Multi-objective optimization of building envelope design for life cycle environmental performance

被引:152
作者
Azari, Rahman [1 ]
Garshasbi, Samira [2 ]
Amini, Pegah [1 ]
Rashed-Ali, Hazem [1 ]
Mohammadi, Yousef [3 ]
机构
[1] Univ Texas San Antonio, Coll Architecture Construct & Planning, San Antonio, TX USA
[2] Islamic Azad Univ, Cent Tehran Branch, Young Researchers Club, POB 13185-768, Tehran, Iran
[3] Natl Petrochem Co, Petrochem Res & Technol Co, POB 14358-84711, Tehran, Iran
关键词
Life cycle assessment (LCA); Building envelope; Optimization; Genetic algorithm; WALL SYSTEMS; ENERGY;
D O I
10.1016/j.enbuild.2016.05.054
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The building envelope incorporates significant amount of construction materials and is a key determinant of the embodied energy and environmental impacts in buildings. It is also a mediator between indoor and outdoor environmental conditions and has significant impacts on operational energy use in many types of buildings. The present article utilizes a multi-objective optimization algorithm to explore optimum building envelope design with respect to energy use and life cycle contribution to the impacts on the environment in a low-rise office building in Seattle, Washington. Design inputs of interest include insulation material, window type, window frame material, wall thermal resistance, and south and north window-to-wall ratios (WWR). The simulation tool eQuest 3.65 is used to assess the operational energy use, while Life Cycle Assessment (LCA) methodology and Athena IE are used to estimate the environmental impacts. Also, a hybrid artificial neural network and genetic algorithm approach is used as the optimization technique. The environmental impact categories of interest within the LCA include: global warming, acidification, eutrophication, smog formation, and ozone depletion. The results reveal that the optimum design scenario incorporates fiberglass-framed triple-glazed window, about 60% south WWR, 10% north WWR, and R-17 insulation. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:524 / 534
页数:11
相关论文
共 33 条
[1]  
[Anonymous], 2002, Evolutionary algorithms for solving multi-objective problems
[2]  
[Anonymous], 2002, INT J LIFE CYCLE ASS, DOI DOI 10.1007/BF02978899
[3]  
[Anonymous], 2005, INTRO NEURAL NETWORK
[4]  
[Anonymous], 2006, ENV MAN LIF CYCL ASS, V2nd
[5]  
[Anonymous], 2015, MONTHL EN REV
[6]  
[Anonymous], 2006, 14044 ISO
[7]  
[Anonymous], 2001, Neural Networks: A Comprehensive Foundation
[8]  
[Anonymous], **DROPPED REF**, DOI DOI 10.1061/(ASCE)1076-0342(2003)9:4(157)
[9]  
[Anonymous], 2004, Wiley InterScience electronic collection.
[10]  
[Anonymous], 2011, 15978 EN EUR COMM ST