Abstract
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 13674945675@163.com 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 � > j k k > ^ ^ ^ ð Þ � 2 ½� ð3Þ;� ð4Þ� > k k j ^ ^ j j > � ð4Þ � ð3Þ o o k o k j j > ðg _g ð3Þ_g ð4ÞÞ > j j j > þ k k ^ ^ 0 o k � >� ð4Þ and � < � ð4Þ o j ðg _g ð4ÞÞ j j j j j Moderate measure whitenization weight function: þ k k 0 � � � ð1Þ or � � � ð4Þ j j j j k k þ k ^ ^ ^ > 0 0 k � ‚½� ð1Þ;� ð4Þ� and � > � ð1Þ o j ðg _g ð1ÞÞ j j j j > j j > k ^ ^ � � ð1Þ < j j k k ^ ^ ^ ð Þ � 2 ½� ð1Þ;� ð2Þ� k j k k ^ ^ j j � ð2Þ � ð1Þ 0 k k f ð� Þ ¼ o o (12) j j j j ðg _g ð1Þ_g ð2ÞÞ j j j > ^ ^ � ð4Þ � > j k k > ^ ^ ^ ð Þ k k � 2 ½� ð2Þ;� ð4Þ� 0 o o > k k j ^ ^ ðg _g ð2Þ_g ð4ÞÞ j j > � ð4Þ � ð2Þ j j > j j : þ k k k ^ ^ ^ 0 0 o k � ‚½� ð1Þ;� ð4Þ� and � < � ð4Þ ðg _g ð4ÞÞ j j j j j j Upper limit measure whitenization weight function: 0 � � � ð1Þ j j > þ k k > ^ ^ 0 k � � � ð1Þ and � > � ð1Þ o o > j ðg _g ð1ÞÞ j j j > j < k ^ ^ � � ð1Þ k k ^ ^ ^ ð Þ � 2 ½� ð1Þ;� ð2Þ� f ð� Þ ¼ k k j (13) ^ ^ j j j � ð2Þ � ð1Þ o o k o k j j ðg _g ð1Þ_g ð2ÞÞ j j j > k k ^ ^ 1 o o k � >� ð2Þ and � < � ð2Þ > j ðg _g ð2ÞÞ j j j j j 1 � > � ð2Þ j j tions are shown in Figure 1-3. Step 3: Determination of grey clustering weight Step 2: Calculation of whitenization weight values The weight reflects the importance degree of differ - The whitenization weight values are calculated ent indexes to the evaluated objects. The entropy according to the whitenization weight function deter- weight method based on interval grey number was mined in step 1. The turning points and index values are applied to determine the index weight. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 215 According to the principle of maximum member- ship, the grey category of the evaluation object can be determined by the following formula. � � max k k� σ ¼ σ (15) 1� k� s Thus the grey category of the evaluation object is k . 4. Proposed model of BIM application capability evaluation The proposed evaluation model was an approach for assessing the BIM application capabilities of enterprises. Figure 1. Lower limit measure whitenization weight function. Assessments using the approach enable the organizers to understand current levels of BIM application capabilities. The assessment results could provide baseline for improve- ments in BIM usages. The evaluation model possessed a schema in which evaluation index system, application capability levels, and evaluation process were defined to facilitate BIM application capability assessments. 4.1. Construction of evaluation index system Considering that related Chinese standards and engi- neering project management models are different from Figure 2. Moderate measure whitenization weight function. those of foreign countries, the five internationally recog- nized BIM maturity models, namely NBIMS CMM, BIM Proficiency Matric, BIM Maturity Matrix, BIM Quick Scan and VDC Scorecard, may not be completely suitable for domestic situations. Therefore, based on the five matur- ity models, this study combined the relevant domestic literature and Chinese BIM-related standards to develop the evaluation index system. The evaluation index sys- tem was confirmed by experts who have rich experi- ence in BIM application and project management. The sources of each index are shown in Table 1, and the evaluation index system is shown in Table 2. 4.2. Division of BIM application capability levels Figure 3. Upper limit measure whitenization weight function. It was necessary to decide how many application cap- ability levels should be defined to cover the different Step 4: Calculation of comprehensive clustering levels of BIM utilization. Various multi-stage divisions coefficients of BIM maturity have been proposed in the literature. After determining the whitenization weight For example, six levels of BIM maturity for NBIMS CMM values and clustering weight, the comprehensive and BIM QuickScan; five levels of BIM maturity for BIM clustering coefficients that represent the member- PM and BIM MM; four levels of BIM maturity for VDC ship degree to a certain grey category of the clus- Scorecard and Multi-functional BIM MM have been tering indexes are calculated by the following defined. However, it has been observed that four- formula. stage divisions have been proposed and tested more frequently (King and Teo 1997). In addition, according k k to Yilmaz, Akcamete, and Demirors (2019), four levels σ ¼ f � � w (14) j j of BIM capability appear to be sufficient without omit- j¼1 ting any significant type of BIM utilization. Therefore, Step 5: Determination of the grey category of the we created four levels of BIM application capability evaluation object starting from Level 0 to Level 3. To define the BIM 216 A. WANG ET AL. Table 1. Sources of evaluation indices for BIM application capabilities. Source of indices NBIM BIM BIM VDC BIM Quick Wang, Wang, and Peng Yu Wang and Li Indices CMM MM PM Scorecard Scan * (2017) (2017) (2018) Technical C √ √ √ √ C √ √ √ √ C √ √ √ √ √ C √ √ √ √ √ √ √ √ C √ √ √ Organization and C √ √ √ √ √ management C √ √ √ √ √ C √ √ √ √ √ √ C √ √ √ C √ √ √ √ Human Aspect C √ √ √ √ √ C √ √ √ √ √ √ C √ √ √ √ Note: “*” -(Shanghai Housing and Urban-Rural Construction Management Committee 2017). Table 2. Index system for BIM application capability evaluation. Indices Indices interpretation and explanation Technical Richness and Accuracy of BIM-Related Data Rich and accurate data on both graphical and non-graphical information and and Information (C ) life cycle information uses Model-Based Calculations and Analysis (C ) Model-based optimization, simulation, cost accounting, schedule control and other calculations and analysis BIM-Related Software and Hardware BIM software selections and hardware configurations for BIM uses Configuration (C ) Interoperability and coordination of BIM- Interoperability and coordination of BIM data among multiple disciplines/ related data (C ) stakeholders Secondary Development Capability of BIM Secondary development capability in BIM software uses Software (C ) Organization and BIM Strategies and Goals (C ) BIM vision, strategic planning and goals for BIM usages management Attitude of Management and Leadership Management and Leadership’s accurate cognition and continuous support towards BIM (C ) toward BIM Perfectness of BIM-Related Standards (C ) Perfectness of BIM-Related Standards at organizational and project level Completeness of BIM Business Processes (C ) Completeness of processes (such as operation, change, and delivery, etc.) at the BIM usages level Applicability of Organizational Structure (C ) Organizational structure is adaptable to BIM usages Human Aspect Experiences, Skills and Knowledge of BIM Staff/ BIM-Related Staff Experiences, Skills and Knowledge of BIM Staff/Stakeholders Stakeholders (C ) BIM-Related Training and Education (C ) BIM-Related Training and Education for Staff/Stakeholders BIM-Related Responsibilities and Roles (C ) Arrangement of BIM-Related Responsibilities and Roles capability levels, Multi-functional BIM MM (Liang et al. BIM application capability level 2-Integrated BIM: 2016) and BIM-CAREM (Yilmaz, Akcamete, and The previously performed BIM is implemented Demirors 2019) were followed, and the actual situation using an integrated BIM supporting collaboration of BIM application in Chinese enterprises was also and data exchange between stakeholders and considered. BIM application capability levels are pre- business processes. sented below. The level descriptions of indices are BIM application capability level 3-Optimized BIM: shown in Table 3. The previously integrated BIM is used at organiza- tional level and is continuously improved to achieve BIM application capability level 0-Incomplete BIM: the strategies and goals of the organization. BIM is not implemented or partially implemented but there are no changes and resource commit- ments to support BIM. 4.3. Evaluation process BIM application capability level 1-Performed BIM: BIM is implemented to achieve the busi- The proposed approach to evaluate BIM application ness process purpose and is used to perform capabilities can be presented in three phases. base practices and produce standalone BIM out- Phase I-In the first phase, a team of experts give comes. However, BIM has not been integrated their judgment on assessment indices score according into the business processes, and there is no to the BIM implementation. In this phase, the proposed significant BIM-based collaboration and data index system will be explained to the experts. exchange between stakeholders and business Phase II–In the second phase, the decision matrix is processes. constructed based on the scores given by experts. The JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 217 Table 3. Level descriptions of indices. Level descriptions of Indices Indices Level 0 Level 1 Level 2 Level 3 C Basic core data Data and limited information. Mostly sufficient and accurate Completely full and accurate information. information. C Only 3D models Model-based optimization analyses Model-based cost accounting, schedule Model-based information and digital are established. (e.g. clash detection, control and other integrated management. comprehensive pipeline details management. design, etc.). C Facilities are Hardware and software could allow Hardware and software could allow Program established for continuous deficient to users to have access to a basic BIM users to have access to an advanced updating BIM system (sustainable support BIM system. BIM system. needs). system. C No Limited interoperability of data based Limited interoperability of data using Full interoperability of data using IFCs, interoperability, on products, and data are shared at IFCs, and data are shared at intervals, and data are shared by following and data are regular intervals, but it is not the number of which are standardized industrial standards. shared when standardized. within organization. requested. C No secondary Forced secondary development Limited secondary development Full secondary development for development continuous improvement BIM. C No BIM Strategies Specific BIM objectives at individual Clear BIM strategies and goals at the BIM strategies and goals are optimized and Goals are project level, which are achieved as organizational level for continuous improvement BIM, available for planned. and are recognized and accepted BIM. with the organization. C Know little about Familiar with BIM, but no support/ Positive attitude towards BIM, and full Positive attitude towards BIM, and full BIM. limited support for BIM support for BIM implementation with support for BIM implementation implementation. some resource commitment. with appropriate resource commitment. C No BIM-related BIM-related standards are established BIM-related standards are standardized BIM-related standards follow industrial standards. but do not have a standardized within the organization. standards. format. C No business Limited business processes are Full business processes are defined, but Business processes are defined and process is defined. are not standardized. standardized within the defined. organization and follow industrial standards. C No change in Limited changes in organizational Limited changes in organizational Full-scale changes in organizational organizational structure, but do not establish structure, and there is a specialized structure, which is adaptable to BIM structure. a specialized BIM group/ BIM group/department in the implementation. department. organization. C Know little about Familiar with BIM and have basic skills Limited experience but with proficient Rich experience, knowledge and skills BIM. for BIM implementation. skills for BIM implementation. for BIM implementation. C No training and Limited training and education are Limited training and education are Systematic training and education are education are provided, but not on a regular provided at regular intervals. provided at regular intervals for provided. basis. continuous improvement BIM C No roles or BIM is the responsibility of the BIM BIM is the responsibility of the BIM BIM responsibility is specified for every responsibilities technical leader in the group/BIM department. member of the organization. are defined. organization. interval-entropy weight method utilizes this matrix to application in China and has been using BIM for estimate weights of the indices. The outputs of this about 10 years. At present, Company A has implemen- phase will be the input (weights of the indices and ted BIM in the whole life cycle of the project, and has decision matrix) of phase III. preliminarily established a BIM data platform including Phase III-At last, in the third phase, the IGCA method project data such as schedule, contract, cost, quality, (as described earlier) is used to evaluate the organiza- drawings, etc., and has also realized the integration of tion and determine the BIM application capability level BIM with standardization, informatization and cloud of the organization. technology. We conducted our case study in company A based on the proposed evaluation model. Step 1: Development of the decision matrix 5. Application of proposed approach Three experts were invited to deliver their judg- ments on index values based on the BIM implemen- 5.1. Case study tation of company A, which were denoted in the This paper took company A in China as an example. interval grey numbers in the domain of [0,100]. Company A is a construction enterprise founded in the These experts were selected on the basis of their 1940 s with many years of experience in construction rich experience in construction project manage- engineering, municipal engineering, fire engineering ment, and all of them have many years of BIM and architectural design. It is the pioneer of BIM experience. The decision matrix was constructed 218 A. WANG ET AL. based on the index values given by experts, which according to the expert opinions, which were as was as follows: follows. ð58; 60Þ ð73; 75Þ ð81; 84Þ ð58; 62Þ 1 f ¼ ½ ; ;ð52; 54Þ;ð62; 65Þ�; Að� Þ ¼ ð64; 67Þ ð63; 66Þ ð75; 77Þ ð56; 60Þ 4 2 ij f ¼ ½ð52; 54Þ;ð62; 65Þ; ;ð78; 82Þ�; ð56; 59Þ ð70; 72Þ ð79; 82Þ ð64; 67Þ ð78; 80Þ ð71; 73Þ ð80; 82Þ ð74; 78Þ ð81; 83Þ ð69; 72Þ ð64; 67Þ ð78; 81Þ ð84; 86Þ ð77; 79Þ 3 f ¼ ½ð62; 65Þ;ð78; 82Þ; ;ð90; 95Þ�; ð74; 76Þ ð68; 70Þ ð84; 86Þ ð81; 85Þ ð87; 90Þ f ¼ ½ð78; 82Þ;ð90; 95Þ ; �: ð75; 77Þ ð56; 58Þ ð75; 79Þ ð67; 70Þ Then we took the average of the index values given ð71; 74Þ ð60; 64Þ ð82; 85Þ ð72; 74Þ by the three experts in step 1 as the index scores, ð79; 82Þ ð58; 60Þ ð77; 80Þ ð76; 79Þ which were written in simplified form and denoted as Step 2: Determine the index weights � . Then we calculated the whitenization weight ðg Þ According to the interval-entropy weight method values of indices according to Equations (11 – 13). introduced in Section 3.2, the index weights were cal- culated as follows. Finally, based on the index weights obtained in step Considering the first indicator (j = 1), the interval 2, we calculated the comprehensive clustering coeffi - grey numbers were written in standardized form with cients according to Equation (14). Whitenization t ¼ 58þ 2r , t ¼ 64þ 3r , t ¼ 56þ 3r . Then weight values and comprehensive clustering coeffi - 11 11 21 21 31 31 cients are shown in Table 5. 58þ 2r p ¼ 178þ 2r þ 3r þ 3r 11 21 31 5.2. Results and discussion 64þ 3r p ¼ According to Equation (15) and the ordering method 178þ 2r þ 3r þ 3r 11 21 31 introduced in Section 3.1.2, the BIM application cap- ability of the enterprise was found at level 2- Integrate 56þ 3r ~ BIM, which is consistent with the Chinese construction p ¼ 178þ 2r þ 3r þ 3r 11 21 31 industry’s qualitative assessment on BIM application capability of company A. In addition, we fed the results ~ ~ ~ ~ ~ ~ back to the BIM managers and BIM engineers of the E ¼ ðp lnp þ p lnp þ p lnp Þ 11 11 21 21 31 31 ln3 enterprise to discuss with them, all of the interviewees Then we obtained E 2 ½0:9972; 0:9994� through stated that the evaluation results were the same as MATLAB software. Similarly, the entropy values of all their expected BIM application capability level. The indices can be obtained. Then, according to the equa- judgment of the construction industry and the feed- tions in step 4 of Section 3.2, the index weights were back from company A verified the effectiveness of the obtained as shown in Table 4. proposed evaluation model. Step 3: Determine the BIM application capability According to the index weights in Table 4, the key level indices that affect the BIM application capability are C - According to the division of BIM application cap- Richness and Accuracy of BIM Related Data and ability levels in the previous section, the grey category Information, C -Model Based Calculations and Analysis, was divided into four. In this phase, we first deter- C -Interoperability and coordination of BIM related data, mined the whitenization weight functions of indices C -BIM Related Responsibilities and Roles, and C - 13 9 Completeness of BIM Business Processes. For Company A, the improvement of these indices could facilitate the Table 4. Calculation results of index weights. improvement of BIM application capability. In addition, ~ ~ Indices E W w ^ w j j j j based on the evaluation results in Table 5, Company C [0.9972,0.9994] [0.0256,0.5714] 0.2985 0.1198 C [0.9976,0.9992] [0.0342,0.4898] 0.2620 0.1051 A could identify the performance of their BIM utilizations, C [0.9989,0.9998] [0.0085,0.2245] 0.1165 0.0467 and at the same time could determine the current level of C [0.9955,0.9987] [0.0128,0.5714] 0.2921 0.1172 each index in conjunction with the level description in C [0.9983,0.9995] [0.0213,0.3469] 0.1842 0.0739 C [0.9987,0.9997] [0.0128,0.2653] 0.1391 0.0558 Table 3, thus could enable continuous BIM improvements. C [0.9992,0.9999] [0.0043,0.1633] 0.0838 0.0336 The IGCA-based assessment approach could deter- C [0.9980,0.9996] [0.0171,0.4082] 0.2126 0.0853 C [0.9980,0.9993] [0.0299,0.4082] 0.2190 0.0879 mine the BIM application capability of the enterprise, C [0.9984,0.9997] [0.0128,0.3265] 0.1697 0.0681 which could also identify the key factors affecting BIM C [0.9985,0.9999] [0.0043,0.3061] 0.1552 0.0623 C [0.9987,0.9999] [0.0043,0.2653] 0.1348 0.0541 12 performance. It indicates that this approach provides C [0.9979,0.9995] [0.0213,0.4286] 0.2250 0.0902 a new idea for BIM application capability evaluation JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 219 Table 5. Whitenization weight values and comprehensive clustering coefficients of indices. 1 2 3 4 ^ ^ ^ ^ ^ � f ð� Þ f ð� Þ f ð� Þ f ð� Þ j o j o j o j o j o j j j j ðg Þ ðg Þ ðg Þ ðg Þ ðg Þ Indices j j j j j 1 (0.03) (0.03) (0.03) (0.03) C 60.6667 0.2698 0.7302 0 0 2 (0.03) (0.04) (0.04) C 69.8333 0 0.6162 0.3838 0 3 (0.03) (0.04) (0.04) (0.04) C 79.6667 0 0.0202 0.9798 0 4 (0.04) (0.04) (0.04) (0.04) C 61.1667 0.2222 0.7778 0 0 5 (0.03) (0.04) (0.04) C 74.8333 0 0.3131 0.6869 0 6 (0.03) (0.04) (0.04) C 68.8333 0 0.6768 0.3232 0 7 (0.03) (0.03) (0.05) (0.05) C 81.8333 0 0 0.8533 0.1467 8 (0.04) (0.04) (0.05) (0.05) C 81.3333 0 0 0.8933 0.1067 9 (0.03) (0.03) (0.05) (0.05) C 82.8333 0 0 0.7733 0.2267 10 (0.03) (0.04) (0.04) C 76.3333 0 0.2222 0.7778 0 11 (0.04) (0.04) (0.04) C 59.3333 0.3968 0.6032 0 0 12 (0.04) (0.04) (0.04) (0.04) C 79.6667 0 0.0202 0.9798 0 13 (0.03) (0.04) (0.04) C 73.0000 0 0.4242 0.5758 0 (0.04) (0.04) (0.05) (0.05) comprehensive clustering coefficients 0.0831 0.3973 0.4856 0.0340 and realizes the conversion between qualitative con- a new way to evaluate BIM usages and enable con- cepts and quantitative values of capability evaluation, tinuous BIM improvements. and also allows the index values to be given in the One limitation in this research should be noted. The form of interval grey number, thus could facilitate assessment of the indices reflects the cognition of the scientific and reliable evaluation results. personnel; thus, the results can be affected by the characteristics of the respondents. In the future research, we plan to expand the number of respon- 6. Conclusion dents to assign weight for each respondent to enhance the reliability of the assessment and conduct other Assessment of enterprise BIM application capability is cases to further validate the model. crucial to the improvement of BIM performance and the development of BIM technology. This paper introduced interval grey clustering analysis to construct an evaluation Acknowledgments model for BIM application capability. The model considers The authors would like to acknowledge the National the problem that the index values are difficult to be Natural Science Foundations of China and Key R&D accurately quantified under the incomplete and uncertain and promotion Special Projects of Henan Province, information, and employs the interval grey number to China for financially supporting this work, and express deal with the BIM application capability evaluation by our appreciation to the experts for providing useful defining the levels of capability in terms of intervals and data, valuable information, and helpful comments dur- ing our research. taking the index values as interval data, which could make The authors would like to extend our sincere gratitude to the evaluation more in line with the reality and the eva- our teacher, Danying Gao, for his instructive advice and luation results more scientific and reliable. useful suggestions on our thesis. We are deeply grateful of The proposed evaluation model consists of three his help in the completion of this thesis. elements: the evaluation index system, the BIM application capability levels and the evaluation pro- Author contributions cess. The index system includes three dimensions of technical, organization and management, and Ailing Wang proposed innovation points, provided research human aspect, which are constructed based on platforms and research funds, guided and modified the manu- script. Mengqi Su did the data collection and analysis, and relevant literature analysis and expert opinions. wrote the manuscript. Shaonan Sun guided and modified the Four capability levels are defined to map the evolu- manuscript. Yuqin Zhao provided the case information. tion of each metric. In order to determine the index weights, the interval-entropy weight method was performed to assign the interval weight for each Disclosure statement indicator. The IGCA method was then applied to No potential conflict of interest was reported by the authors. evaluate BIM application capability. Finally, a case study was performed to verify the validity of the evaluation model. Based on the feedback of the Funding interviewees on the evaluation results, it indicates This work was supported by the National Natural Science that the proposed evaluation model could be used Foundation of China under Grant [number 51709115]; to effectively identify the BIM capability levels of National Natural Science Foundation of China under Grant enterprises. The evaluation model could provide [number 71801195]; and Key R&D and promotion Special 220 A. WANG ET AL. Projects of Henan Province under Grant [number and Management 143 (4): 04016112. doi:10.1061/(ASCE) 182102210066]. CO.1943-7862.0001259. Guo, S. D., Y. Li, F. Y. Dong, B. J. Li and Y. J. 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Journal
Journal of Asian Architecture and Building Engineering
– Taylor & Francis
Published: Mar 4, 2021
Keywords: BIM application capability; interval grey clustering analysis; interval-entropy weight method; evaluation
