Development of an Automated Tool for Buildings’ Sustainability Assessment in Early Design Stage

Kumanayake, Ramya; Luo, Hanbin

doi:10.1016/j.proeng.2017.08.023

Cited by 10 publications

(11 citation statements)

References 28 publications

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“…Therefore, there are proposals regarding how to automate the exchange process [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Two studies established a fully automatic exchange of data between BIM and LCA [36,37]. Abanda et al [36] combined a BIM tool with the .NET framework environment to import the information to Excel.…”

Section: Classification Of the Integration Process/interoperabilitymentioning

confidence: 99%

“…Abanda et al [36] combined a BIM tool with the .NET framework environment to import the information to Excel. Kumanayake and Luo [37] established an automatic information exchange from BIM to Excel by combining Visual Studio C# object-oriented programming language with the .NET framework, integrated with a structured query language containing information from the Bath inventory of carbon and energy (ICE) database. On the other hand, several studies have tried to facilitate the integration of BIM and LCA at least to some degree.…”

Section: Classification Of the Integration Process/interoperabilitymentioning

confidence: 99%

“…could be used for this purpose. Another issue observed is that the databases mostly originate, and contain data, from developed countries [37]. This is not directly connected to the integration process of BIM and LCA, but the emerging countries are those where much construction work is performed, and the development of accurate databases could help in realistically assessing the associated environmental impacts.…”

Section: Informational Issuesmentioning

confidence: 99%

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BIM and LCA Integration: A Systematic Literature Review

et al. 2020

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To foster sustainable development, the environmental impacts of the construction sector need to be reduced substantially. Life cycle assessment (LCA) is the established methodology for the quantification of environmental impacts, and therefore has been increasingly applied to assess the environmental performance of buildings. By coupling LCAs with digital design tools, e.g., building information modeling (BIM), the identification of environmental hotspots and their mitigation is possible during the design process. The objective of the study is to identify the current integration approaches, and determine the pros and cons of the integration process from different viewpoints, namely, technical, informational, organizational and functional issues. Therefore, a comprehensive systematic literature review (SLR) was performed. We identified 60 relevant BIM-LCA case studies and analyzed the applied BIM-LCA workflows in detail. A total of 16 of the reviewed studies applied LCA during the early design stage. These studies used a manual or semiautomatic data exchange between the BIM models and LCA tools. In most cases, contemporary BIM-LCA workflows utilized conventional spreadsheets (e.g., Excel sheets in 16 cases). However, the analysis shows that an automated link between LCA and BIM can be achieved when overcoming the technical, organizational and informational issues discussed in the paper. This could enable the streamlining of LCA applications in design practice, and thus support the necessary improvements in the environmental performance of buildings.

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Section: Classification Of the Integration Process/interoperabilitymentioning

confidence: 99%

Section: Classification Of the Integration Process/interoperabilitymentioning

confidence: 99%

Section: Informational Issuesmentioning

confidence: 99%

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BIM and LCA Integration: A Systematic Literature Review

et al. 2020

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“…It also quantifies the embodied carbon of building materials (AlHashmi et al, 2017). The carbon data are obtained from the Inventory of Carbon Energy (ICE) database developed by the University of Bath (DesignBuilder, 2019b;Kumanayake and Luo, 2017). The EICAM outputs play a vital role in predicting the environmental impact costs, which are calculated as follows:…”

Section: Data Sets Developmentmentioning

confidence: 99%

Environmental impacts cost assessment model of residential building using an artificial neural network

Hamida

Al‐Sudairi

Alshaibani

et al. 2020

ECAM

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PurposeBuildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.Design/methodology/approachAn Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.FindingsThe MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.Research limitations/implicationsThis study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.Originality/valueBy providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.

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“…Reuse of previously built or contaminated areas can be defined as an extent of utilization of land that is contaminated or was previously occupied [ 21 , 28 ]. This sub-indicator is beneficial to the environment as urban sprawl is restrained and greenfield sites are preserved.…”

Section: Env: Environmental Factormentioning

confidence: 99%

Indicator rating methodology for Rapid Sustainability Assessment Method (RSAM) for existing residential buildings using opinions of residents

2020

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The presented method provides the details for how each indicator in “RSAM: A Rapid Sustainability Assessment Method” is scored using a residents’ opinions-based sustainability assessment procedure that is specifically designed for the assessment of existing residential buildings. Existing methods in the literature mostly suggest indicator scores in construction sustainability assessments using highly technical data and require a high level of engineering and management expertise. This makes them elaborate but labor-, time-, and cost-intensive; and thus, their use is more frequently favored in new projects. The presented method redefines a set of conventional environmental, social-functional, and economic indicators in a context which combines data from residents’ opinions (e.g. perceived quality of indoor temperature comfort level) and easy-to-obtain information (e.g. annual utility bills). All indicator ratings are represented within a 10-point scale, and their weights on the overall model are identified by stakeholders. RSAM provides a rapid and low-cost assessment for existing buildings which normally may not go through a conventional sustainability assessment due to resource limitations.

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Development of an Automated Tool for Buildings’ Sustainability Assessment in Early Design Stage

Cited by 10 publications

References 28 publications

BIM and LCA Integration: A Systematic Literature Review

BIM and LCA Integration: A Systematic Literature Review

Environmental impacts cost assessment model of residential building using an artificial neural network

Indicator rating methodology for Rapid Sustainability Assessment Method (RSAM) for existing residential buildings using opinions of residents

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