A review and outlook for integrated BIM application in green building assessment

Sustainable Cities and Society 48 (2019) 101576

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Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs

A review and outlook for integrated BIM application in green building assessment ⁎

T

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Mark Kyeredey Ansah, Xi Chen , Hongxing Yang , Lin Lu, Patrick T.I. Lam Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Hong Kong, China

A R T I C LE I N FO

A B S T R A C T

Keywords: GBAS BIM Integration Data exchange Green building assessment criteria

In the past decade, there has been a momentous increase in the use of Green Building Assessment Schemes (GBAS). Also, the contribution of BIM (Building Information Modelling) in evaluating GBAS criteria has been extensively recognized and is driving research on utilizing BIM to obtain GBAS credits. Although many green BIM studies have been published, there is a lack of a comprehensive and in-depth overview to synthesize the integration of BIM with GBAS. To fill this research gap, this paper presents a thorough and systematic review on the breadth of green building evaluation matrixes achievable with BIM. Great opportunities are identified in parametric analyses and optimizations of a holistic sustainable design approach by integrating GBAS with BIM. In addition, the study summarizes the development of comprehensive databases, data-rich models, data exchange protocols, assessment modules and output presentations. The results indicate urgent research needs in the following directions: automating the development of a database of manufacturer-certified elements; comprehensiveness of data exchange protocols; practical demonstration and detailed reporting of unaddressed assessment criteria in GBAS; exploration of cloud-based BIM and GBAS.

1. Introduction In the last decade, interactions between concerns for the environment, increased demand for buildings and higher quality indoor environment has influenced the way buildings are designed and constructed. In response to this trend, Green Building Assessment Schemes (GBAS) have been developed and practiced globally. GBAS such as LEED, BREEAM, BEAM Plus and CASBEE are established as comprehensive measures of building sustainability levels from the concept design to the operation, renovation and demolition of buildings. Thus, the benefits of GBAS extend beyond certification and the criteria of GBAS can provide building practitioners with a reasonable forecast of a building’s performance (Maltese et al., 2017). However, green building practitioners are confronted with a major challenge in satisfying these criteria at the building design stage (Zuo & Zhao, 2014), when assessments are usually less accurate, inconsistent and resource demanding with respect to time and costs. To facilitate green building designs, Building Information Modelling (BIM) emerged as a technology and process allowing 3D modelling and information management throughout the life cycle of buildings (Lu, Wu, Chang, & Li, 2017). It is widely recognized that, BIM is a data-rich, intelligent and object-oriented parametric building modelling tool (Gao, Koch, & Wu, 2019). Various categories of information can be fed ⁎

into a 3D model and managed to suit the need of users. In recent years, innovative development of BIM has provided opportunities to support green building practices and is classified as Green BIM. A core definition of green BIM is “a model-based process of generating and managing coordinated and consistent building data that facilitates the accomplishment of established sustainability goals” (Wong & Zhou, 2015). By this definition, green BIM facilitates various performance analysis and evaluations such as acoustic analysis, carbon emission, construction and demolition waste management, lighting analysis, operational energy use and water use. Since BIM contains multidisciplinary data for various analysis, it is indicated that sustainability metrics can be superimposed on a 3D model to support the evaluation of credits in GBAS (Maltese et al., 2017). Storing and managing GBAS rating data within BIM tools can then help to make useful design decisions with real project data at the early design stage of a project. Over the last two decades, a large amount of academic research related to green BIM has been published. Gao et al. (2019) presented a review on BIM-based Building Energy Modelling (BEM) for the development of energy efficient building designs. Pezeshki, Soleimani and Darabi (2019) for instance, presented a valuable review on green BIM literature between 2015 and 2018 with a focus on the use of BIM database in BEM. Sanhudo et al. (2018) presented a review on the technological capability of BIM for energy retrofitting. Kamel and Memari

Corresponding authors. E-mail addresses: [email protected], [email protected] (X. Chen), [email protected] (H. Yang).

https://doi.org/10.1016/j.scs.2019.101576 Received 11 February 2019; Received in revised form 24 April 2019; Accepted 26 April 2019 Available online 02 May 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.

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M.K. Ansah, et al.

Nomenclature

GBI gbXML IES IES-VE IEQ IFC LEED LEED US

Abbreviations API Application Programming Interface BCA Building and Construction Authority BIM Building Information Modelling BEAM Plus Building Environmental Assessment Method Plus BREEAM Building Research Establishment Environmental Assessment Method CASBEE Comprehensive Assessment System for Built Environment Efficiency COBie Construction Operations Building Information Exchange CUI Concrete User Index GBAS Green Building Assessment Schemes GBAT Green Building Assessment Tool

ODBC NR NS PDF USGBC XML

Green Building Index Green Building XML schema Integrated Environmental Solutions Integrated Environmental Solutions, Virtual Environment Indoor Environment Quality Industry Foundation Classes Leadership in Energy and Environmental Design Leadership in Energy and Environmental Design, United States Open Database Connectivity Not Reported Non-specific Portable Document Format United States Green Building Council Extensible markup language

in evaluating GBAS criteria. The study claimed that the public transportation access, rapidly renewable materials, and material reuse criteria were not achievable with BIM, whereas these criteria have been successfully evaluated with BIM (Chen & Nguyen, 2017; Ilhan & Yaman, 2016). Given the above introduction and literature review, there is clearly the lack of a systematic review on the application of BIM in evaluating GBAS criteria. A number of unaddressed research questions include: (1) the breadth of assessment criteria that can be achieved from the extensive use of BIM and (2) the development of practical BIM tools by software vendors for evaluating GBAS criteria. This study therefore carefully examines BIM and GBAS-related literature to address the identified research questions. The present study provides a synthesized analysis on the integration of BIM with GBAS from the perspective of both academic research and commercial tools. The rest of this paper is

(2019) reviewed the challenges and solutions to the interoperability between BIM and BEM modelling processes. Wong and Zhou also Wong and Zhou (2015) presented a review of green BIM literature from the perspective of building life cycle. Even though above-mentioned studies have made remarkable contributions to mapping green BIM, none of them addressed technical studies on the use of BIM for evaluating GBAS criteria and obtaining corresponding credits. Lu et al. (2017) presented a comprehensive review addressing the application of BIM throughout building project’s life cycle and various functions of BIM for sustainable analyses. However, their review paid less attention to integrating BIM with the green building assessment (GBAS) and failed to address the breadth of criteria evaluation achievable with BIM. Furthermore, none of these studies considered commercial applications for evaluating GBAS criteria and corresponding credits. Other studies such as those conducted by Azhar et al. (2011) have addressed the application of BIM

Fig. 1. Scope of review for BIM/GBAS integration. 2

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M.K. Ansah, et al.

undertaken. The research process is depicted in Fig. 2.

organized in the following manner. Section 2 presents the research methodology. Section 3 addresses the breadth of assessment achievable for different GBAS. Section 4 addresses the application of software tools in evaluating GBAS criteria. Section 5 presents the database infrastructure. Section 6 addresses the data exchange modules. Section 7 addresses the criteria assessment modules. Section 8 presents the identified research gaps and recommendations. Finally, section 9 concludes the study and provides an outlook. The entire scope of the review is summarized in Fig. 1.

2.1.1. Stage one: preliminary search The initial search for literature was conducted using Scopus, which covers an extensive range of academic publications (Oppong, Chan, & Dansoh, 2017; Owusu, Chan, & Shan, 2019). Scopus has a higher accuracy and a faster indexing process than Web of Science, so that it is most likely to archive recent publications (Meho & Rogers, 2008). In addition, Scopus has been widely used in similar reviews such as (Bradley, Li, Lark, & Dunn, 2016; Wong & Zhou, 2015). A comprehensive search was done under the title/abstract/keyword field of Scopus with keywords of “Building information modelling” or “Building information modeling” or “building information model” or “virtual design and construction” or “as-built model”, which are limited by any of the following terminologies: “LEED”, “Leadership in Energy and Environmental Design”, “BEAM Plus”, “Building Environmental Assessment Method”, “BREEAM”, “Building Research Establishment Environmental Assessment Method”, “CASBEE”, “Comprehensive Assessment System for Built Environment Efficiency”, “Green Star”, “Green Mark”, “Green Building Index”, “GBI”, “Green globes”, “SBtool”, “Green Building Labelling-Assessment Standard for Green Building”, “GBL_ASGB”, “HK BEAM”, “HK-BEAM”. To avoid the omission of any relevant paper, the date range was set to “all years till present”. Also, the query was not limited to specific journals as this may limit the number of identified publications. The document type was also set to “article” or “review” as they represent the most reputable and influential sources of knowledge (Ramos-Rodríguez & Ruíz-Navarro, 2004; Santos, Costa, & Grilo, 2017). The terminologies specified in the search were influenced by a prior search with generic terms. It was noticed that the title/abstract/keyword of some publications contained specific names of GBAS rather than generic terms such as “green building assessment scheme” or “green building rating systems”. To retrieve such

2. Methodology The concept of green BIM has been previously mapped by other studies such as (Eleftheriadis, Mumovic, & Greening, 2017; Lu et al., 2017; Wong & Zhou, 2015). These are valuable inputs for understanding green BIM and provide a good foundation to further research. This study defines its scope within technological and system extensions employed to facilitate automated evaluations of green building assessment criteria within a BIM environment. Therefore, the breadth and depth of incorporating assessment criteria into the BIM model and advances in supportive techniques are the focuses of this study. The methodology part is divided into two main aspects: research publications and BIM software, as detailed in the following subsections. 2.1. Selection of academic output To identify and examine extensive literature within the established scope, the methods used by (He et al., 2017; Lu et al., 2017) were adapted for this study. This included a preliminary literature search with different databases, a filtration process and a content analysis. Thus, a three-stage selection of academic journal papers, selection of closely related publications and systematic content analysis was

Fig. 2. Research Methodology. 3

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Lu et al., 2017; Pezeshki et al., 2019) were also beyond the focus. However, these papers are mentioned when necessary. The number of selected publications and key papers are shown in Table 1.

publications, it was relevant to specify these terminologies. Moreover, more than four of the GBAS selected for the search are commonly practiced globally and appear frequently in highly cited green building assessment schemes and green BIM literature (Chen, Yang, & Lu, 2015; Lu et al., 2017). A total of 85 publications were retrieved from this search query. Given the limited number of publications retrieved from Scopus, Web of Science, ScienceDirect, ProQuest and Google Scholar were selected for a further search, to ensure that an adequate number of research outputs on the integration of BIM with GBAS were retrieved for the review. After removing duplicates, a total of 92 publications including journal and conference articles were retrieved. A preliminary screening was conducted to remove publications that covered subjects not related to construction or just happened to contain some of the search keywords within their title/abstract/keywords section. The results of this exercise revealed that Automation in Construction, Sustainable Cities and Society, Building and Environment, Building Simulation, Engineering Construction and Architectural Management, Journal of Civil Engineering and Management, Journal of Cleaner Production and Journal of Management in Engineering have three or more papers each. These eight journals are included in the Science Citation Index Expanded database. The total number of publications after this stage was 43 from 22 journals and conference proceedings.

2.1.3. Stage 3: Content analysis of selected publications In this study, a scope of green BIM triangle taxonomy, namely “BIM supported analysis and assessment of green projects” developed in (Lu et al., 2017) is further extended. Consequently, this review adapted the methodology in (Lu et al., 2017) to review the key papers identified. Step one: The first step was to specify the domain of objects to be classified. In order to provide a relevant outlook on the research outputs, it is important to classify the contents of these publications. However, the domain, representing the limits/area of objects should be clearly indicated. As shown in Section 2, the domain of objects for this present study is the key papers identified. Step two: The second step was to define and measure essential properties. The selection of essential properties for this study was based on the keywords, themes and major components of the frameworks underlying the relevant studies. Four essential properties were identified and classified under themes and frameworks which include integration module, data exchange protocol, BIM tools and databases. The major properties that evolved from keywords are green building assessment schemes and various criteria addressed. Step three: The main task for this stage was to evaluate the differences and similarities for the relevant papers in order to permit classification and assignment of their outputs based on a series of essential properties. For instance, various databases or integration and assessment modules were identified. Step four: The final step was to assess the point of convergence and divergence in the reviewed papers. Thus, after identifying the essential groups, this study critically reviewed the output of papers to synthesize relevant contributions to the current body of knowledge and identify research gaps in automated evaluation of GBAS with BIM. As indicated in Step 3, the present study synthesizes the outputs of relevant studies based on the essential properties identified within relevant papers. It is possible that not all papers contain the essential properties identified. Tables 3 and 4 provide a summary of essential properties extracted from the relevant publications.

2.1.2. Stage two: key paper selection After completing stage one, a more critical and comprehensive examination of the 43 publications was carried out to identify those papers that are closely relevant to exploring the identified research topic. The selection criteria lie in the research objectives, methods and the major findings of these publications. The main criteria for the selection process was the technical development of BIM to evaluate various criteria of green building assessments and award of corresponding credits. In this regard, the study is inclined to examining issues such as storing, recognising, capturing, processing data related to the evaluation of GBAS criteria within the BIM environment. Therefore, managerial issues, such as the adoption and implementation addressed in (He et al., 2017) are not the focus of this paper. Moreover, building energy modelling such as energy performance analysis, carbon emissions analysis and evaluations in (Gao et al., 2019; Kamel & Memari, 2019; Table 1 Overview of selected publication and publications relevant to the study. Journal

No. of selected publications

No. of key papers for in-depth analysis

ARPN Journal of Engineering and Applied Sciences Automation in Construction Building and Environment Building Simulation Built Environment Project and Asset Management Congress on Computing in Civil Engineering Proceedings Construction Innovation Electronic Journal of Information Technology in Construction Energy Procedia Engineering Construction and Architectural Management Ework And Ebusiness In Architecture, Engineering and Construction, Proceedings of the 11th European Conference on Product and Process Modelling ECPPM 2016 International Journal of Architectural Computing Journal of Architectural Engineering Journal of Civil Engineering and Management Journal of Cleaner Production Journal of Construction Engineering Journal of Management in Engineering Open Construction and Building Technology Journal Proceedings of the 2009 ASCE International Workshop on Computing in Civil Engineering Proceedings of the 19th International Conference on Computer-Aided Architectural Design Research in Asia Proceedings of the AEI Conference 2015 Sustainable Cities and Society Total

1 6 3 3 1 1 1 1 1 3 1

1 5 0 1 0 1 1 1 0 0 1

2 1 3 3 1 3 1 1 1 1 4 43

1 1 0 0 1 0 1 1 1 1 1 19

4

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LEED criteria attainable with BIM are also demonstrated in (Akcay & Arditi, 2017; Alwan et al., 2015; Azhar et al., 2011; Barnes & CastroLacouture, 2009; Bergonzoni et al., 2016; Chen & Nguyen, 2017; Jalaei & Jrade, 2014, 2015; Jrade & Jalaei, 2013; Nguyen, Shehab, & Gao, 2010) and summarised in Table 5. Unlike quantitative criteria, qualitative criteria such as “Innovation” may require subjective judgement of an assessor. Accordingly, they are more difficult to be incorporated into the BIM environment. Nevertheless, external applications as a part of multi-faceted cloud BIM can be explored to incorporate, and process qualitative data requirements in green building assessments. A survey of the official webpage of USGBC did not indicate any direct association of the LEED assessment with BIM. However, a few records of BIM to satisfy “Innovation” criteria were identified. Also, LEED online and LEED automation were identified to facilitate connections with USGBC’s partners such as IES and GreenWizard for LEED assessment and documentations. While Azhar et al. (2011) indicated that the enhanced commissioning criterion of LEED cannot be assessed using BIM, recent development in BIM tools such as the ERGON module of IES-VE can model real operational data. Such data driven modelling may help achieve evaluations of LEED monitoring-based building commissioning and BREEAM “Energy Prediction and Verification”. These are valuable opportunities to explore new dimensions of BIMbased GBAS.

2.2. Selection of BIM tools Besides the relevant literatures identified, it was necessary to address the developments by software vendors to present a holistic overview of evaluating GBAS criteria with BIM. Twelve popular BIM tools identified by (Lu et al., 2017) for common usage in green BIM are included in this review. In addition, other relevant tools identified from a search on evaluating GBAS criteria were also included. Table 2 presents a summary of the selected tools. The second column indicates criteria which can be evaluated and awarded credits in the BIM tool. “X” in the third column means the tool is not directly applicable for evaluating and awarding credits but may support the process. In the following sections, details of the development of BIM for green building assessments are presented based on the essential properties identified in Tables 3 and 4. 3. Breadth of green building assessment achievable with BIM In this section, five GBASs integrated with BIM are presented because they appeared in key literatures. The following subsections delineate the breadth of assessment accomplished with BIM and identify opportunities for further studies. 3.1. Leadership in energy and environmental design (LEED)

3.2. Building research establishment environmental assessment method (BREEAM)

Over 70% of the relevant publications demonstrate various evaluation procedures for LEED criteria (Akcay & Arditi, 2017; Alwan, Greenwood, & Gledson, 2015; Azhar, Carlton, Olsen, & Ahmad, 2011; Barnes & Castro-Lacouture, 2009; Bergonzoni, Capelli, Drudi, Viani, & Conserva, 2016; Biswas & Krishnamurti, 2012; Chen & Nguyen, 2017; Jalaei & Jrade, 2014; Jrade & Jalaei, 2013; Wu & Issa, 2011; Zhang & Chen, 2015). The high occurrence of LEED may be associated with its applications in over 165 territories around the world (US Green Building Council, 2018). LEED assessment criteria have been categorised as quantitative and qualitative. Quantitative criteria refer to those with numeric values such as annual energy and water consumption while qualitative criteria are those requiring subjective judgement such as innovations in the building design (Biswas & Krishnamurti, 2012). Azhar et al. (2011) discovered that 17 quantitative criteria and 2 prerequisites forming a total of 38 points can be achieved and documented with BIM. Assessment criteria in the public transportation access, development density, community connectivity and indoor air quality performance have recently been achievable with BIM as demonstrated in (Bergonzoni et al., 2016; Chen & Nguyen, 2017). Other

BREEAM classifies assessment criteria into different categories and summarizes total achieved points to determine final grade of building (Chen et al., 2015). Only one study has been conducted by Ilhan and Yaman (2016), which proposed and validated a novel framework for the material category of BREEAM. Although BREEAM shares similar assessment criteria with other GBAS such as LEED, the requirements to attain credits for these criteria may vary. There is a need to conduct further studies as many BREEAM credits remain unexplored. 3.3. Building environmental assessment method plus (BEAM plus) BEAM Plus provides four folds of assessments and a unique grading system as depicted in (Chen et al., 2015; The Hong Kong Green Building Council, 2018). Like BREEAM, only one study was identified to have evaluated BEAM Plus criteria in a BIM software (Wong & Kuan, 2014). The study reported that twenty-six criteria of BEAM Plus can be attained through documentations produced by BIM. Out of these, fifteen

Table 2 Selected BIM tools and functions for evaluating GBAS criteria. Software Tool AECOsim ArchiCAD Autodesk Green Building Studio Autodesk Revit (Light Analysis Revit) Bentley Hevacomp DesignBuilder Simulation DOE2 eQUEST EnergyPlus FloVENT HEED Integrated Environmental Solutions® (Virtual Environment Navigator) Navisworks ODEON Room Acoustics Software One Click LCA

Applicable criteria

Not directly applicable x x x

LEED (IEQc8.1 2009) and LEED v4 (EQc7 opt2) x x x x x x x LEED (Thermal comfort, daylight and quality views of indoor environment quality), BREEAM (management, health and wellbeing and energy credits) x x LEED V4 (Building Life Cycle Impact Reduction of (MRc1)) and BREEAM (Life Cycle Impact of (Mat 1))

TRNSYS

x

5

Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture ArchiCAD

(Akcay & Arditi, 2017)

6

Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture Autodesk Revit Architecture

(Jalaei & Jrade, 2015)

Note: NR - Not Reported.

(Zhang & Chen, 2015)

(Wu & Issa, 2012)

(Wu & Issa, 2011)

(Wong & Kuan, 2014)

(Raffee et al., 2016)

(Nguyen et al., 2016)

(Nguyen et al., 2010)

(Jrade & Jalaei, 2013)

Autodesk Revit Architecture

(Jalaei & Jrade, 2014)

(Ilhan & Yaman, 2016)

(Chen & Nguyen, 2017)

(Chandra & Zhou, 2014)

(Biswas & Krishnamurti, 2012)

(Barnes & Castro-Lacouture, 2009) (Bergonzoni et al., 2016)

(Azhar et al., 2011)

(Alwan et al., 2015)

BIM Tools

Authors

Table 3 Summary of established BIM frameworks.

Revit API interface

LoraxPRO, Bentley AECOsim Energy Simulator

Integrated Environmental Solutions (IES)

Sustainable Assessment Building Information Green Building Index (SABIG) NR

LEED Evaluator

Microsoft Access

Microsoft Excel

Autodesk Ecotect Analysis/ Integrated Environmental Solutions (IES-VE), Microsoft Excel Microsoft Access

Green building assessment tool

Google Maps Web Map Service

CUI add-on tool

NR

NR

Microsoft Excel

Augmented and Functional databases Augmented and External databases

Augmented database

Augmented and External databases Augmented database

Augmented database

Augmented database

External database

External database

External database (Web Map Service) External database (Green materials library) External database

NR

Functional database

NR Augmented database

External database (RSMeans) NR

Database

Integrated Environmental Solutions-Virtual Environment (IES-VE), Project Vasari Integrated Environmental Solutions-Virtual Environment (IES-VE) Integrated Optimization Tool

Safaira/Excel

BIM-Based performance analysis and Auxiliary tools

Revit Application Programming Interface (API).

LEED Application Programming Interface (API)/ Open Database Connectivity (ODBC) and IFC Cloud-based approach (LEED Online)

NR

IFC

Revit Application Programming Interface (API).

Revit Application Programming Interface (API).

LEED-RS

Revit Application Programming Interface (API).

EcoScorecard plugin

IFC to green building rating tool

Revit Application Programming Interface (API).

Revit Application Programming Interface (API).

COBie/IFC model

Dynamo visual scripting tool

NR

gbXML

gbXML

Microsoft Excel Macro

Integration or data exchange model

API Interface developed with sub-interfaces

PDF LEED Online template

NR

Sustainable Assessment Building Information Green Building Index (SABIG) Schedules

LEED Evaluator

API interface

Schedules

Microsoft Access

EcoScorecard plugin interface

Spreadsheets

Microsoft Excel Sheets and Map Images

XML format Report

LEED submittals in XML format

Microsoft Excel Report

Integrated Environmental Solutions (IES) Report NR

NR

Microsoft Excel Report

Output

M.K. Ansah, et al.

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Table 4 Investigated green building assessment schemes and categories. Authors

GBAS

Category

Validation

(Akcay & Arditi, 2017)

LEED

Energy and Atmosphere

(Alwan et al., 2015)

LEED

(Azhar et al., 2011)

LEED

(Barnes & Castro-Lacouture, 2009) (Bergonzoni et al., 2016)

LEED

Sustainable sites, Energy & Atmosphere, Materials and Resources and Indoor Environment Quality Energy and Atmosphere, Water Efficiency, and Indoor Environmental Quality Sustainable Sites and Materials & Resource

Case study on Office building, Chicago Midway, Illinois Sample Revit model of Museum of Architecture, Doha. Case study on Perdue School of Business, Salisbury University Sample Revit project

LEED, Italy

Indoor Environment Quality

(Biswas & Krishnamurti, 2012)

LEED

Sustainable Sites

(Chandra & Zhou, 2014)

Green Mark Singapore LEED

Materials Category

(Chen & Nguyen, 2017) (Ilhan & Yaman, 2016) (Jalaei & Jrade, 2014)

BREEAM UK LEED, Canada

(Jalaei & Jrade, 2015) (Jrade & Jalaei, 2013)

LEED, Canada LEED, Canada

(Nguyen et al., 2010) (Nguyen et al., 2016) (Raffee et al., 2016) (Wong & Kuan, 2014)

LEED LEED GBI BEAM Plus, Hong Kong LEED LEED LEED

(Wu & Issa, 2011) (Wu & Issa, 2012) (Zhang & Chen, 2015)

Sample case study; Jewellery Manufacturing Plant Case study on LEED NC 2.1 silver-certified building Sample project

Sustainable Sites

LEED Gold certified wafer factory project (based on LEED v3) Sample project

Materials Category Energy and Atmosphere, Materials and Resources and Indoor Environment Quality Energy and Atmosphere and Materials and Resources Sustainable Sites, Energy and Atmosphere, Materials and Resources, Indoor Environment Quality, Innovation in design and Regional Priority NS Sustainable Site NR Site Aspects, Materials Aspects, Energy Use and Water Use

Sample project Sample residential building Six floor apartment building at the design stage in the city of Ottawa. Sample 16 storey hotel project Sample 4 storey project NR Sample project

NS Energy and Atmosphere NS

NR Sample project NR

Note: NR - Not Reported, NS – Non-specific.

3.6. Status of BIM application in green building assessment

can be attained through scheduling with the BIM software, while eleven others require simulations with a BIM-based performance assessment tool. Unfortunately, the criteria evaluated so far have been limited to the case study presented as part of the work. The study focused on evaluating “Materials Aspects” of BEAM Plus and payed little attention to the other criteria.

Table 5 provides a summary of all GBAS criteria which can be achieved with BIM as demonstrated in the reviewed studies. In general, many of the identified studies have been limited to LEED. While it may be possible to evaluate many criteria in a BIM software, many of the methods used focus on individual projects and fail to produce a replicable approach. Furthermore, the comprehensiveness of these evaluations remains questionable as many details on incorporating assessment criteria within BIM tools are not reported. Also, some studies indicate the potential for evaluating criteria from a BIM model without substantial practical demonstration. With regards to criteria which were previously reported as not achievable, recent improvement to BIM applications shows that opportunities do exist to address criteria such as building commissioning, parking capacity, materials reuse and waste management. There is therefore a need to research improvement to methods of evaluation and secondly evaluate unaddressed criteria.

3.4. Green building index (GBI) GBI has different folds of assessments for seven building and neighbourhood developments (Green Building Index Organisation, 2018; Green Building Index Organization, 2018). Despite growth in its application, studies integrating GBI with BIM are scarce. Raffee, Karim, & Hassan (2016) proposed a BIM-based method to evaluate GBI criteria. However, the study failed to provide a replicable approach for assessments, because of the lack of focus on specific criteria and practical demonstration.

4. Application of software tools in evaluating assessment criteria 3.5. Green mark In this study, tools identified are categorized as BIM modelling tools, BIM-based performance assessment tools and auxiliary tools. The focus of this section is to provide an up-to-date synthesis on the capacities of the tools to support GBAS criteria evaluation. First, the application of these software in the relevant literature is presented, followed by the practical developments by software vendors to evaluate different criteria of GBAS. Autodesk Revit Architecture and ArchiCAD were the two main authoring tools identified for this study. Both tools are built on a parametric modelling technology which allows users to create designs from a combination of graphical and non-graphical data (Nguyen et al., 2010). 3D BIM models can be developed based on real construction elements such as walls, columns, beams, floors and windows from

Building and Construction Authority (BCA) of Singapore provides twenty-four unique Green Mark Schemes addressing different building typologies (Green Building Index Organisation, 2018). Recently Liu, Chen, Peh, and Tan (2017) explored the potential of applying BIM technology to aid the certification process of Green Mark for Non-Residential buildings. The authors indicated that 31 Green Mark assessment criteria could be attained through a combined input of the BIM software (Revit) and other BIM-based performance analysis software. However, this study did not make any contribution to practical demonstration of BIM-based evaluation.

7

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Table 5 GBAS criteria achievable with BIM. GBAS Criteria

References

Accredited professional Concrete User Index Daylight and views – daylight

(Jrade & Jalaei, 2013) (Chandra & Zhou, 2014) (Azhar et al., 2011)

Development Density and Community Connectivity Effluent discharge to foul sewers Embodied energy in building structural elements Enhanced commissioning Heat Island Effect: Roof

(Chen & Nguyen, 2017) (Wong & Kuan, 2014) (Wong & Kuan, 2014) (Jrade & Jalaei, 2013) (Barnes & Castro-Lacouture, 2009) (Jrade & Jalaei, 2013) (Bergonzoni et al., 2016) (Azhar et al., 2011) (Wong & Kuan, 2014) (Alwan et al., 2015) (Nguyen et al., 2016)

Increased ventilation Indoor Environment Quality Innovative wastewater technologies Landscaping and planters Light Pollution Reduction Location and Transportation: Green vehicles, Access to quality transit Low-emitting Materials Materials Reuse Maximize open space Measurement and verification Microclimate around buildings-elevated temperature Minimum Energy Performance Minimum landscaping area Modular and standardised design On-Site Renewable Energy Optimise energy performance Prefabrication Public Transportation Access Rapidly Renewable Materials Recycled Content Regional Materials

(Jalaei & Jrade, 2014) (Barnes & Castro-Lacouture, 2009) (Jrade & Jalaei, 2013) (Jrade & Jalaei, 2013) (Wong & Kuan, 2014) (Alwan et al., 2015) (Wong & Kuan, 2014) (Wong & Kuan, 2014) (Alwan et al., 2015) (Akcay & Arditi, 2017) (Wong & Kuan, 2014) (Chen & Nguyen, 2017) (Barnes & Castro-Lacouture, 2009) (Jalaei & Jrade, 2014)

(Jalaei & Jrade, 2014)

(Alwan et al., 2015)

(Jrade & Jalaei, 2013)

(Jrade & Jalaei, 2013)

(Jrade & Jalaei, 2013) (Jalaei & Jrade, 2014)

(Jrade & Jalaei, 2013)

(Wong & Kuan, 2014)

(Jalaei & Jrade, 2014)

(Jrade & Jalaei, 2013)

(Wong & Kuan, 2014) (Jrade & Jalaei, 2013)

(Wong & Kuan, 2014)

(Azhar et al., 2011)

(Alwan et al., 2015)

(Azhar et al., 2011)

(Wong & Kuan, 2014) (Barnes & CastroLacouture, 2009) (Jalaei & Jrade, 2014)

Site Selection Storage and Collection of Recyclables

(Barnes & Castro-Lacouture, 2009) (Alwan et al., 2015) (Alwan et al., 2015)

Storm water design: quality control Thermal Comfort

(Jrade & Jalaei, 2013) (Alwan et al., 2015)

(Azhar et al., 2011)

Waste management Water use reduction

(Jrade & Jalaei, 2013) (Azhar et al., 2011)

(Wong & Kuan, 2014) (Wong & Kuan, 2014)

(Barnes & CastroLacouture, 2009) (Jrade & Jalaei, 2013)

VE-Navigator is a module developed by IES for automated assessment of some credits of LEED version 4. VE-Navigator can evaluate and award credits related to thermal comfort, daylight and quality views of indoor environment quality and prerequisites of energy and atmosphere. This tool provides the platform to input, manage and produce results fitted for use with LEED templates. Besides, the tool is fully compatible with IES Tap for LEED, a cloud-based project management tool which allows direct submission of evidence to LEED online. Apart from LEED, IES provides analysis capability for some management, health and wellbeing and energy credits of BREEAM. Autodesk Revit developed Revit Credit manager to automate the assessment and generation of LEED submittals. The tool was able to evaluate four LEED 2009 credits. They were daylight (option 1: simulation and option 2: prescriptive), views (IEQ 8.2), water use reduction and recycled content of materials. Presently, Autodesk has ended the technology but continues to operate Light Analysis Revit (LA/R) which is a plug-in used to evaluate LEED IEQc8.1 2009 and LEED v4 EQc7 opt2. Light Analysis Revit (LA/R) uses Autodesk Rendering Cloud service to evaluate and generate LEED submittals. One-Click Life Cycle Assessment tool was found useful in evaluating criteria such as Building Life Cycle Impact Reduction of LEED V4 (MRc1) and Life Cycle Impact of BREEAM (Mat 1).

which details materials schedules can be extracted. The second group of software (BIM-based performance tools) includes IES-VE, Safaira, Ecotect analysis, Project Vasari. These tools have been associated with simulation-based quantitative criteria such as building energy use, water use or indoor environment quality (Akcay & Arditi, 2017; Alwan et al., 2015; Jalaei & Jrade, 2014). To evaluate these criteria, the BIM model must be imported into these software tools either manually or through automated queries. An appropriate data exchange platform is therefore required to reduce data losses as per discussions in Section 6. IES-VE was identified as the most commonly used tool for simulation. Other tools such as Microsoft Excel and Access were also identified for the purpose of data manipulation, storage and presentation of assessment results (Akcay & Arditi, 2017; Bergonzoni et al., 2016; Jalaei & Jrade, 2015; Jrade & Jalaei, 2013; Nguyen et al., 2010). Web Map Service was used to generate map data for transportation and location criteria (Chen and Nguyen, 2017). Ilhan and Yaman (2016) proposed a software, a Green Building Assessment Tool (GBAT), for criteria in the material category of BREEAM. Apart from the literature reviewed, the results of a survey on technical developments by selected BIM software vendors showed IES, One Click LCA and Autodesk had developed tools for evaluating and generating submittals for some LEED and BREEAM criteria. 8

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5. Database infrastructure

functionality significantly influences the tool used. Chen and Nguyen (2017) combined web map services to provide map information for assessing the location and transportation criteria of LEED. Unlike the rather static material databases identified earlier, users may be automatically fed with the most updated and suitable map data once an API is loaded. The cloud-based approach (Wu & Issa, 2012) extends functionalities beyond the design levels by providing for field and inventory data. Later during the construction stage, the project team can garner information as the work progresses to validate the design model. This information is referred to as “field data”. Similar information on materials and equipment used may be collected and categorized as inventory data. Instead of sophisticated methods of collecting as-built information demonstrated in (Rebolj, Pučko, Babič, Bizjak, & Mongus, 2017; Son & Kim, 2017; Wang, Sohn, & Cheng, 2018), Wu and Issa (2012) proposed a much simpler approach, a Velka cloud-based computing software which allows real-time collection and validation of BIM model. However, such methods have not been sufficiently explored in the literature.

The use of BIM for effective green building assessment requires a minimal variation between the BIM model and the constructed building. Therefore, the development of a quality database is critical to the evaluation process. As proposed in (Biswas & Krishnamurti, 2012), these databases can be classified as augmented, external and functional. BIM software such as Autodesk Revit, ArchiCAD and IES-VE have an embedded library of building elements (Jalaei & Jrade, 2015) from which users can construct a building envelope. In this study, they are classified as augmented databases. Revit’s building elements are classified under three levels including categories, families and types. Categories form the first level and are the general group of elements such as columns, beams, doors, roofs, walls and windows. Families narrow down as subgroups, for example concrete walls, brick walls and timber floors. Types have more defined characteristics. For example, dimensions and glazing type. Fig. 3 shows a typical Revit single flush door family and some associated parameters. Properties of materials in the form of texts, integers or computations can be inputted into definitive fields called parameters to evaluate criteria (Jalaei & Jrade, 2015). While some parameters are completely customizable, others are limited to some form of computations and values (Autodesk Revit, 2018). Besides these parameters, extensible parameters can be created and assigned to Revit elements using programming languages (e.g. C#) with Revit API (Nguyen, Toroghi, & Jacobs, 2016). Extensible parameters are particularly useful for automating the assessment process. Building objects can be tagged with characteristics such as “reused or renewable” to automatically retrieve details to assess criteria within Revit (Wong & Kuan, 2014). API can also retrieve model information to be fed into performance assessment tools. While augmented databases are intrinsic to the BIM authoring software, other forms of databases can be extrinsic to the BIM application and therefore classified as external databases. These databases range across online platforms to study-specifically designed platforms. With regards to the former, organisations such as EcoScoreCard (SmartBIM Technologies, 2018b), SMARTBIM Technologies (SmartBIM Technologies, 2018a) and GreenWizard (SPOT UL, 2018) provide platforms for manufacturer certified BIM objects or project information. BIM objects may be downloaded in the form of Autodesk Revit Files (RVT) or eXtensible Markup Language (XML) files with pre-programmed with manufacturer specific green labels. Currently, the data provided is limited to a few assessment schemes such as LEED (SmartBIM Technologies, 2018b) but can be adequately streamlined for use with others such as BEAM Plus. Developing a database of building elements and materials may be easily achieved, whereas the major challenges lie in regular updating to reflect changes and improvement to these products. Manual updating is usually time-consuming, tedious and demands expensive manpower. Sign-in requirement associated with most of these databases like Green Guide to Specification make automated updates even more challenging (Ilhan & Yaman, 2016). As extensible parameters and API provide opportunities to automate a substantial part of this process, it is necessary to develop tools that can identify and manage sources of sustainable information and database management/BIM software. A few studies established databases from collating information in research papers, assessment schemes’ websites and online data platforms including EcoScorecard mentioned above (Ilhan & Yaman, 2016; Jalaei & Jrade, 2014, 2015). Currently, proprietary database management tools such as Microsoft Access and Office can be designed to correlate with various parameters of objects or family files directly. However, the need for burdensome manual updates remains a challenge to these databases. Some studies (Biswas & Krishnamurti, 2012; Wu & Issa, 2012) have identified information pertinent to BIM-based green building assessments, but beyond those described above and is hence proposed the third category - functional databases. For this category, the desired

6. Data exchange module Due to the requirement of transferring BIM models between authoring software and BIM performance assessment tools, 3D models must be easily interpreted and used by different applications. First, manipulation of data between the BIM-related software involves interoperability issues which may result in substantial data loss. Another challenge is the ability to transfer assessment to populate GBAS templates. Exchange protocols identified from BIM/GBAS literature include gbXML schema in (Alwan et al., 2015; Azhar et al., 2011), Industry Foundation Class (IFC) in (Biswas & Krishnamurti, 2012; Ilhan & Yaman, 2016), Open Database Connectivity (ODBC) in (Jalaei & Jrade, 2014, 2015; Raffee et al., 2016; Wu & Issa, 2011, (Biswas & Krishnamurti, 2012) and Construction Operations Building Information Exchange (COBie) (Biswas and Krishnamurti, 2012). The level of

Fig. 3. Sample Revit Family, Type and Parameter window of a typical door assembly. 9

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database in other database management software like Microsoft Excel and Assess. Wu and Issa (2012) proposed a model to populate LEED online template with information generated directly from BIM models. However, the study did not show a detailed practical demonstration of the proposed approach. Unlike the protocols mentioned above, ODBC’s independent structure enables maximum interoperability, provides software developers with enough workspace, and transfers data without compromising the intended functionalities. Basically through ODBC, users can extract building information in tabular forms accessible through database management software such as Microsoft excel or Access (Jalaei and Jrade (2015)). Wu and Issa also highlighted challenges associated with ODBC including the loss of shared parameter information. These protocols are recommended for the final stage of BIM/GBAS automation to aggregate, evaluate and propagate information into LEED templates (Biswas & Krishnamurti, 2012). The extra information required for BIM-based green building assessments raise concerns with the capabilities of these exchange protocols. Therefore, their capacity to transfer model details should be further investigated. Furthermore, new tool development should focus on immediate reflection in credits when changes are made to a BIM model. Such developments require a shift in operation between tools from a sequential approach to a concurrently interactive approach (Wu & Issa, 2012).

performance attained with each of these exchange protocols vary depending on the type of data (Sanhudo et al., 2018). gbXML schema facilitates the transfer of data between a database, BIM authoring and simulation tools. This protocol defines information in BIM models by linking building geometries with descriptive data (Eddy & Brad, 2008). gbXML is developed based on XML, a non-proprietary protocol which allows customization of markup languages for exchanging information within various domains (Dong, Lam, Huang, & Dobbs, 2007). gbXML can capture the representation rather than the relationship between information. Primarily, all surfaces in a geometry are assigned two representations, a planar geometry and rectangular geometry. They complement each other for the purpose of checking the accuracy of model translation. For a model containing sensor information related to lighting or energy data, gbXML includes a meter element which handles the information name, description and utility rate for each sensor. gbXML is renowned for its simplicity, as data can be extracted with an XML sheet from the gbXML file especially with sensor information. However, geometry information is only limited to rectangular shapes (Jalaei & Jrade, 2014). Since it is a non-proprietary protocol, further studies may modify gbXML to recognize and interpret sustainable information unique to BIM-based green building assessment. Industry foundation classes (IFC) supports information storage and enhanced interoperability among a broader range of software (Sanhudo et al., 2018). IFC data files can be exchanged between applications using any of the three formats identified in (Building SMART, 2018). In exchanging data between software, IFC protocols provide and interpret relational and organizational data in the form of geometry and topology (Dong et al., 2007). Unlike gbXML which allows representing only rectangular geometry, IFC can represent multiple geometrical shapes. Its placement function locates an object within a coordinate system by two attributes: location and dim. Location is the geometric position of an item with regards to a reference point, and dim is the space dimension of the object (Dong et al., 2007). The quality of data transferred between application is dependent on the user implementation, transferred data type and interaction between software (Sanhudo et al., 2018). Jalaei and Jrade (2014) used the gbXML protocol for transferring material quantity take-off to the life cycle assessment tool. Dong et al. (2007) advocated that storing and retrieval sensing information related to energy and lighting is relatively simpler with the gbXML protocol. With regards to user implementation, model checking is essential. Occurrence of gaps after transfer between tools may occur, which can affect the evaluations especially for energy related criteria. In other instances (Biswas & Krishnamurti, 2012; Chandra & Zhou, 2014), adopted XML protocols to populate LEED templates. For the materials category of BREEAM assessment (Ilhan & Yaman, 2016), adopted IFC files for the model transfer between ArchiCAD and the developed green building rating tool. Functionalities of IFC protocols were extended beyond simulation domains to areas such as building construction and commissioning in (Dong et al. (2007)) and may be useful for output representation. Further research may be required to explore the limits of managing IFC to generate submittals. Besides these two exchange protocols, other protocols like COBie and ODBC have been proposed to overcome issues connected with data loss during model transfer. In (Biswas & Krishnamurti, 2012), COBie was also proposed as a protocol to incorporate unique data such as commissioning data. With the spreadsheet implementation of COBie, cumulative data structure facilitates data collection through the design, construction, commissioning and handing-over stages of projects. It is necessary to explore the amount and quality of information that can be embedded in COBie to fill in LEED templates automatically. Primarily, ODBC could be used to augment BIM models by integrating information that cannot be done with conventional methods. In (Jalaei & Jrade, 2014), the authors linked Life Cycle Assessment tools with BIM models through ODBC protocols. Jalaei and Jrade (2015) mentioned the possibility of directly exporting BIM data through ODBC to a predefined

7. Criteria assessment modules Generally, credit assessment required the extension of BIM software in the form of plugins or integration with some other tool functions (Jalaei & Jrade, 2015). They included an API, Microsoft Excel Macros, and Inbuilt extensions such as the Revit’s Dynamo Visual Scripting, COBie and a Cloud-Based Approach. In the most basic form of assessment, users tag materials with desired properties, extract a material take-off and evaluate credits attained as demonstrated in (Wong & Kuan, 2014). Scheduling function is especially handy for criteria such as building reuse (existing walls, floors and roof), materials reuse, and recycled content. There are some limitations such as extracting irrelevant information, double counting or ignoring difference such as floor levels or schedules (Chandra & Zhou, 2014; Wong & Kuan, 2014). Also, this approach still requires substantial expertise to extract the take-off. The identified APIs were unique to Autodesk Revit and Google Maps. API based integration modules operate as add-ons/plug-ins and can be embedded within the BIM software as toolbars. Revit’s API is based on the. NET framework compatible with programming languages like C#, F# or visual basic to develop the plug-in. The simplicity of C# makes it a commonly adopted programming language. For Revit, API can automate repetitive tasks, extract project data to automatically generate reports, import external data to create new elements and integrate with other applications (AutoDesk, 2018). Revit API has been frequently demonstrated in BIM/GBAS automation. Chen and Nguyen (2017) used an API to extract BIM model information and building’s location information from Revit and Google Maps. Nguyen et al. (2010) developed an interface between Revit and Microsoft Access with API to retrieve sustainable indicators for computing LEED credits. Jalaei and Jrade (2015) used Revit API to execute conditional queries between Revit and a designed database. Another study proposed an API with sub-interfaces to assess each criterion explicitly (Zhang & Chen, 2015). Jalaei and Jrade (2014) used an API to develop a plug-in which automates energy analysis and daylight simulation in Ecotect and IES-VE with input from Revit. These studies only focus on the evaluations achieved and pay less attention to the methodology/process of developing these APIs and evaluating relevant criteria. As a result, the evaluation process is less replicable. With regards to CUI under the sustainable construction of Green Mark, (Chandra & Zhou, 2014) presented an add-on for Revit to automate the evaluation process of CUI. Although the study achieved a higher level of accuracy in addition to distinguishing different floor 10

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automated evaluation of GBAS criteria from a BIM model. Based on reviewed literature and software identified, a need to improve the nexus between BIM and evaluating criteria of green building assessments is identified, and possible research directions are summarized in Fig. 4. The first research gap lies in the integrity of databases for the BIM model development. BIM-based green building assessments are highly dependent on comprehensive qualitative and quantitative data. Existing databases such as augmented databases embedded into BIM Modelling software and external databases are primarily limited to quantitative data storage. With these databases, discrepancies in elements used for BIM models and as-built models raise questions about the robustness of BIM-based assessments. An important step forward is the creation of a manufacturer-certified products database. GBAS such as BREEAM and LEED can partner with other organizations providing sustainable manufacturer-certified product information, so that tremendous opportunities exist to create parametric components with information required for evaluation. Also, interoperability issues arise from the variance in the structure of augmented and external databases, resulting in the manual input and loss of quality information. Furthermore, login requirements can limit possibilities of auto-updating databases. Future research can focus on the development of API based interfaces for automated data exchanges between BIM tools, databases and platforms providing manufacturer-certified BIM sustainable building components. Besides, provisions of qualitative data must be addressed in databases. Some authors have proposed functional databases through a platform like COBie. It is necessary that future studies explore the applicability of such tools in qualitative data storage and retrieval. The second gap is identified in the validity and robustness of studies reporting BIM-based green building assessments. Most of the reported studies lack rigorous validations of proposed models or are characterised by an ad hoc nature. Some studies report frameworks assessing a wide range of criteria but have only demonstrated a few cases. Even with these few cases, reports centre on the final achieved credits and seldom provide in-depth information on processes and procedures leading to them. Consequently, it is difficult to replicate the methods

levels, the results presented are ad hoc basis and less replicable. Nguyen et al. (2016) designed LEED Evaluator based on conditional queries with the Revit API to retrieve information required for LEED assessments. Once the developed interface is launched, it searches and populates itself with sustainable information retrieved from the BIM model, calculates credits achieved and generates reports. Wu and Issa (2012) demonstrated a seamless cloud-based information flow for the minimum energy performance credit of LEED. From BIM and BIM-based assessment tools, energy simulation results are imported into Lorax Pro, a third-party cloud-based LEED automation management tool, and then to LEED online with API. Through LEED Automation, partners of USGBC can receive API authorization and hence develop a natural interface to populate LEED online templates. Autodesk and IES, for instance, are partners and have developed apps for LEED automation. API can also be used to embed supplementary information like LEED documents and literature into the user interface to facilitate a more informed decision making. Apart from API, Microsoft Excel macro, Dynamo and COBie were used to integrate BIM software with criteria assessment module. To determine the optimum material combination for energy performance, (Akcay & Arditi, 2017) developed a macro in Microsoft Excel which provides the material combination and associated costs. In (Bergonzoni et al., 2016), an open source graphical programming extension to Revit (Dynamo) was presented. Dynamo was used to demonstrate a bi-directional data flow to verify the compliance of the design airflow with LEED requirements. However, many iterations may compromise the quality of data. In (Biswas & Krishnamurti, 2012), LEED requirements are converted into executable rules to demonstrate the assessment of some criteria with COBie. Model information retrieved and other information such as assessors’ names are used to augment the model based on conditions embedded into COBie. The output can then populate LEED submission templates in the XML format. 8. Research gap and recommendation for future work This paper has provided a review of literature and software based on the need to identify current levels of developments concerned with

Fig. 4. Summary of future research directions. 11

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reported in these studies. Furthermore, most studies barely report the limitations of used methods, for instance, the impact of exchange module on data loss. Also, a generalized framework open to a range of categories without demonstrations is not so meaningful. Therefore, future studies should centre on demonstrating a criterion with a greater focus on the replicability of methodologies. Some studies have indicated substantial differences between the reported and actual performance of GBAS certified projects. Hence, efforts should be made towards quantifying the difference between the predicted and actual green building performance (i.e. the limitation of applied methods) based on BIM. Another hurdle to the BIM-based sustainability assessment is the wide range of tools and varying information involved in assessments. This implies the transfer of BIM models among different tools. The key to interoperability is an appropriate model information exchange mechanism. Recent developments of IFC and gbXML facilitate the seamless bi-directional flow of standardized/conventional information between BIM, BIM-based performance analysis and other auxiliary tools. Nonetheless, BIM-based green building assessments rely on data-rich models, which require embedding more than standardized/conventional information into model design elements as highlighted earlier. In the case of the model transfer between tools, the model integrity after some iterations is questionable. Comprehensiveness of exchange protocols, as well as interoperability among software for BIM-based green building assessments, can be the next-step research. Also, future research should be extended beyond IFC and gbXML to explore the potential of COBIE and ODBC for transferring and managing various types of data required for GBAS. Besides evaluations for design improvements, generating project submittals with the help of API and functional databases is also critical in BIM based green building assessments. Existing research has paid little attention to the development process of API. Rather, many studies have focused on the results attained. Research and demonstration of the extraction of information through algorithms and functions of API and platforms should be brought to the future research agenda. A cloudbased BIM approach has been proposed to extend functionalities beyond normal single-desk BIM model developments. Facilitating green building assessments with cloud services is a potential research direction. Also, most GBAS have online platforms to coordinate the assessment process and provide API access to partners. Future studies can also focus on exploring cloud-based BIM with a focus on automatically populating GBAS submittal templates. Although some evaluation criteria are assigned more weight in GBAS, the comprehensiveness of assessment is a more valuable indicator of a building's sustainability. Most studies have extensively addressed criteria especially for LEED. Future studies may focus on demonstrating the assessment of criteria other GBASs such as BREEAM and BEAM Plus. Although evaluating some of these criteria used to be impossible with BIM, it is now considered feasible with API and models developed by IES and One Click Lifecycle, for instance. These recent developments can be further explored in future studies.

(2)

(3)

(4)

(5)

of energy & atmosphere, materials & resources have been extensively demonstrated through Revit scheduling and API. The development density, community connectivity and alternative transportation, although seldom addressed, have been well demonstrated in the literature. The databases identified within BIM/GBAS frameworks include augmented, external and functional databases. Augmented and external databases work best with quantitative criteria given the increased validity of assessments by reducing the deviation between BIM and as-built models. Functional databases, on the other hand, can facilitate the incorporation of unconventional data through the assessment process. Revit was identified to provide an API for extending functionalities. These API provide opportunities for future research in developing plug-ins to evaluate GBAS criteria. With a well-established developed database, materials and resources criteria can be assessed within the BIM model but assessing criteria in energy & performance, indoor environment quality, water use and site aspects requires other auxiliary tools such as Web Map Service providers (i.e. Google Maps, Yahoo maps), Safaira, Ecotect, Microsoft Excel and Word. IES-Navigator, Autodesk and One Click LCA were identified as tools provided by vendors for automated evaluation of criteria. IFC and gbXML are identified as the two most popular data exchange platforms across databases, BIM and BIM-based tools. IFC can store more geometrical forms than gbXML, while gbXML performs better with sensing information. Others exchange platforms like ODBC and COBie can be used to augment transferred models in case of data losses. For the integration and criteria assessment, the most frequently used method is the Application Programming Interface (API). The cloud-based approach, Microsoft macros and Dynamo (an inbuilt Revit extension) are also applicable for automating the assessment process.

This study provides references for both researchers and practitioners. Practitioners can gain a more comprehensive knowledge in applying BIM to green building assessments, while researchers can obtain a substantial guide to broaden the scope of BIM-based green building assessments based on numerous addressed frameworks. Future research should fill the identified research gaps in Section 8 to promote a comprehensive evaluation of green buildings from a BIM model. Acknowledgement The work described in this paper was supported by the PhD studentship from the Research Institute for Sustainable Urban Development (RISUD) of The Hong Kong Polytechnic University. References Akcay, E. C., & Arditi, D. (2017). Desired points at minimum cost in the “optimize energy performance” credit of leed certification. Journal of Civil Engineering and Management, 23, 796–805. https://doi.org/10.3846/13923730.2017.1319412. Alwan, Z., Greenwood, D., & Gledson, B. (2015). Rapid LEED evaluation performed with BIM based sustainability analysis on a virtual construction project. Construction Innovation Information Process Management, 15, 134–150. https://doi.org/10.1108/ CI-01-2014-0002. AutoDesk (2018). Help: What can you do with the revit platform API? (Accessed 22 October 2018) https://help.autodesk.com/view/RVT/2019/ENU/?guid=Revit_API_Revit_ API_Developers_Guide_Introduction_Getting_Started_Welcome_to_the_Revit_Platform_ API_What_Can_You_Do_with_the_Revit_Platform_API_html. Autodesk Revit (2018). Type of parameter reference | Revit Products 2016 | Autodesk Knowledge Network. (Accessed 2 August 2018) https://knowledge.autodesk.com/ support/revit-products/learn-explore/caas/CloudHelp/cloudhelp/2016/ENU/RevitModel/files/GUID-57C2F6A1-9947-47FA-A980-C8DF6B25E218-htm.html. Azhar, S., Carlton, W. A., Olsen, D., & Ahmad, I. (2011). Building information modeling for sustainable design and LEED®rating analysis. Autom. Constr.ation in Construction, 20, 217–224. https://doi.org/10.1016/j.autcon.2010.09.019. Barnes, S., & Castro-Lacouture, D. (2009). BIM-enabled integrated optimization tool for leed decisions. In: Proc. 2009 ASCE Int. Work. Comput. Civ. Eng. 258–268. https://doi.

9. Conclusions BIM has evolved as an invaluable tool in the architectural, engineering and construction industry. With the recent increasing momentum in the green building assessment and certification, BIM can provide excellent opportunities to facilitate sustainable design while reducing the certification cost. This paper presents a review of literature on the breadth of automated evaluation of GBAS criteria. The study also addressed development by software vendors. Significant findings can be summarized as below: (1) LEED criteria were identified as the most frequently addressed while BEAM Plus, GBI, Green Mark and BREEAM were seldom evaluated in the literature. Assessments concerning the categories 12

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M.K. Ansah, et al.

Science. Journal of the American Society for Information Science and Technology: JASIST, 59, 1711–1726. https://doi.org/10.1002/asi.20874. Nguyen, T. H., Shehab, T., & Gao, Z. (2010). Evaluating sustainability of architectural designs using building information modeling. The Open Construction & Building Technology Journal, 4, 1–8. https://doi.org/10.2174/18748368010040100001. Nguyen, T. H., Toroghi, S. H., & Jacobs, F. (2016). Automated green building rating system for building designs. Journal of Architectural Engineering, 22. https://doi.org/ 10.1061/(ASCE)AE.1943-5568.0000168. Oppong, G. D., Chan, A. P. C., & Dansoh, A. (2017). A review of stakeholder management performance attributes in construction projects. International Journal of Project Management, 35, 1037–1051. https://doi.org/10.1016/J.IJPROMAN.2017.04.015. Owusu, E. K., Chan, A. P. C., & Shan, M. (2019). Causal factors of corruption in construction project management: An overview. Science and Engineering Ethics, 25, 1–31. https://doi.org/10.1007/s11948-017-0002-4. Pezeshki, Z., Soleimani, A., & Darabi, A. (2019). Application of BEM and using BIM database for BEM: A review. Journal of Building Engineering, 23, 1–17. https://doi.org/ 10.1016/J.JOBE.2019.01.021. Raffee, S. M., Karim, M. S. A., & Hassan, Z. (2016). Building sustainability assessment framework based on building information modelling. ARPN : Journal of Engineering and Applied Sciences, 11, 5380–5384. Ramos-Rodríguez, A.-R., & Ruíz-Navarro, J. (2004). Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980–2000. Strategic Management Journal. 25, 981–1004. https://doi.org/ 10.1002/smj.397. Rebolj, D., Pučko, Z., Babič, N.Č., Bizjak, M., & Mongus, D. (2017). Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring. Automation in Construction, 84, 323–334. https://doi.org/10.1016/j.autcon.2017.09. 021. Sanhudo, L., Ramos, N. M. M., Poças Martins, J., Almeida, R. M. S. F., Barreira, E., Simões, M. L., et al. (2018). Building information modeling for energy retrofitting – A review. Renewable and Sustainable Energy Reviews, 89, 249–260. https://doi.org/10.1016/j. rser.2018.03.064. Santos, R., Costa, A. A., & Grilo, A. (2017). Bibliometric analysis and review of Building Information Modelling literature published between 2005 and 2015. Automation in Construction, 80, 118–136. https://doi.org/10.1016/J.AUTCON.2017.03.005. SmartBIM Technologies (2018a). Free parametric, data-rich revit objects. (Accessed10 October 2018) http://library.smartbim.com/?__hstc=141315565. f07b695e50524e59ae477026f1588f38.1539139537711.1539575338007. 1540653870756.3&__hssc=141315565.8.1540653870756&__hsfp=4279870473. SmartBIM Technologies (2018b). Find manufacturers - EcoScoreCard. (Accessed 12 September 2018) http://ecoscorecard.com/. Son, H., & Kim, C. (2017). Semantic as-built 3D modeling of structural elements of buildings based on local concavity and convexity. Advanced Engineering Informatics, 34, 114–124. https://doi.org/10.1016/j.aei.2017.10.001. SPOT UL (2018). SPOT | the world’s largest source for credible product sustainability information. (Accessed 1 October 2018) https://spot.ul.com/. The Hong Kong Green Building Council (2018). BEAM plus new buildings | introduction – the Hong Kong green building council (HKGBC) 香港綠色建築議會. (Accessed 25 October 2018) https://www.hkgbc.org.hk/eng/NB_Intro.aspx. US Green Building Council, LEED | USGBC (2018). US green building council, LEED | USGBC. (Accessed 25 October 2018) https://new.usgbc.org/leed. Wang, Q., Sohn, H., & Cheng, J. C. P. (2018). Automatic as-built BIM creation of precast concrete bridge deck panels using laser scan data. Journal of Computing in Civil Engineering, 32. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000754. Wong, J. K.-W., & Kuan, K.-L. (2014). Implementing “BEAM Plus” for BIM-based sustainability analysis. Automation in Construction, 44, 163–175. https://doi.org/10. 1016/j.autcon.2014.04.003. Wong, J. K. W., & Zhou, J. (2015). Enhancing environmental sustainability over building life cycles through green BIM: A review. Automation in Construction, 57, 156–165. https://doi.org/10.1016/J.AUTCON.2015.06.003. Wu, W., & Issa, R. R. A. (2011). BIM facilitated web service for LEED automation. In: Congr. Comput. Civ. Eng. Proc. 673–681. https://doi.org/10.1061/41182(416)83. Wu, W., & Issa, R. R. A. (2012). Leveraging cloud-BIM for LEED automation. Electronic Journal of Information Technology in Construction, 17, 367–384. Zhang, C., & Chen, J. (2015). LEED embedded building information modeling system. In: AEI 2015 Birth Life Integr. Build. - Proc. AEI Conf. 2015, 25–36. https://doi.org/10. 1061/9780784479070.003. Zuo, J., & Zhao, Z.-Y. (2014). Green building research–current status and future agenda: A review. Renewable and Sustainable Energy Reviews, 30, 271–281. https://doi.org/10. 1016/J.RSER.2013.10.021.

org/10.1061/41052(346)26. Bergonzoni, G., Capelli, M., Drudi, G., Viani, S., & Conserva, F. (2016). Building information modeling (BIM) for LEED® IEQ category prerequisites and credits calculations. In: EWork Ebus. Archit. Eng. Constr. - Proc. 11th Eur. Conf. Prod. Process Model. ECPPM 201675–79. Biswas, T., & Krishnamurti, R. (2012). Data sharing for sustainable building assessment. International Journal of Architectural Computing, 10, 555–574. https://doi.org/10. 1260/1478-0771.10.4.555. Bradley, A., Li, H., Lark, R., & Dunn, S. (2016). BIM for infrastructure: An overall review and constructor perspective. Automation in Construction, 71, 139–152. https://doi. org/10.1016/J.AUTCON.2016.08.019. Building SMART (2018). IFC Overview summary—Welcome to buildingSMART-Tech.oRg. (Accessed 21 August 2018) http://www.buildingsmart-tech.org/specifications/ifcoverview. Chandra, D., & Zhou, N. (2014). Bim add-on tool for automated CUI calculation. In: Rethink. Compr. Des. Specul. Counterculture - Proc. 19th Int. Conf. Comput. Archit. Des. Res. Asia, CAADRIA 2014305–314. Chen, P.-H., & Nguyen, T. C. (2017). Integrating web map service and building information modeling for location and transportation analysis in green building certification process. Automation in Construction, 77, 52–66. https://doi.org/10.1016/j. autcon.2017.01.014. Chen, X., Yang, H., & Lu, L. (2015). A comprehensive review on passive design approaches in green building rating tools. Renewable and Sustainable Energy Reviews, 50, 1425–1436. https://doi.org/10.1016/j.rser.2015.06.003. Dong, B., Lam, K. P., Huang, Y. C., & Dobbs, G. M. (2007). A comparative study of the IFC and gbXML informational infrastructures for data exchange in computational design support environments. IBPSA 2007 – Int. Build. Perform. Simul. Assoc. 20071530–1537. Eddy, K., & Brad, N. (2008). Green BIM: Successful sustainable design with building information modeling. Eleftheriadis, S., Mumovic, D., & Greening, P. (2017). Life cycle energy efficiency in building structures: A review of current developments and future outlooks based on BIM capabilities. Renewable and Sustainable Energy Reviews, 67, 811–825. https://doi. org/10.1016/J.RSER.2016.09.028. Gao, H., Koch, C., & Wu, Y. (2019). Building information modelling based building energy modelling: A review. Applied Energy, 238, 320–343. https://doi.org/10.1016/J. APENERGY.2019.01.032. Green Building Index Organisation (2018). Green building index rating tools. (Accessed 26 October 2018) http://new.greenbuildingindex.org/how/tools#. Green Building Index Organization (2018). Green building index classification. (Accessed 26 October 2018) http://new.greenbuildingindex.org/how/classification. He, Q., Wang, G., Luo, L., Shi, Q., Xie, J., & Meng, X. (2017). Mapping the managerial areas of Building Information Modeling (BIM) using scientometric analysis. International Journal of Project Management, 35, 670–685. https://doi.org/10.1016/J. IJPROMAN.2016.08.001. Ilhan, B., & Yaman, H. (2016). Green building assessment tool (GBAT) for integrated BIMbased design decisions. Automation in Construction, 70, 26–37. https://doi.org/10. 1016/j.autcon.2016.05.001. Jalaei, F., & Jrade, A. (2014). An automated BIM model to conceptually design, analyze, simulate, and assess sustainable building projects. Journal of Construction Engineering, 2014, 21. https://doi.org/10.1155/2014/672896. Jalaei, F., & Jrade, A. (2015). Integrating building information modeling (BIM) and LEED system at the conceptual design stage of sustainable buildings. Sustainable Cities and Society, 18, 95–107. https://doi.org/10.1016/j.scs.2015.06.007. Jrade, A., & Jalaei, F. (2013). Integrating building information modelling with sustainability to design building projects at the conceptual stage. Building Simulation, 6, 429–444. https://doi.org/10.1007/s12273-013-0120-0. Kamel, E., & Memari, A. M. (2019). Review of BIM’s application in energy simulation: Tools, issues, and solutions. Automation in Construction, 97, 164–180. https://doi.org/ 10.1016/J.AUTCON.2018.11.008. Liu, Z., Chen, K., Peh, L., & Tan, K. W. (2017). A feasibility study of building information modeling for green mark new non-residential building (NRB): 2015 analysis. Energy Procedia, 80–87. https://doi.org/10.1016/j.egypro.2017.12.651. Lu, Y., Wu, Z., Chang, R., & Li, Y. (2017). Building Information Modeling (BIM) for green buildings: A critical review and future directions. Automation in Construction, 83, 134–148. https://doi.org/10.1016/J.AUTCON.2017.08.024. Maltese, S., Tagliabue, L. C., Cecconi, F. R., Pasini, D., Manfren, M., & Ciribini, A. L. C. (2017). Sustainability assessment through green BIM for environmental, social and economic efficiency. Procedia Engineering, 180, 520–530. https://doi.org/10.1016/J. PROENG.2017.04.211. Meho, L. I., & Rogers, Y. (2008). Citation counting, citation ranking, and h -index of human-computer interaction researchers: A comparison of Scopus and Web of

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