving LEED certification for existing building, this paper presents the development of a novel optimization model that is capable of minimizing the required upgrade cost of achieving a desired LEED certification level such as Silver or Gold. The model is designed to identify cost-effective building measures, plans, and/or performance to achieve a specified LEED certification for existing buildings while keeping required upgrade cost to a minimum. The optimization model is implemented using a genetic algorithm (GA) due to its capabilities of identifying optimal solutions for this type of problem in a reasonable computational time, and efficiently and accurately modeling this specific optimization problem with the least number of decision variables and constraints. A project case study of a rest-area building is used to illustrate the optimization model and to demonstrate its novel and unique capabilities. The primary contribution this research makes to the body of knowledge is its new methodology for optimizing the selection of building upgrade measures to achieve LEED certification for existing building with minimum upgrade cost.
Minimizing Upgrade Cost to Achieve LEED Certification for Existing Buildings
Abdallah, Moatassem (author) / Liu, Liang / El-Rayes, Khaled
2016
Article (Journal)
English
| British Library Conference Proceedings | 2004
Go Ahead, Take the Leed - Contractors help achieve certification
Online Contents | 2005
LEED releases an update for existing buildings
British Library Online Contents | 2018