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Rong, J.; Qi, L.; Wu, H.; Zhang, M.; Hu, X. Framework for Evaluating the BIM Application Performance. Encyclopedia. Available online: https://encyclopedia.pub/entry/47927 (accessed on 30 August 2024).
Rong J, Qi L, Wu H, Zhang M, Hu X. Framework for Evaluating the BIM Application Performance. Encyclopedia. Available at: https://encyclopedia.pub/entry/47927. Accessed August 30, 2024.
Rong, Jingguo, Lizhong Qi, Hongbo Wu, Ming Zhang, Xiancun Hu. "Framework for Evaluating the BIM Application Performance" Encyclopedia, https://encyclopedia.pub/entry/47927 (accessed August 30, 2024).
Rong, J., Qi, L., Wu, H., Zhang, M., & Hu, X. (2023, August 11). Framework for Evaluating the BIM Application Performance. In Encyclopedia. https://encyclopedia.pub/entry/47927
Rong, Jingguo, et al. "Framework for Evaluating the BIM Application Performance." Encyclopedia. Web. 11 August, 2023.
Framework for Evaluating the BIM Application Performance
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This entry is adapted from the peer-reviewed paper 10.3390/su151511658

Building information modeling (BIM) is one of the core applications of information technology (IT) in the construction industry. BIM technology builds virtual buildings with geometric information through digital means.

BIM application performance evaluation framework

1. Introduction

Building information modeling (BIM) is one of the core applications of information technology (IT) in the construction industry. BIM technology builds virtual buildings with geometric information through digital means. The geometric information in virtual buildings includes not only the visual information of building shape but also nongeometric information such as fire resistance grade and procurement information. Moreover, BIM technology can summarize the data throughout the whole life cycle of projects and process the document data from the design stage to the operation stage in an integrated way, which will continue to play a role in the construction and operation stage of the project. BIM can integrate all parametric modeling information, functional requirements, and component performance throughout the life cycle of a building project into a single architectural model. It includes process information such as during construction processes and service processes [1][2]. Accordingly, the application of BIM technology can improve the productivity of the construction industry and assist its transformation toward digitization [3]. However, the lack of effective BIM application evaluation measures has hindered BIM application in the construction industry [4].
With the rapid development of the national economy and the requirements of sustainable development, investment in power generation projects in China has increased rapidly. However, due to the complexity of the various on-site conditions, project stakeholders, and expectations, the question of how to enhance the construction performance and sustainability of power generation projects is a critical challenge for management practices and decision-makers. To solve these challenges, developing and applying digital technologies in substation construction projects has recently become a priority [5]. BIM technology should be an important and effective tool for power engineering projects due to its ability to achieve flexible information sharing and adaptive interaction [6]. However, BIM only reflects information on a single building project and cannot meet the needs of the grid network and crossover and information exchange between different power grids in power transmission and transformation projects. Accordingly, the application of BIM technology in power transmission and transformation projects is expected to be studied.
Based on BIM, the China State Grid Corporation has developed a computer-aided design technology that enables the digital representation of information to meet the 3D design needs of transmission and substation projects, which is named grid information modeling (GIM). GIM technology has improved the shortcomings of the previous BIM to a certain extent. However, GIM cannot meet the relevant national requirements in terms of application scope, depth of application, and user incentives [7]. Moreover, analyzing and evaluating the GIM application in substation construction projects can improve work efficiency and enable rapid development of these projects. Therefore, it is very necessary to establish an evaluation system for assessing the GIM application in the power generation construction industry.

2. Performance Evaluation of BIM Application

With the development of the digital economy and industrial digitization, BIM has gradually been applied to various fields of construction projects. For example, more and more engineering projects have been adopting BIM technology, such as power engineering [8], large airport projects [9], and hydropower engineering [10]. BIM can not only accurately and quickly measure project costs [11] and minimize project labor and time costs [12], but also conduct real-time quality information collection and processing on construction sites, identify construction problems, and support real-time quality control [13]. Moreover, based on the integration of BIM with artificial intelligence, construction robots, big data, and other new-generation information technologies, it also benefits to establish an information-based management platform, improve the collaborative work of stakeholders, and achieve multi-stage information transmission and full-life-cycle project management [14]. Therefore, BIM has been an important platform for promoting sustainable development in the construction field.
Recently, in order to efficiently promote the BIM application, its application performance has been evaluated. Li et al. established an evaluation model for the effectiveness of BIM application by focusing on owners and analyzed the correlation between the five secondary and tertiary benefit indicators of product, finance, organization, management, and strategy [15]. Chan et al. discussed the critical success factors of BIM implementation in the architecture, engineering, and construction industry of Hong Kong, aiming to improve the successful delivery of projects as well as coordination and cooperation in project design, construction, and management stages [16]. Liu et al. focused on the application performance of BIM during the construction phase [17]. Barlish and Sullivan developed a computational model for assessing the benefits of BIM, highlighting the significant impact of information transfer, change, and improved project duration [18]. Al-Ashmori et al. pointed out that the hindrance in BIM implementation is largely due to stakeholders’ lack of understanding of the technology’s benefits, and that cost, production advantage, and coordinated communication are the most important application benefits for stakeholders [3]. Deng et al. employed the fuzzy comprehensive evaluation (FCE) method to study the application benefits of BIM in project cost control [19].
Evaluating the BIM application performance could promote the BIM application in the construction field and then improve construction productivity and project management performance. Wang et al. demonstrated that evaluating and improving the application ability of BIM has a positive effect on the application efficiency of BIM, and they evaluated the application efficiency of BIM from three dimensions of application ability: technology, organizational management, and personnel using an interval gray clustering analysis model [20]. Badrinath and Hsieh found that the key factors of project delivery success using BIM technology can drive the critical success factors of the project [21]. Alaloul et al. emphasized the important role of BIM in risk identification, prediction, and mitigation during the construction process through the analysis of semistructured questionnaire data using SPSS 26 software, and they recognized that BIM can significantly improve construction project safety and reduce accident rates [22]. Therefore, it is essential to assess the performance of BIM applications in the construction industry.
Previous studies generally developed different performance indicators to evaluate the BIM application performance. Luo et al. established a BIM application benefit evaluation model based on the four dimensions of the Balanced Scorecard—learning and growth, internal operation, finance, and customers—and also analyzed the model using the network data envelopment analysis (DEA) performance evaluation method [4]. Sinoh et al. analyzed 184 questionnaires from Malaysian construction, engineering, and construction companies and found that nontechnical factors such as management, leadership, and coordination had a higher correlation with BIM performance compared to technical factors such as software and hardware [23]. Phang et al. evaluated the benefits of applying BIM to precast concrete companies and identified eight important factors that influence the successful application of BIM technology, including policy support, high-level attention to BIM, competitive advantage, design intent, accurate information on BIM, increased productivity, reduced time and support teams [24]. Mahamadu et al. used the fuzzy entropy weight method to analyze the application performance of BIM and divided the influencing factors in the pre-qualification and bidding stages into different dimensions such as ability, resources, culture and attitude [25]. Nonirit et al. evaluated the performance of BIM implementation from the aspects of efficiency, the relevance of BIM to specific environments, and its usefulness [26].
However, there is a lack of a systematic evaluation system that focuses on application benefits and costs. Yang and Chou proposed an overall benefit evaluation framework for BIM implementation, including formative evaluation (requirement evaluation, structured conceptualization, implementation evaluation, process evaluation, etc.) and summary evaluation (structural evaluation, economic evaluation, impact evaluation, secondary analysis, and meta-analysis) [27]. However, this evaluation system only assessed the BIM application benefits without considering the application cost. The cost related to BIM infrastructure, training, and operation is an obstacle to BIM application [28].

3. Evaluation Methods of BIM Application

Previous research has adopted different research methods for the performance evaluation of BIM applications. The main evaluation methods include Delphi, principal component analysis (PCA), gray relational analysis (GRA), DEA, analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). For example, Denecke et al. used the Delphi method to obtain evaluation indicators through a literature review, and obtained the health robot application evaluation model after three rounds of expert evaluation [29]. Wyke et al. identified 26 factors affecting the cost and time overrun of construction projects through interviews with project managers and a literature review, and they grouped the 26 factors into four dimensions of quality control, project preparation, user management, and project management by using principal component analysis [30]. Bai and Liu calculated the weights of factors affecting the quality of the construction process using the GRA method [31]. Luo et al. built a BIM application evaluation index system and evaluated the application performance of BIM based on DEA [4]. Gudiene et al. used AHP to analyze the weight of the critical success factors of the construction project and ranked them [32]. The comparison among them is shown in Table 1. AHP can effectively combine qualitative and quantitative analysis, thereby increasing evaluation reliability.
Table 1. Common methods of performance evaluation in BIM application.
Method Introduction Advantages Disadvantages Suitable Object
Delphi Organize experts to conduct independent evaluations, summarize and re-evaluate, and carry out round by round. Simple operation, give full play to the role of experts, take the essence and discard the dregs. Strong subjectivity, long operation time, and poor usability for complex problems. Simple, unquantifiable objects.
PCA By reducing dimensions, multiple indicators are simplified into several important indicators to avoid information duplication. Comprehensive, objective, and comparable. It requires a large amount of basic data, which cannot reflect the comprehensive level of a certain element, and is not for evaluating qualitative indicators. Classify the evaluation objects.
GRA Quantify the dynamic development state of the system and judge the development trend of the gray process. It requires less sample size and is easy to operate. Defining curve similarity is slow and requires comparability of the selected variables. Technical performance, service level evaluation.
DEA Determine the weight coefficient of each unit and evaluate the relative effectiveness and relative ineffectiveness. The index dimension is not limited; objectivity is strong, and it is suitable for a multi-input multi-output system Data are too sensitive; there are no absolute indicators, only to measure the relative level, not for actual conditions. Production efficiency, scale effectiveness, etc.
AHP According to the relationship of each influencing factor, a hierarchical structure model is formed, the weights of the indicators are determined by comparing with each other, and the importance of the overall goal is finally determined. The principle is simple, the error is small, the result is reliable, and the combination of qualitative and quantitative. The subjectivity is strong, the correction cannot be optimized, and the pairwise comparison of the evaluation factors is too difficult. Establishment of evaluation index and determination of weight.
FCE The membership function is used to determine the evaluation matrix and quantify the evaluation index. For fuzzy systems with more qualitative indexes, comprehensive decision making can be realized and the result is clear. If information duplication cannot be avoided, there will be subjective membership. Qualitative index analysis, systematic evaluation.

References

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Subjects: Construction & Building Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Jingguo Rong
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Lizhong Qi
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Hongbo Wu
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Ming Zhang
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Xiancun Hu
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Update Date: 11 Aug 2023
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