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A model of BIM application capability evaluation for Chinese construction enterprises based on interval grey clustering analysis
A model of BIM application capability evaluation for Chinese construction enterprises based on...
Wang, Ailing; Su, Mengqi; Sun, Shaonan; Zhao, Yuqin
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 2, 210–221 https://doi.org/10.1080/13467581.2020.1782214 CONSTRUCTION MANAGEMENT A model of BIM application capability evaluation for Chinese construction enterprises based on interval grey clustering analysis a b c d Ailing Wang , Mengqi Su , Shaonan Sun and Yuqin Zhao a b c Zhengzhou University, Zhengzhou, China; School of Management Engineering, Zhengzhou University, Zhengzhou, China; School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, China; Department of Business Contract, The First Company of China Eighth Engineering Division Ltd, Zhengzhou, China ABSTRACT ARTICLE HISTORY Received 18 December 2019 Application of BIM plays a key role in the practices of architecture, engineering and construc- Accepted 29 May 2020 tion. Evaluating and improving BIM application capability have important effects on increasing BIM performance and enhancing the benefits of BIM usages. To evaluate the BIM application KEYWORDS capability of construction enterprises, this study proposed an evaluation model based on BIM application capability; interval grey cluster analysis (IGCA). Firstly, the study constructed an assessment index system interval grey clustering of BIM application capability from three dimensions, including technical, organization and analysis; interval-entropy management, and human aspect. Secondly, BIM application capability and its evaluation index weight method; evaluation were divided into four levels. Thirdly, in order to determine the index weights, the interval- entropy weight method was applied. Then IGCA was applied to identify the BIM application capability. Finally, a case study was conducted to explain the application of the proposed model and to verify the validity of the model. The results indicated that the evaluation model could provide a new way to evaluate and improve BIM application capabilities. 1. Introduction rapid development in China, the BIM application capabil- ity is as a whole insufficient. BIM application capability is In recent years, Building Information Modeling (BIM) has the comprehensive one involving the management, tech- experienced a rapid development in China (Wu et al. nology and human in the process of introducing and 2017) and become more common application in con- applying BIM in the enterprise (Wang and Li 2018). The struction projects (Lin and Yang 2018). As the second challenge is how to improve the BIM adoption rate and revolution in the Architecture, Engineering and application capability of China. To address this challenge, Construction (AEC) industry, Building Information an assessment schema for AEC organizations should be Modeling (BIM) has been considered to be the most developed to gauge the effectiveness of BIM implemen- promising recent developments (Lin, Lee, and Yang tations in order to measure the performance of BIM utili- 2016) as well as the leading technology for use in AEC zations and enable continuous BIM improvements practices (GhaffarianHoseini et al. 2017). It has a critical (Yilmaz, Akcamete, and Demirors 2019). However, there role in enhancing the effectiveness of project delivery is not enough emphasis on the issue in China. Only a few from the initial concept to completion and post- studies have focused on the evaluation of BIM application construction maintenance (Ding, Zhou, and Akinci capability (Wang et al. 2017; Yu 2017; Wang and Li 2018). 2014; Volk, Stengel, and Schultmann 2014), and also In addition, in order to make a quantitative evalua- has a significant impact on the efficiency of generation tion of BIM application capability, an appropriate eva- of building information and sharing of this information luation method should be found. This study among various stakeholders throughout the building introduced interval grey clustering analysis (IGCA) for lifecycle (Yilmaz, Akcamete-Gungo, and Demirors 2017). the evaluation of BIM application capability. According to a survey by China in 2019, 52.07% of Considering that there are few studies on BIM applica- construction enterprises were engaged with BIM on less tion capability evaluation, this study established an than 10 projects, while only 7.03% of construction enter- IGCA-based evaluation model of BIM application cap- prises were engaged with BIM on more than 50 projects ability, which included evaluation index system, BIM (Chen et al. 2019). Furthermore, there were still 18.09% of application capability levels and quantitative assess- construction enterprises which have not established ment method. The model could provide theoretical a BIM organization, not to mention continuous BIM and practical guidance for evaluating and improving usages. These imply that although BIM is in a stage of BIM application capability of AEC enterprises. CONTACT Shaonan Sun firstname.lastname@example.org School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, China This article has been republished with minor changes. These changes do not impact the academic content of the article. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 211 organization. VDC Scorecard was developed in 2012 by 2. Literature review Stanford University. It measures the project perfor- Successful BIM implementation requires a thorough mance against an industry benchmark, which includes understanding of the current situation of BIM opera- 4 main areas, 10 divisions, and 74 measures (Kam et al. tions as well as effective, advanced, and high- 2013). Although each model has a different number of performing measurements (Wu et al. 2017). However, measures clustered into different numbers of layers, there is not enough emphasis on the issues in China. the common concepts have been selected to define Wang et al. (2017) and Yu (2017) established the index the metrics. In most models, their measures are classi- system for BIM application capability evaluation and fied into the common categories, which are process, conducted the assessment based on the analytic hier- stakeholder/personnel, standard, software, hardware, archy process (AHP). Wang and Li (2018) utilized the and data (Yilmaz, Akcamete, and Demirors 2019). factor analysis to construct an index system for enter- Moreover, according to Chinese researches Wang and prise BIM application capability evaluation, but there Li (2018), BIM application capability is the comprehen- was no quantitative evaluation. In addition, the estab- sive one involving the management, technology and lished index systems were not comprehensive, and the human in the process of introducing and applying BIM classification and hierarchy were confusing: divisions in the enterprise. Therefore, based on the above or sub-divisions with the same title comprised different measures. For instance, the measure “BIM related train- research, we classified BIM application capability into ing” was classified under the organizational aspect in three dimensions: technology, organization and man- Wang et al. (2017), while this measure was under the agement, and human aspect. process aspect in Yu (2017). These differences easily In terms of research methods of BIM application lead to confusions. In fact, this measure is generally capability evaluation, most literatures utilized analytic classified under Human aspect (Succar 2010). hierarchy process (AHP) for evaluation (Wang et al. Considering that there are few studies on BIM appli- 2017; Yu 2017). Although AHP is a common method to solve multicriteria decision-making (MCDM) pro- cation capability evaluation in China, the BIM maturity blems, the results obtained through it tend to be sub- model can serve as a reference for the development of jective. Moreover, capability assessment is affected by BIM application capability evaluation model due to the many factors, and most of these factors are qualitative similarities and overlaps between the metrics of the measures, which makes it difficult to conduct quanti- two (Wang and Li 2018). Several models have been tative evaluation when the information is uncertain developed abroad to measure BIM maturity, such as and incomplete. Grey system theory is a method to NBIMS CMM (NIBS, 2007), BIM PM created by Indiana handle uncertainties in small data samples with impre- University Architect’s Office (IU Arhictect’s Office, cise information (Wong and Hu 2013; Sun, Liang, and 2009), BIM QuickScan from the Netherlands (Van Wang 2019). Grey clustering analysis is one of the Berlo and Hendriks 2012), VDC Scorecard developed classic methods of grey evaluation methods. It is the by Stanford University Center for Integrated Facility combination of grey system theory and cluster analy- Engineering (Kam et al. 2013) and BIM MM by (Succar sis, and is widely utilized in many fields such as eco- 2010). NBIMS CMM was proposed by the National nomics, military, biology, transportation and Institute of Building Science in 2007. The model evalu- environmental quality assessment (Yuan and Liu ates BIM implementation in 11 areas using a 10-level 2012; Pei and Wang 2013; Xie, Liu, and Zhan 2013; scale (NIBS 2007; Giel 2014). It focuses on evaluating Jian et al. 2014; Shen, Xu, and Wang 2008; Wang, BIM maturity of construction projects. BIM PM was Ning, and Chen 2012; Li, Zhang, and He 2012; Jia, Mi, designed to assess BIM services performance in terms and Zhang 2013). The traditional grey clustering ana- of 8 areas, 32 measures and 5 maturity levels (CIC lysis is generally based on the real number domain, 2012). Unlike NIBMS CMM, it focuses on assessing the which is not applicable when the sample value is an BIM maturity of organizations. Although the two mod- interval. Considering this problem, the researchers pro- els are the bases for the following models, they are posed interval grey clustering analysis (IGCA) (Zhou et usually criticized because of limited measurement al. 2013; Wang et al. 2015; Qian, Liu, and Xie 2016; scope in technical aspects (Succar 2010). BIM MM was Dang et al. 2017). However, the reported applications developed to assess BIM performance of organizations, of this method in the construction industry are limited. projects, teams and individuals. It provides compre- In summary, there is not enough emphasis on BIM hensive explanations for each measure to minimize application capability evaluation for enterprises in inconsistencies and expands the measuring scope to China. Only a few studies have focused on the cover non-technical aspects of BIM (Giel and Issa 2013). issue. Moreover, previous studies have not found an BIM QuickScan was launched in 2011. It consists of 4 appropriate method for BIM application capability main areas and 44 measures, which provides insight evaluation. Therefore, based on five foreign maturity into the strengths and weaknesses of BIM usage in an models and relevant domestic literatures, this paper 212 A. WANG ET AL. firstly established an evaluation index system for BIM Ω 2 ½0; 1�, the interval grey number � is called the application capability, and then constructed an IGCA- standard grey number. μð� Þ ¼ � � is the mea- based assessment model for BIM application capabil- surement of the range of � . In the absence of the ity evaluation. Lastly, the model validity and applic- value distribution information of interval grey ability were verified through case studies. ^ number � , we note � as the kernel of � , where þ o � ¼ ð� þ� Þ=2, and g ð� Þ as the degree of grey- ness of � , where g ð� Þ ¼ μð� Þ=μðΩÞ (Guo et al. 2019; 3. Methodology Liu, Fang, and Forrest 2010). For example, considering Considering the methods for evaluation, there are many � ¼ ½2; 8� is the interval grey number defined on the commonly employed methods such as fuzzy compre- domain of discourse [0, 10], then the hensive evaluation (FCE), AHP, grey correlation analysis μð� Þ ¼ 8 2 ¼ 6, μðΩÞ ¼ 10 0 ¼ 10, the and TOPSIS (Li and Yu 2013; Wang et al. 2017; Yu 2017; � ¼ ð2þ 8Þ=2 ¼ 5, and the g ð� Þ ¼ 6=10 ¼ 0:6. Wu and Hu 2020). However, the results obtained Considering the interval grey number through FCE and AHP tend to be subjective, and þ � 2 ½� ; � �, it can alternatively be represented as because of the difficulty in determining the reference � o or � ðrÞ ð� ðrÞ ¼ � þð� � Þr; 0 � r � 1Þ, ðg Þ sequence or the optimal vector, the grey correlation where � o is the simplified form and � ðrÞ is the ðg Þ analysis and TOPSIS may not be applicable to this standardized form of interval grey number � (Wang study. In addition, structural equation model (SEM), et al. 2015; Qian, Liu, and Xie 2016). The two forms principal component analysis (PCA), factor analysis (FA) contain both the upper limit and lower limit informa- and BP neural network are also popular methods for tion, and have the one-to-one correspondence with evaluation (Gunduz, Birgonul, and Ozdemir 2017; Ma, interval grey number, i.e. the two forms contain the Shang, and Jiao 2018; Li 2019; Liu, Zhan, and Tian 2019). same amount of information as the original interval However, these methods require large data samples to grey number. Given an interval grey number conduct evaluation, which are not applicable to � 2 ½� ; � �, we can represent it in simplified form research due to the difficulty to obtain multiple samples. or standardized form. On the contrary, when the sim- As discussed previously, grey clustering analysis has the plified form or standardized form is known, we can also advantages of both grey system theory and clustering get the original interval grey number via the previous analysis, and can solve the multi–index evaluation pro- definition. For example, continuing the example blem with small samples and poor information. The above, the simplified form of interval grey number evaluation results by this method are intuitive and reli- � ¼ ½2; 8� is � ¼ 5 , and the standardized form ð0:6Þ able. According to grey clustering analysis, the white- is � ðrÞ ¼ 2þð8 2Þ� r ¼ 2þ 6r; 0 � r � 1. nization values of the clustering object for different The algorithm of interval grey numbers is the theo- clustering indices are summarized according to retical basis of grey system theory, which plays an a number of grey numbers to determine the grey cate- important role in the application of interval grey num- gories (Fu and Zou 2018). Moreover, for the issue of bers. The researches (Guo et al. 2019; Liu, Fang, and capability evaluation, it is difficult to accurately quantify Forrest 2010; Li, Yin, and Yang 2017) have proposed the relevant indices and classify the grey categories of the algorithm of interval grey numbers based on ker- the evaluation objects due to the complexity of the nel and degree of greyness, which could avoid the reality and the incomplete information. In most cases, problems caused by the original algorithm, such as data range may be given as intervals based on existing the abnormal amplification of the degree of greyness. information. On this account, interval grey clustering Assume there are two interval grey numbers � 2 analysis (IGCA) was chosen for evaluation. � � � � þ þ � ; � and � 2 � ; � , which are simply 1 1 2 2 ^ ^ recorded as � and � , then the algorithms of 1 o 2 o ðg Þ ðg Þ 1 2 3.1. Related theory of interval grey number interval grey numbers are: The interval grey number refers to the uncertain value in ^ ^ ^ ^ � þ� ¼ ð� þ� Þ o o (1) 1 o 2 o 1 2 a certain interval or a general number set (Zhou et al. ðg Þ ðg Þ ðg _g Þ 1 2 1 2 2013). In this paper, the entropy weight method and grey clustering analysis with interval grey number were ^ ^ ^ ^ � � ¼ ð� � Þ (2) o o 1 o 2 o 1 2 ðg _g Þ ðg Þ ðg Þ 1 2 applied to develop an evaluation model of BIM applica- 1 2 tion capability. For this purpose, the basic concepts and algorithms of interval grey number are introduced, and ^ ^ ^ ^ � � � ¼ ð� � � Þ o o (3) 1 o 2 o 1 2 ðg Þ ðg Þ ðg _g Þ 1 2 1 2 interval grey number ordering is also discussed. ^ ^ ^ ^ � =� ¼ ð� =� Þ (4) 3.1.1. Basic concepts and algorithms o o 1 o 2 o 1 2 ðg _g Þ ðg Þ ðg Þ 1 2 Assume � 2 ½� ; � � is the interval grey number ^ ^ defined on the domain of discourse Ω, and when k�� ¼ ðk�� Þ o (Suppose k is a real number)(5) 1 o 1 ðg Þ ðg Þ 1 JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 213 Step 1: Determination of the decision matrix 3.1.2. Interval grey number ordering The evaluation value of the i-th expert on the j-th According to the ordering method based on precision and relative kernel (Liu, Fang, and Forrest 2010; Ma et al. index is recorded as the interval grey � � number� 2 a ; b ði ¼ 1; 2;��� ;m; j ¼ 1; 2;��� ;nÞ, 2017), the method for ranking interval grey numbers is ij ij ij as follows. which can also be written as t ¼ a þ b a � ij ij ij ij Suppose � 2 ½� ; � � is the standard grey num- r r 2 ½0; 1� according to the definition of the stan- ij ij ber. � is the kernel of � , and g ð� Þ is the degree of dard interval gray number, so the decision matrix can greyness of � , then we note γð� Þ as the precision of be expressed as T ¼ t . ij m� n � , where γð� Þ ¼ 1=ð1þ g ð� ÞÞ, and δð� Þ as the Step 2: Data standardization relative kernel of � , where δð�Þ ¼ γð�Þ � � . In order to eliminate the influence of different Considering the two standard grey numbers � and dimensions between the indicators, the original data � , then 2 ~ matrix should be normalized to P ¼ ðp Þ . The stan- ij m� n If δð� Þ> δð� Þ, then � � � ; 1 2 1 2 dardization formula is as follows. If δð� Þ< δð� Þ, then � � � ; 1 2 1 2 ij If δð� Þ ¼ δð� Þ, then p ~ ¼ P (6) 1 2 ij ij i¼1 (1) If γð� Þ> γð� Þ, then � � � ; 1 2 1 2 Step 3: Calculation of the information entropy (2) If γð� Þ< γð� Þ, then � � � ; 1 2 1 2 (3) If γð� Þ ¼ γð� Þ, then � ¼ � . 1 2 1 2 ~ ~ E ¼ p lnp (7) j ij ij lnm i¼1 h i For example, considering the interval grey number where E 2 E ; E . The smaller the information j j � ¼ ½0:3; 0:6� and � ¼ ½0:4; 0:9�, which are repre- 1 2 entropy of evaluation index is, the more effective infor- sented as � ¼ 0:45 and � ¼ 0:65 . Then the 1 ð0:3Þ 2 ð0:5Þ mation it provides, and the greater the weight of the γð� Þ ¼ 1=ð1þ 0:3Þ ¼ 0:7692, γð� Þ ¼ 1=ð1þ 0:5Þ ¼ 1 2 index will be. 0:6667; δð� Þ ¼ 0:7692� 0:45 ¼ 0:3461; δð� Þ ¼ 1 2 Step 4: Calculation of the index weights 0:6667� 0:65 ¼ 0:4334. δð� Þ< δð� Þ, so � � � . 1 2 1 2 1 E When the degree of greyness is equal to zero, the j W ¼ (8) interval grey number becomes a real number, then the n E j¼1 h i comparison of the interval grey numbers is converted where W 2 w ; w , into the comparison between real numbers. 1 E w ¼ P (9) j n n E j¼1 j 3.2. Entropy weight method with interval grey number 1 E w ¼ P (10) j n In multi-attribute decision-making, different weights n E j¼1 j need to be assigned to each attribute due to its differ - After obtaining the grey entropy weight of index ent influence on the evaluation object. The methods þ w þw j j j,w ^ ¼ is taken as the weight of index for determining the weights include Delphi method, j according to the theory of interval grey number analytic hierarchy process, sequential scoring method, “kernel”, which is normalized to get the final weight etc. However, the weights obtained through these vector w ¼ ðw ; w ;��� ; w Þ. 1 2 n methods are subjective and arbitrary. The entropy weight method was chosen because it is an objective weighting method which determines the index weight 3.3. Evaluation method based on IGCA according to the variability of indices (Ma et al. 2017). Generally, the smaller the information entropy of an The specific steps of the assessment are as follows. index is, the greater the variability of the index Step 1: Construction of the interval grey number becomes, thus the more information it provides, and whitenization weight functions the greater its weight (Suchith Reddy, Rathish Kumar, The grey categories are classified according to the and Anand Raj 2019; Dos Santos, Godoy, and Campos requirements of the project, and then the correspond- 2019). Entropy weight method could ensure the objec- ing interval grey number whitenization weight func- tivity and accuracy of the index weights, so as to tions of each gray category are determined. The ensure the authenticity and reliability of the evaluation whitenization weight function of index j on the k-th results. In this paper, the interval gray number is intro- grey category is denoted as f ð�Þðj ¼ 1; 2;��� ; n; h i h duced into the entropy weight method to determine k k k k k k ¼ 1; 2;��� sÞ. f ; ;� ð3Þ;� ð4Þ , f � ð1Þ; j j j j j the weight of indices. The specific steps are as follows h i k k k k k (Qian, Liu, and Xie 2016). � ð2Þ; ;� ð4Þ�, f � ð1Þ;� ð2Þ; ; are the lower j j j j j 214 A. WANG ET AL. limit measure whitenization weight function, the mod- rewritten in simplified form before calculation. erate measure whitenization weight function and Considering the interval grey number � of � � o k the upper limit measure whitenization weight ^ index j,� ¼ � , g ¼ μ � =μ Ω , and� ðlÞ ¼ j j o j j j j ðg Þ k k k k � � function, respectively.� ð1Þ;� ð2Þ;� ð3Þand� ð4Þ j j j j k o k � ðlÞ , g ðlÞ ¼ μ � ðlÞ =μ Ω . The calculation o k j ðg ðlÞÞ j j are, respectively, the first, second, third and fourth j formula of the whitenization weight values for the turning points of the whitenization weight function, h i index j on all grey categories can be represented as k k k where � ðlÞ 2 � ðlÞ ;� ðlÞ ðl ¼ 1; 2; 3; 4Þ: The j j j follows (Dang et al. 2017). three interval grey number whitenization weight func- Lower limit measure whitenization weight function: þ k 0 � � 0 or � � � ð4Þ > j j j ^ ^ > 1 � 2 ½0;� ð3Þ� and � < 0 ðg Þ j j j þ k > 1 0 � � � � � � ð3Þ j j j k k þ k ^ ^ f ð� Þ ¼ 1 o o k � 2 ½0;� ð3Þ� and � > � ð3Þ (11) j ðg _g ð3ÞÞ j j j j j > ^ ^ � ð4Þ