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].

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.