Next Article in Journal
Multi-Indicator Environmental Impact Assessment of Recycled Aggregate Concrete Based on Life Cycle Analysis
Next Article in Special Issue
Understanding the Disruptiveness of Integrated Project Delivery (IPD) in the AEC Industry
Previous Article in Journal
Development of Safety Domain Ontology Knowledge Base for Fall Accidents
Previous Article in Special Issue
A Capability Maturity Model for Integrated Project Delivery
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 15
Issue 13
10.3390/buildings15132300
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Investigating Prefabricated Construction Technology Innovation Dynamics: Evidence from a Patent Analysis in China

by
Yuna Wang
1,
Anqi Huang
1 and
Yudan Dou
2,*
1
School of Management, Guangzhou University, Guangzhou 510006, China
2
Department of Construction Management, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(13), 2300; https://doi.org/10.3390/buildings15132300
Submission received: 8 May 2025 / Revised: 13 June 2025 / Accepted: 24 June 2025 / Published: 30 June 2025
(This article belongs to the Special Issue Innovation in Construction and Project Management: Digital Technologies, Intelligent Systems, and Sustainable Solutions)
Browse Figures
Versions Notes

Abstract

Prefabricated construction technology (PCT) is a significant driver for promoting high-quality development in the construction industry. Patents, as critical outputs of technological innovation, provide diversified data that can manifest trends in technological innovation systems. However, few studies have comprehensively revealed the dynamics of PCT innovation systems from a systematic view, considering both innovation actors and technologies. Based on 6047 patent data in China, a combination of bibliometric analysis and social network analysis are employed to examine the structure of the PCT innovation system. Subsequently, networks are constructed based on the collaborative relationships between patent applicants and technologies. Through analysis of the metrics of entire networks and nodes, the dynamics of PCT innovation systems is revealed. Generally, China’s PCT innovation system has evolved into a complex network characterized by multi-actor participation and multi-technology collaboration, playing a pivotal role in fostering sustained PCT innovation and generating substantial innovative outcomes. Nevertheless, challenges persist, including insufficient cross-domain collaboration and constrained flows of innovation resources. Moving forward, efforts should prioritize enhancing interdisciplinary cooperation, optimizing the allocation of technological resources, refining policy guidance mechanisms, and strengthening the system’s overall collaborative innovation capacity.

1. Introduction

Prefabricated construction technology (PCT) represents a series of transformative technologies that revolutionize conventional in situ construction methods [1]. Key benefits include accelerated project delivery [2], superior quality [3], reduced resource consumption [4], and lower carbon emissions [5]. Given these advantages, PCT has gained global adoption as a strategic solution to address challenges in traditional construction methods [6].
Enterprises, research institutes, and universities have actively pursued PCT innovations. Previous studies have examined PCT evolution through either academic literature reviews [1,7] or policy bibliometrics [6,8]. Patent analyses have recently emerged to map technology diffusion among PC organizations [9], from the view that patents are a critical technological innovation output. However, few endeavors have been made on digging into technology collaborations in PCT innovation activities. Consequently, the dynamics of PCT innovation systems, comprising innovation subjects (actors) and innovation objects (technologies), remain inadequately mapped. This study addresses this gap through comprehensive patent analysis, aiming at providing a holistic, dynamic, and systematic image of PCT innovation systems for academics and practitioners. The specific objectives are (1) to systematically identify the key elements of the PCT innovation system through a dual-dimensional analysis of innovation actors and technologies, (2) to elucidate the interrelationships among elements within the PCT innovation system by analyzing the collaboration networks of actors and technologies, and (3) to reveal the evolutionary trajectory of PCT innovations through phased network analyses. Given significant variations in the initial development timelines of PCTs across countries, coupled with divergent current development levels [6] and the substantial policy-dependence characteristic of construction industry innovations [10,11], this study specifically examines PCT innovation within the national context of China. This paper proceeds through five structured sections: First, global PCT innovations are reviewed, followed by an exposition of the methodology, including data sources, analytical techniques, and conceptual frameworks. The subsequent sections present patent analysis results, discuss China-specific PCT innovation system dynamics, and conclude with theoretical and practical implications.

2. PCT Innovation Review

PCT innovation refers to the development and application of advanced prefabricated technologies in building construction. It aims to reform or restructure the socio-technical systems in the construction industry by effectively leveraging technology rationality [12]. The significance of PCT innovation is manifested in reducing construction costs, shortening project durations, and pre-controlling product quality for developers and end-users, enabling contractors to break free from volatile labor markets and traditional organizational structures while significantly improving enterprises’ profitability through enhanced productivity and creating high-quality building assets that align with national development, resource conservation, urban construction, and quality of life for broader society [13,14,15].
Early PCT innovation studies primarily focused on specific technologies, including traditional PCTs, e.g., standardized design of exterior walls [16] and modular facade retrofitting [17], and emerging digital technologies, e.g., robotic applications in prefabricated building production [18], blockchain for prefabrication plant monitoring [19], and AI-driven production decision-making [20]. The digital transformation of the construction industry has amplified PCT’s inherent characteristic of multi-disciplinary technology integration [12]. PCT innovation now encompasses both conventional technology domains (e.g., design, component production, quality inspection, and on-site assembly) and emerging digital technology domains (e.g., intelligent production control, advanced sensors, digital modeling, and big data analysis).
Subsequent studies propose that organizational structures play a more critical role than technology advancements in driving innovation [18]. This stems from the complex value chain of PCTs, which involves multiple specialized offsite processes and diverse stakeholders [13]. It requires cross-disciplinary, multi-actor collaboration for integrated innovation. Consequently, a few studies suggest examining PCT innovation through the view of collaborative innovation activities [21]. Collaborative innovation emphasizes actor interactions across two dimensions, i.e., project-centric stakeholders (owners, contractors, designers, suppliers, etc.) along project value chains [15], and innovation-centric alliances (industry–academia partnerships, innovation consortia) that facilitate knowledge transfer through patents, publications, standards, and R&D systems [22,23].
Recent studies adopt a systems perspective in PCT innovation, where technology innovation systems focus on organizational relationships. The rationality lies in the fact that innovation typically involves resource recombination [24] and essential innovation elements often require inter-organizational acquisition [25] or emerge through collaborations [26]. Such interactions enable risk-sharing and complementary learning to address complexity and uncertainty [27]. Thus, system functionality depends fundamentally on system composition and relational dynamics. Networks constitute the basis of technology innovation systems, serving as channels for knowledge exchange, information transfer, capability enhancement, and resource allocation [28,29]. Network analyses in technology innovation systems typically model innovation actors as nodes connected by directed flows of knowledge, technology, data, or capital [22], from an organizational view. Emerging approaches also construct technology co-occurrence networks with IPC classes as nodes to analyze technology innovation systems from a technological perspective [30].

3. Research Design

3.1. Research Method

Patents serve as critical outputs of technological innovation and have been widely utilized in prior studies analyzing innovation dynamics, with their efficacy well-demonstrated (e.g., [31,32,33,34]). Existing patent-based analyses of technological innovation dynamics have adopted two complementary perspectives, i.e., innovation actors (e.g., inter-organizational innovation networks) [22] and technological artifacts (e.g., structural evolution of knowledge domains) [30]. Actor-oriented research predominantly employs social network analysis (SNA), utilizing centrality metrics and community detection algorithms to characterize spatiotemporal patterns in collaboration networks [9]. For technological artifact analysis, two methodological approaches prevail: bibliometric analysis [34] and machine learning techniques [33]. Bibliometric studies typically leverage structured patent metadata [35] or IPC codes [30], applying frequency analysis, co-citation mapping, and co-word clustering to identify technological innovation trends. This study combined the two perspectives based on a systematic view that innovation actors and technologies constitute the PCT innovation system; the dynamics of the system can be described by the evolution of actors and technologies.
Social network analysis (SNA) is a methodology for investigating social structures by examining individuals (nodes) and their relationships (edges). Its primary objective is to reveal core actors, critical pathways, and clusters, as well as interactions and structural characteristics within networks [35]. SNA emphasizes the significance of relationships and structures. In social networks, the connections between individuals often provide more explanation for collective behaviors and outcomes than individual attributes alone [9]. The significance of SNA lies in its ability to uncover structural features and node centrality within complex systems, offering critical insights for academic research and real-world decision-making. Consequently, it has been widely adopted in studies related to technology innovation [22,30]. This study employs SNA to map the dynamics of PCT innovation systems through the establishment of actor collaboration networks and technology collaboration networks and analyses on the metrics of networks.

3.2. Data Collection

Patent documents were collected from the IncoPat database (https://www.incopat.com). IncoPat is one of China’s leading intellectual property databases. It aggregates over 186 million patents from 170 global authorities, including the Derwent World Patents Index, with weekly updates. Its extensive technological innovation data have significantly supported academic research [30,36].
To identify PCT patent documents, several rules are established. Firstly, the application date of patents is set to be from 1 January 2012 to 31 December 2024. The reason for this is that, in early 2012, the State Council of China issued the Outline for Quality Development (2011–2020). It is proposed to comprehensively enhance technological innovation capabilities in engineering fields, aiming to possess core technologies in critical sectors such as building construction, and progressively increasing the proportion of industrialized construction. This policy significantly catalyzed PCT innovations in China. Following its intervention, PCT-based technological innovations have progressively and substantially developed. Therefore, this study takes 2012 as the starting point for PCT dynamics analysis. Furthermore, to better elucidate technological innovation trends, complete calendar years were selected for analysis to make dynamic patterns more discernible; secondly, a wide range of keywords are selected for searching. Since PCT in China experienced different development stages of distinguished targets, objects, and tools [6], the expressions of PCT are not unified across different phases. Therefore, a set of keywords were employed representing all focuses of historical PCT development in China, i.e., prefabricated, prefabrication, industrialized, industrialization, modular, module, component, building, residential building; thirdly, to accurately retrieve patent data related to building construction, patents not containing the IPC classification “E04” (Building Construction) were excluded. Ultimately, a total of 6047 patent records were obtained. The data collection procedures are summarized as follows:
(1) The search query was constructed as TIAB = (“prefabricated” OR “prefabrication” OR “industrialized” OR “industrialization” OR “modular” OR “module” OR “component”) AND (“building” OR “residential building”).
(2) The search was constrained to Chinese invention patents with granted legal status, limited to active patents filed between 1 January 2012 and 31 December 2024.
(3) Subsequent filtering excluded patents not containing the IPC classification “E04”, resulting in a finally refined dataset.

3.3. Data Analysis

Patent data comprise multiple fields, e.g., title, abstract, applicant, applicant type, International Patent Classification (IPC) code, and legal status. Among them, the IPC code serves as a critical indicator of technology domains.
Application date, applicant, applicant type, applicant province/municipality, and primary IPC code are taken to conduct basic statistical analyses of PCT innovation actors, i.e., the geographical distribution of PCT innovation actors, the types of PCT innovation actors, and the top 15 PCT innovation actors, and PCT technologies, i.e., temporal distribution of PCT patent applications, technological classifications of PCT patents.
The co-occurrence relationships among applicants are utilized to construct collaboration networks of innovation actors. The co-occurrence relationships among all IPC subclasses are used to build technology collaboration networks. Network metrics analyses, i.e., average, degree, average weighted degree, graph density, modularity, average clustering coefficient, are performed to reveal collaborative relationships among PCT innovation actors and PCTs. Node metrics as degree, weighted degree, closeness centrality, betweenness centrality, and eigenvector centrality are also analyzed to locate actors and technology domains in networks. For node clustering in networks, the modularity optimization algorithm employed is the Louvain Method, a greedy heuristic approach designed to maximize modularity through an iterative process of local node movement and community aggregation. Gephi 0.10.1 is used to visualize the results, and distinct colors are automatically assigned to different communities to facilitate visual differentiation and analysis of network modular structures [6]. The importance of a node is quantified by its weighted degree. Weighted degree is defined as the sum of weights across all edges connected to the node. This metric is applicable to undirected graphs and typically reflects a node’s total interaction strength [37]. Notably, a node with fewer but stronger connections may exhibit a higher weighted degree than a node with numerous weak connections. Finally, by examining temporal trends in patent applications, PCT innovation developments are divided into distinct phases. For each phase, collaboration networks of innovation actors and technology collaboration networks are specifically constructed. The evolutionary trajectory of the PCT innovation system is subsequently revealed through analyses of network property changes.
The research framework is shown in Figure 1.

4. Results

4.1. Framework of PCT Innovation System

4.1.1. PCT Innovation Actors

  • Geographical distribution of PCT innovation actors
As illustrated in Figure 2, the spatial distribution of PCT patent applicants exhibits significant regional disparities. Coastal areas in eastern China demonstrate a pronounced advantage. Jiangsu, Beijing, and Guangdong collectively account for 34.3% of the national total, forming the core hubs of PCT innovation. Among these, Jiangsu, one of the earliest provinces to promote PCT in China, has maintained a high level of patent activity in prefabricated component production, standardized design, and modular construction techniques. Guangdong, Jiangsu, Beijing, Zhejiang, and Shanghai have consistently ranked among the top regions in terms of patent applications, establishing three major PCT innovation clusters: the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region. These areas benefit from strong economic foundations, high industrial agglomeration, and abundant resources in higher education and research institutions, ensuring robust technological supply. Additionally, local governments in these regions have implemented early policy incentives to support PCT development, such as designating prefabricated building demonstration cities, establishing PCT industrial parks, and introducing financial subsidies, thereby fostering a favorable institutional environment for the PCT innovation activities of enterprises. In recent years, PCT innovations in central and western China have shown increasing momentum, with notable growth in patent applications in Sichuan, Chongqing, Hunan, Hubei, and Henan. Driven by national strategies such as the Western Development and Rise of Central China initiatives, these regions have seen increased infrastructure investment and growing governmental emphasis on PC. Through policy support and industrial guidance, they have effectively stimulated regional innovations.
  • Types of PCT innovation actors
The composition of PCT patent applicants is illustrated in Figure 3. Enterprises account for the dominant share at 68%. This proportion not only underscores the strong PCT leadership of enterprises but also reflects the market-driven nature of current PCT innovation. Enterprises possess the capacity to rapidly industrialize technological advancements, and their sustained investment in key technologies has resulted in a highly concentrated patent filing landscape. Universities constitute 22% of applicants. Although significantly lower than enterprise representation, their notable presence in patent data highlights their continued role in knowledge production within the construction sector. Detailed information of patents reveals that universities primarily focus on fundamental or cutting-edge research, including structural performance optimization (e.g., CN109667351B: Tooth-groove waterproof connection structure for horizontal joint grouting in prefabricated building wall panels), novel material development (e.g., CN118851719B: High-performance composite aerogel structural material for foldable prefabricated buildings: preparation method and applications), and integrated energy-efficient design (e.g., CN111910832B: An energy-saving thermal insulation wall for prefabricated buildings and its fabrication and assembly method). Some universities collaborate with industry partners through research platforms to facilitate technology transfer. However, the predominance of independent university filings suggests that industry–academia collaboration has yet to become institutionalized. In contrast, individual applicants represent only 5% of filings. Despite their limited share, their contributions in niche technical areas remain noteworthy. Independent inventors, often drawing from hands-on construction experience, have proposed practical solutions in areas such as connection detailing and joint simplification (e.g., CN111197360B: A prefabricated wall connection joint and its construction method). While these innovations are typically experience-based and lack theoretical depth, they demonstrate strong applicability. Nevertheless, without institutional support or commercialization channels, many of these inventions remain confined to the patent stage, limiting their broader impact.
Joint applications exhibit a fragmented distribution. Co-filings between enterprises and universities account for a modest 4%, indicating nascent but underdeveloped collaboration trends. Such partnerships are often driven by research programs, rather than market-led innovation. Voluntary industry–academia cooperation remains rare in self-initiated patent applications. Other joint filing categories—such as collaborations between research institutes and government agencies, or enterprises and research institutes—collectively represent less than 1% of patents. While research institutes should theoretically play a pivotal role in foundational breakthroughs, constraints in resource allocation and technology transfer mechanisms have resulted in limited PCT patent output.
  • Top 15 PCT innovation actors
Figure 4 presents the Top 15 PCT patent applicants. Beijing University of Technology emerges as the undisputed leader, with 113 patents, followed closely by Zhejiang Yasha Decoration Co., Ltd. (Zhejiang, China) (105 patents) and Southeast University (68 patents). Beijing University of Technology’s substantial patent portfolio reflects its strong PCT innovation capabilities; further patent data analysis indicates its advantages in component prefabrication (e.g., CN113216510B: A precast concrete component with ECC tube-embedded grouting sleeve connection), PC connection joints (e.g., CN107859196B: A prefabricated energy-dissipation joint with self-resetting and replaceable functions), and integrated construction processes (e.g., CN111809764B: Prefabricated self-regulating energy-saving PCM wall panel and its construction installation method), which are outcomes of its long-term research focus on PC. In contrast, Zhejiang Yasha Decoration, as a leading private enterprise, shows remarkable market-driven PCT innovation capacity, with patents concentrated in practical areas, such as modular interior systems (e.g., CN110284627B: A modular prefabricated interior partition wall system and its assembly process) and dry connection techniques (e.g., CN112554478B: A modular anti-seismic connecting component for structural applications) that align with China’s PC promoting policies, reflecting the effective collaboration between policy guidance and market reactions. The third to sixth ranked applicants are Southeast University, China Seventeenth Metallurgical Group Co., Ltd. (Anhui, China), Xi’an University of Architecture and Technology, and Shenyang Jianzhu University, respectively. These academic institutions and state-owned enterprises share common characteristics of strong research accumulation and engineering capabilities. As top universities in architectural disciplines, both Southeast University and Xi’an University of Architecture and Technology have produced significant PCT innovations, primarily focusing on the mechanical performance of composite components (e.g., CN113719152B: Externally confined prestressed precast concrete reinforcement structure for steel components and its construction method) and the optimization of PC shear wall systems (e.g., CN111749351B: Self-centering energy-dissipative connection device for prefabricated shear walls). These PCT innovation directions closely align with China’s R&D priorities for the enhanced performance of PC structures. In contrast, patents of China Seventeenth Metallurgical Group concentrate primarily on on-site assembly techniques (e.g., CN106836777B: Prefabricated collapsible working platform for external wall installation and its erection method). This patent portfolio demonstrates the enterprise’s strategic emphasis on innovation at the construction implementation.
Notably, universities account for half of the top 10 applicants, confirming their dual role as both theoretical research centers and important sources of PCT innovation. Since the implementation of the National Medium- and Long-Term Education Reform and Development Plan (2010–2020), many universities have generated batches of core PCTs and accelerated technology transfer through industry collaboration. Among enterprise applicants, Gold Mantis Fine Decoration Technology (Suzhou) Co., Ltd. (Suzhou, China); Beijing New Building Material Group Co., Ltd. (Beijing, China); and China Construction Eighth Engineering Division Corp., Ltd. (Shanghai, China) have also achieved significant patent outputs. These enterprises typically possess rich practical experience in construction methods, joint optimization, and structural connection systems. For instance, the patents of China Construction Eighth Engineering Division cover the entire prefabricated construction process from structural systems (e.g., CN115162775B: Precast lateral load-resisting braced frame structure and its erection method) to transportation and hoisting techniques (e.g., CN103541556B: Leveling installation method for PC precast components in prefabricated residential buildings), demonstrating its strength in comprehensive and integrated whole-process PCT innovation. Harbin Institute of Technology, Qingdao University of Technology, and Jilin Jianzhu University have developed specialized technologies adapted to local conditions. For example, Harbin Institute of Technology has strategically incorporated cold-region PCTs (e.g., CN109138225B: Embedded cross-plate interconnection structure for prefabricated architectural framing systems) into its long-term research agenda, responding to national urbanization policies advocating localized prefabricated building.
The ranking reveals that PCT innovation is driven by multiple stakeholders, e.g., universities provide theoretical support, enterprises promote technological application, and research institutions supplement technical depth. They collaboratively form a complete synergistic system from basic research to engineering practice. This structural configuration represents a concrete manifestation of the industry–university–research integration that has been consistently emphasized in China’s technological innovations in recent years.

4.1.2. PCT Domains

  • Temporal distribution of PCT patent applications
The temporal distribution of PCT patent applications is presented in Figure 5. Prior to 2016, the annual patent filing volume is relatively small. A rapid growth commenced in 2016, with annual applications increasing steadily. The peak occurred in 2021, followed by a declining trend in subsequent years.
From 2016 to 2021, patent applications demonstrated sustained annual growth for six consecutive years. Notably, 2019–2021 witnessed an accelerated surge, with annual filings jumping from 528 to 1252. This phase represented both an intensive PCT innovation boom and a heightened industry focus on core processes, critical joints, and standardized components. A substantial proportion of patents during this period primarily concentrated on component connection techniques (e.g., CN111980173B: Modular beam-column joint connection structure for prefabricated buildings), integrated construction process optimization (e.g., CN111549930B: Prefabricated building assembly process control system), and intelligent prefabricated building systems (e.g., CN112355958B: An intelligent prefabricated building). These trends marked the transition of PC into an era of integrated and intelligent development.
The peak appeared in 2021. It is not only the outcome of widespread industry and academic engagement with PCTs but also the results of policy incentives. Guidelines on Vigorously Promoting Prefabricated Buildings was issued by China’s State Council in 2016, established a goal of improving the ratio of total prefabrication area of new building projects to 30% within 10 years, serving as a critical policy milestone that significantly stimulated PCT innovation activities.
However, the post-2021 decline warrants careful consideration. Patent filings dropped to 992 in 2022 and plummeted to 348 in 2023 and 343 in 2024. One of the possible reasons for the downturn is that China is still in its preliminary stage of PCT development [10]; although a boom appears after a few years of policy incentive, its further promotion is constrained by various types of challenges, such as high cost, lack of building codes and standards, and the complexity of specific technologies [10,38].
The development of PCT in China can be divided into three phases that accord with the number of granted patent applications, i.e., the emerging phase P2012–2015, the developing phase P2016–2021, and the maturity phase P2022–2024. In Section 4.3, the evolution of PCT innovation systems is analyzed across these three phases.
  • Technological classifications of PCT patents
As shown in Table 1, the top 10 primary IPC codes collectively account for 5947 patents, representing 98.35% of the total. It demonstrates a concentrated innovation and clear dominant trends of specific PCTs. Notably, E04 patents reach 5651, constituting 95.02% of the total. This indicates an exceptionally high degree of technological concentration, revealing that current PCT innovations in China are predominantly focused on building structure-related technologies. In particular, active developments can be found in prefabricated component design (e.g., CN110761449B: A fastener for fixing wall panels and profiles in prefabricated building components), joint construction (e.g., CN112196107B: A precast steel-reinforced concrete primary–secondary beam connection joint), and assembly techniques (e.g., CN112144695B: A prefabricated wall and its installation method). The objectives of standardization, modularization, and construction efficiency improvement remain central to advancing PC. Consequently, the predominant position of building structure-related patents reflects both the progression of PCT development and the combined influence of policy guidance and market orientation.
B66 and E02 patents, while relatively limited in quantity, demonstrate significant practical application value through their construction-focused technical functions. The 64 B66 patents (1.08%) primarily cover lifting equipment and hoisting techniques (e.g., CN118701946B: A hoisting device for prefabricated building wall panels), directly serving component transportation (e.g., CN113636489B: An adaptive adjustment transport platform for prefabricated floor slabs), lifting (e.g., CN111661768B: A hoisting method and system for multiple peripheral components of prefabricated buildings), and on-site positioning (e.g., CN113697687B: A rapid hoisting method and positioning fixture for underground multi-layer double-T slab structures), offering essential support for efficient assembly construction. The 57 E02 patents (0.96%) mainly concentrate on adaptive foundation structure designs (e.g., CN112227407B: A connection system and method for precast laminated foundations and precast concrete columns), highlighting the critical role of foundation engineering as prerequisites for component installation. With the increasing level of prefabrication, optimized foundation technologies become crucial connection of traditional construction methods with modern prefabrication techniques.
Although representing a small proportion (36 patents, 0.61%), G06 patents related to information-based construction possess considerable strategic value in BIM platform development (e.g., CN112487520B: A BIM-based modeling and construction method for prefabricated steel structures) and construction management system establishment (e.g., CN109857988B: A safety monitoring method for modern prefabricated timber buildings in cold regions). These patents indicate an emerging trend toward comprehensive digital collaboration in PCT. As digital construction concepts continue to develop, this direction shows strong potential to become a significant growth area in the future.
Regarding PC materials and processes, B28 and C04 contain 32 and 31 patents, respectively, accounting for 0.54% and 0.52%. These technologies focus on concrete forming (e.g., CN108638322B: A concrete pouring method for precast columns in prefabricated buildings) and inorganic binding material innovations (e.g., CN113998979B: An inorganic welding agent, preparation method, and prefabricated building connection method), closely aligning with the objectives of component performance improvement and energy efficiency. Under the carbon-related policies, the green and low-carbon direction represented by these material patents demonstrates promising development prospects and strong policy alignment.
Additionally, F24, G01, E01, and E06 collectively comprise 76 patents, slightly exceeding 1.2% of the total. Covering areas such as HVAC integration (e.g., CN110160156B: A modular ceiling for prefabricated radiant air conditioning systems), construction measurement (e.g., CN108896616B: A quality evaluation method for grout-filled sleeve splicing of rebar in prefabricated concrete structures), modular municipal facilities (e.g., CN113308952B: A construction method for lightweight embankments using foamed concrete in prefabricated bridge-head retaining walls), and envelope system assembly (e.g., CN114541629B: Prefabricated unitized curtain wall). These technology categories illustrate preliminary signs of systematic and coordinated development trends. In particular, E06 patents concerning integrated doors/windows/curtain walls and interior finishes respond to market demands under current emphasis of prefabricated buildings on integrated decoration, marking the gradual expansion of PCT focus toward functional systems and lifecycle building optimization.

4.2. Collaborations Within PCT Innovation System

4.2.1. Collaboration Network of Innovation Actors

A collaboration network of innovation actors was constructed based on applicants’ co-occurrence relationships, as illustrated in Figure 6. In this network, nodes represent patent applicants, while edges indicate that two applicants jointly applied for one patent. The thickness of edges signifies the frequencies of joint patent applications.
Metrics of the collaboration network of innovation actors are illustrated in Table 2. The exceptionally low value of graph density of 0.002 clearly indicates an extremely sparse collaboration network among patent applicants. Such remarkably limited collaboration between applicants severely constrains communications on technological information and resources, significantly reducing interactions between actors, concurrently diminishing the propagation efficiency of technological innovation information and resources. The average clustering coefficient is 0.75, strongly suggesting significant clustering among patent applicants. As shown in Figure 7, a tightly-knit collaboration group has formed around China MCC 20 Group Corp., Ltd. (Shanghai, China) as the core, encompassing numerous research institutes and enterprises. Meanwhile, universities and industry–academia collaborations have established another distinct group through academic partnerships and talent cultivation. Such a clustering structure closely correlates with the multidisciplinary characteristics of PCT innovation. Within specific domains, applicants with similar technical backgrounds and research domains exhibit more frequent collaborations. Generally, network metrics exhibit a pronounced community structure of the network with high modularity, containing multiple innovation communities. While intra-community collaborations remain intensive, facilitating in-depth PCT innovation cooperations in specialized fields, inter-community collaborations remain insufficient, limiting cross-domain innovations.
Network metrics for the top 10 nodes by weighted degree are illustrated in Table 3. Central Research Institute of Building and Construction Co., Ltd. MCC Group (Beijing, China) occupies the central position of the network with the highest weighted degree and degree centrality. A high weighted degree indicates prominent comprehensive importance within the network, reflecting both extensive connections and strong collaboration intensity. Other key nodes include universities, e.g., Xi’an University of Architecture and Technology, and enterprises, e.g., China Shipbuilding Industry Group Co., Ltd. (Beijing, China) Universities, aggregating research talents and theoretical research resources, promoting industry–academia integration through joint patent applications, and providing novel methods and theoretical support for PCT innovation.
Regarding betweenness centrality, Central Research Institute of Building and Construction Co., Ltd. MCC Group (Beijing, China) achieves an exceptionally high value of 9485.08, confirming its position on critical network paths. It dominates in technical standard formulation and key technology development, serving as the core driver of PCT innovation. Conversely, small enterprises exhibit near-zero betweenness centrality. They play negligible roles in the information transmission of PCT innovation and predominantly rely on core nodes for PCT knowledge access.

4.2.2. Technology Collaboration Network

A technology collaboration network was constructed based on IPC subclass co-occurrence relationships, as illustrated in Figure 7. In this network, nodes represent IPC subclasses, while edges indicate that two IPC subclasses appear in the same patent. The thickness of edges signifies the frequencies of co-occurrence.
Network attributes are illustrated in Table 4. The graph density of merely 0.05 reveals a relatively sparse network structure, where actual connections constitute only a small fraction of all connections. This suggests that collaborative relationships among technologies have not yet achieved extensive coverage. Such low-density characteristics align with current PCT development that features numerous technical categories, widely distributed actors, and significant professional barriers. The network’s average clustering coefficient reaches 0.803, indicating a high-level local connectivity. It typically correlates with frequent interactions within small groups, effectively facilitating the rapid integration of local innovation resources. PCT innovation collaborations of technologies demonstrate distinct small-world characteristics, forming highly cohesive innovation clusters within specific technical domains. Composite metrics reveal that core nodes in the technology collaboration network exhibit high degree and weighted degree centrality, playing dominant roles in PCT innovation. However, the overall network sparsity and low modularity suggest room for improvement in cross-domain technology collaboration.
Network metrics for the top 10 nodes by weighted degree are illustrated in Table 5. E04B demonstrates exceptional network prominence, with a weighted degree of 4211, significantly surpassing other nodes. Weighted degree, as an indicator of collaboration intensity between a node and others, typically reflects strong technological integration capability or extensive partnership when achieving high values. E04G, E04C, and E04H also exhibit relatively high weighted degrees, constituting important PCT collaboration domains. This reflects concentrated PCT innovation activities, with technological resources primarily converging around a few network hubs. Betweenness centrality reveals E04B’s outstanding bridging role (5552.91), indicating its critical function in facilitating cross-node communication and knowledge flow within the network. Although E04G and E04C also demonstrate certain intermediary capabilities, their influence remains substantially weaker compared to E04B. Composite metrics indicate that core nodes represented by E04B feature dense connections and high centrality values, displaying distinct technological dominance. In contrast, nodes with lower weighted degree and centrality (e.g., E02D, B28B) demonstrate weaker collaboration and influence. While such a structure may enhance innovation efficiency in core regions, it could potentially limit PCT development in peripheral areas, possibly affecting the diversity and vitality of the entire technology collaboration network.

4.3. Evolution of PCT Innovation System

4.3.1. Dynamics of Actor Collaboration Network

The actor collaboration networks across three phases are illustrated in Figure 8. In P2012–2015, large state-owned enterprise, e.g., State Grid Corporation of China, dominated as major nodes. In P2016–2021, universities, e.g., Xi’an University of Architecture and Technology, and research institutes, e.g., Central Research Institute of Building and Construction Co., Ltd. MCC Group (Beijing, China), emerged as key players. Inter-enterprise collaborations became more prevalent, leading to greater diversity among PCT innovation actors. In P2022–2024, the distribution of core nodes among universities, private enterprises, and state-owned enterprise became more balanced, with an increase in the number of key organizations. Such evolution results in a multi-centric, multi-actor collaboration system of PCT innovation.
Metrics of actor collaboration networks across three phases are shown in Table 6. The rise in modularity values indicates that, as the innovation system expanded, the network developed more distinct community structures. Changes in graph density reflect shifts in collaboration breadth. P2012–2015 exhibited low density, with sparse connections concentrated among a limited number of PCT innovation actors. In P2016–2021, density decreased, signaling that, while the network expanded significantly, the relative connectivity per node declined. This indicates broader but less concentrated actor collaboration. In P2021–2024, density rebounded slightly, suggesting that, while maintaining large-scale connectivity, the network collaboration of PCT innovation actors decreased. The evolution of bridging nodes is captured through changes in the average clustering coefficient and network diameter. The high clustering coefficient in P2012–2015 indicates tightly knit groups but limited connectivity. In P2016–2021, the clustering coefficient declined while the network diameter increased, highlighting enhanced inter-group linkages facilitated by brokerage nodes. In P2022–2024, the clustering coefficient modestly recovered, and the network diameter continued to grow. This demonstrates that, as the network scaled, connectivity became more complex, with bridging nodes further enabling cross-community knowledge flows. The sustained growth in both average degree and weighted degree underscores the deepening of PCT actor collaborations, reflecting more frequent and substantive interactions among PCT innovation actors.

4.3.2. Dynamics of Technology Collaboration Network

The technology collaboration networks across three phases are illustrated in Figure 9. In P2012–2015, technology collaborations primarily focused on traditional PCTs, including enhanced prefabricated building structures and prefabricated component production. E04B (general building structures) and E04C (building materials/components) that concentrated on existing process enhancements are core technology domains. P2016–2021 witnessed the integration of automation control technologies, evidenced by frequent appearances of B29C (plastics processing) and G05B (control systems) in technology collaborations, marking the gradual incorporation of intelligent production techniques into PCT. In P2022–2024, emerging green construction and building information techniques led to collaborations between traditional PCTs and diverse technology domains like B08B (cleaning/maintenance) and F24F (ventilation/air conditioning), demonstrating technological diversification and convergence.
Metrics of the technology collaboration network across three phases are presented in Table 7. The average degree indicates a continuous expansion of technology collaboration scope. In P2012–2015, the relatively loose connections of nodes suggest limited collaboration of different technology domains. P2016–2021 shows significant average degree increases, reflecting enhanced technology collaborations and accelerated integration. Although slightly decreased, P2022–2024 maintains a high average degree, with the network continuing to expand while gradually concentrating around key technology domains. Graph density changes further validate the evolutionary trajectory. The trend suggests that, while network scale expanded with new technology domains, connections between nodes decreased, indicating technology collaboration concentration toward core domains despite quantitative increases. Changes of modularity demonstrate a consistent decline across three phases, revealing progressively broken boundaries between originally segregated technology domains, and increasingly frequent cross-domain technology collaborations. The lowest modularity in P2022–2024 particularly signifies that most technology domains have established tight collaborations, where PCT innovations are no longer driven by singular technologies but by multi-technology collaborations. The evolution of weighted degree further reveals technology collaboration intensity. The average weighted degrees increased from P2012–2015 to P2016–2021, then slightly decreased in P2022–2024, indicating substantial enhancements in both PCT domains and collaboration intensity. Although the involvement of emerging technologies caused decline in P2021–2024, sustained high-intensity collaboration among core domains suggests that technology convergence is advancing toward efficient collaborative PCT innovations.

5. Discussion

From the view of a static system structure, the PCT innovation system exhibits a distinct core–periphery structural characteristic across both networks of actors and technologies. In the technology collaboration network, core nodes demonstrate high degree centrality and weighted degree, playing pivotal roles in driving PCT innovation; however, the overall network remains relatively sparse, indicating significant potential for enhancing the cross-domain collaborations of PCTs. The collaborative network of PCT innovation actors has formed multiple stable innovation communities with strong internal connections that facilitate in-depth PCT development within specialized domains, yet exhibits insufficient inter-community communication, suggesting the need to strengthen cross-boundary innovation.
From the view of dynamic system evolution, the actor collaboration network of PCT innovation transitioned from state-owned enterprise dominance to multi-agent participation, and eventually to multi-centric and multi-actor collaboration. The network continuously expanded in scale, with collaborations evolving from localized intensity to globally integrated connectivity, thereby enhancing knowledge flow efficiency, providing robust support for sustained PCT innovation. The technology collaborations of PCT have evolved from singular technologies to multi-domain collaborations. Early-stage innovation concentrated on traditional technology domains, and then automation techniques began to promote the intelligent transformation of PCT innovation. Thereafter, digital technologies expand technology collaborations in both depth and breadth. The evolution has not only elevated PCT innovation progress but has also fostered the multidimensional development of the PCT innovation system.
Generally, China’s PCT innovation system has essentially formed a complex network characterized by multi-actor and multi-technology collaboration. It plays a significant role in promoting continuous PCT innovation and yields abundant PCT innovation outcomes. However, challenges still exist in insufficient cross-domain collaboration and constrained innovation resource flows. Future efforts should focus on strengthening interdisciplinary collaborations to optimize technological resource allocation, refine policy guidance mechanisms, and enhance the system’s overall collaborative innovation capacity for providing more robust support for high-quality PCT development.
The findings of this study can be further compared with existing pieces of literature to identify potential discrepancies between actual technological innovation practices and academic studies [35]. Recently, a few academic researches reviewed progress in the body of knowledge in prefabricated construction [39], the relationship between stakeholders in prefabricated construction [40], and the advancement of management in prefabricated construction [10], incorporating discussions on PCT progress. It can be concluded that, for PCT innovation actors, the entities have evolved from a single-entity model toward an industry–university–research collaborative paradigm, facilitating the advancement of PCT [10], which aligns with the findings of this study. This study further demonstrates the evolution of collaboration patterns in PCT innovation practices. However, both practical and academic studies declare the necessity of improved collaborations between stakeholders, which currently constrains PCT development [40]. For technologies, it is proposed that PCT in China is still in its preliminary development phase [10,39], with a key focus on basic technologies such as structural design, product production optimization, and on-site hosting and assembly, with digital technologies found to be a future focus but with slow advancement [10,39]. This corroborates the results of IPC analysis in this study that E04 is the predominant technical domain, in which G06 represents a small proportion. It is also indicated that, as PCTs advance, a growing body of studies has emerged focusing on enhancing the integration of technologies [10], which is consistent with the findings of this study regarding closer collaborative relationships among technical domains.

6. Conclusions

This study systematically analyzed 6047 valid granted invention patents related to PCTs in China from 2012 to 2024. Using basic statistical methods, the framework of PCT innovation systems was described from dual dimensions, such as innovation actors and technology domains, including distribution patterns, key actors, and major technological fields. Through SNA, actor collaboration networks were constructed based on applicant co-occurrence relationships, and a technology collaboration network was constructed based on IPC co-occurrence relationships. By examining network and node metrics, structural features and node roles were revealed. Finally, temporal analysis of network metrics uncovered the evolution of the PCT innovation system.
Through comparative analysis between the findings of this study and prior academic literatures, both convergences and divergences regarding PCT innovation actors and technological trends were identified between scholarly research and practical applications. Such validation further substantiates the methodological rationale of employing patent data to characterize the technological innovation system dynamics of this study. The theoretical implications of this study are two-fold: First, a comprehensive, data-driven framework for analyzing PCT innovation systems from the perspectives of both actor (innovation subjects) and technology (innovation objects) was established, addressing prior research limitations that focused on either dimension in isolation; Second, a methodological framework for studying static structures and dynamic evolution of innovation system using patent data was proposed, offering a replicable approach for related technology innovation studies.
The empirical implications of this study are also two-fold: First, the findings of this study may empower PCT innovation actors to identify hotspot technologies, key domains, and their strategic positioning, thereby guiding PCT innovation direction and resource allocation for more efficient PCT innovation endeavors; Second, the insights of key actors, significant technology domains, and bottlenecks in the PCT innovation system may inform policymakers to facilitate targeted policy instrument for further PCT innovation.
The limitation of this study lies in its reliance solely on IPC classes in the analysis of technologies. While IPC classes provide well-structured information of technology domains, they fail to capture nuanced details of technologies. To achieve a more comprehensive understanding of technological specifics, future research should incorporate content analysis tools to examine the substantive aspects of the technologies in greater depth.

Author Contributions

Conceptualization, Y.W.; methodology, Y.W. and A.H.; software, A.H.; formal analysis, A.H.; data curation, A.H.; writing—original draft preparation, A.H. and Y.W.; writing—review and editing, Y.W.; supervision, Y.D.; funding acquisition, Y.W. and Y.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research is supported by the National Natural Science Foundation of China (NO. 72401074 and NO. 72101044), Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515110461), and Natural Science Foundation of Liaoning Province (NO. 2023-BSBA-026).

Data Availability Statement

The dataset used in this study is publicly available. To ensure appropriate use of the data, please contact the corresponding author via email (douyudan@dlut.edu.cn) to request access. Upon receiving the request and confirming its reasonable purpose, the corresponding author will provide access to the relevant data.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hosseini, M.R.; Martek, I.; Zavadskas, E.K.; Aibinu, A.A.; Arashpour, M.; Chileshe, N. Critical Evaluation of Off-Site Construction Research: A Scientometric Analysis. Autom. Constr. 2018, 87, 235–247. [Google Scholar] [CrossRef]
  2. Goh, M.; Goh, Y.M. Lean Production Theory-Based Simulation of Modular Construction Processes. Autom. Constr. 2019, 101, 227–244. [Google Scholar] [CrossRef]
  3. Ping, T.; Pan, W.; Zhang, Z. Developing a Systematic Performance Measurement Framework for Benchmarking Steel Modular Building Construction. Eng. Constr. Archit. Manag. 2023, 32, 1837–1856. [Google Scholar] [CrossRef]
  4. Ho, C.; Kim, Y.W.; Zabinsky, Z.B. Prefabrication Supply Chains with Multiple Shops: Optimization for Job Allocation. Autom. Constr. 2022, 136, 104155. [Google Scholar] [CrossRef]
  5. Pervez, H.; Ali, Y.; Petrillo, A. A Quantitative Assessment of Greenhouse Gas (GHG) Emissions from Conventional and Modular Construction: A Case of Developing Country. J. Clean. Prod. 2021, 294, 126210. [Google Scholar] [CrossRef]
  6. Wang, Y.; Xue, X.; Yu, T.; Wang, Y. Mapping the Dynamics of China’s Prefabricated Building Policies from 1956 to 2019: A Bibliometric Analysis. Build. Res. Inf. 2020, 49, 216–233. [Google Scholar] [CrossRef]
  7. Jin, R.; Gao, S.; Cheshmehzangi, A.; Aboagye-Nimo, E. A Holistic Review of Off-Site Construction Literature Published between 2008 and 2018. J. Clean. Prod. 2018, 202, 1202–1219. [Google Scholar] [CrossRef]
  8. Luo, T.; Xue, X.; Wang, Y.; Xue, W.; Tan, Y. A Systematic Overview of Prefabricated Construction Policies in China. J. Clean. Prod. 2021, 280, 124371. [Google Scholar] [CrossRef]
  9. Li, T.; Li, Z.; Dou, Y. Diffusion Prediction of Prefabricated Construction Technology under Multi-Factor Coupling. Build. Res. Inf. 2023, 51, 333–353. [Google Scholar] [CrossRef]
  10. Li, C.Z.; Li, S.; Li, X.; Wu, H.; Xiao, B.; Tam, V.W.Y.; Asiedu-Kwakyewa, C. A Scientometric Review of Management of Prefabricated Construction from 2011–2021. Buildings 2022, 12, 1515. [Google Scholar] [CrossRef]
  11. Dang, J.; Motohashi, K. Patent Statistics: A Good Indicator for Innovation in China? Patent Subsidy Program Impacts on Patent Quality. China Econ. Rev. 2015, 35, 137–155. [Google Scholar] [CrossRef]
  12. Bortolini, R.; Formoso, C.T.; Viana, D.D. Site Logistics Planning and Control for Engineer-to-Order Prefabricated Building Systems Using BIM 4D Modeling. Autom. Constr. 2019, 98, 248–264. [Google Scholar] [CrossRef]
  13. Mao, C.; Shen, Q.; Pan, W.; Ye, K. Major Barriers to Off-Site Construction: The Developer’s Perspective in China. J. Manag. Eng. 2015, 31, 04014043. [Google Scholar] [CrossRef]
  14. Salama, T.; Salah, A.; Moselhi, O.; Al-Hussein, M. Near Optimum Selection of Module Configuration for Efficient Modular Construction. Autom. Constr. 2017, 83, 316–329. [Google Scholar] [CrossRef]
  15. Liu, G.; Li, K.; Zhao, D.; Mao, C. Business Model Innovation and Its Drivers in the Chinese Construction Industry during the Shift to Modular Prefabrication. J. Manag. Eng. 2017, 33, 04016051. [Google Scholar] [CrossRef]
  16. Gasparri, E.; Aitchison, M. Unitised Timber Envelopes. A Novel Approach to the Design of Prefabricated Mass Timber Envelopes for Multi-Storey Buildings. J. Build. Eng. 2019, 26, 100898. [Google Scholar] [CrossRef]
  17. Du, H.; Huang, P.; Jones, P. Modular Facade Retrofit with Renewable Energy Technologies: The Definition and Current Status in Europe. Energy Build. 2019, 205, 109543. [Google Scholar] [CrossRef]
  18. Pan, M.; Pan, W. Determinants of Adoption of Robotics in Precast Concrete Production for Buildings. J. Manag. Eng. 2019, 35, 05019007. [Google Scholar] [CrossRef]
  19. Li, X.; Wu, L.; Zhao, R.; Lu, W.; Xue, F. Two-Layer Adaptive Blockchain-Based Supervision Model for off-Site Modular Housing Production. Comput. Ind. 2021, 128, 103437. [Google Scholar] [CrossRef]
  20. Benjaoran, V.; Dawood, N. Intelligence Approach to Production Planning System for Bespoke Precast Concrete Products. Autom. Constr. 2006, 15, 737–745. [Google Scholar] [CrossRef]
  21. Xue, X.; Zhang, X.; Wang, L.; Skitmore, M.; Wang, Q. Analyzing Collaborative Relationships among Industrialized Construction Technology Innovation Organizations: A Combined SNA and SEM Approach. J. Clean. Prod. 2018, 173, 265–277. [Google Scholar] [CrossRef]
  22. Dou, Y.; Xue, X.; Wu, C.; Luo, X.; Wang, Y. Interorganizational Diffusion of Prefabricated Construction Technology: Two-Stage Evolution Framework. J. Constr. Eng. Manag. 2020, 146, 04020114. [Google Scholar] [CrossRef]
  23. Zhang, W.; Jiang, Y.; Zhang, W. Capabilities for Collaborative Innovation of Technological Alliance: A Knowledge-Based View. IEEE Trans. Eng. Manag. 2021, 68, 1734–1744. [Google Scholar] [CrossRef]
  24. Kaplan, S.; Vakili, K. The Double-Edged Sword of Recombination in Breakthrough Innovation. Strateg. Manag. J. 2015, 36, 1435–1457. [Google Scholar] [CrossRef]
  25. Laursen, K.; Salter, A. Open for Innovation: The Role of Openness in Explaining Innovation Performance among U.K. Manuf. Firms. Strateg. Manag. J. 2006, 27, 131–150. [Google Scholar] [CrossRef]
  26. Vakili, K.; Zhang, L. High on Creativity: The Impact of Social Liberalization Policies on Innovation. Strateg. Manag. J. 2018, 39, 1860–1886. [Google Scholar] [CrossRef]
  27. De Faria, P.; Lima, F.; Santos, R. Cooperation in Innovation Activities: The Importance of Partners. Res. Policy 2010, 39, 1082–1092. [Google Scholar] [CrossRef]
  28. Zeng, S.X.; Xie, X.M.; Tam, C.M. Relationship between Cooperation Networks and Innovation Performance of SMEs. Technovation 2010, 30, 181–194. [Google Scholar] [CrossRef]
  29. Carayannis, E.G.; Grigoroudis, E.; Campbell, D.F.J.; Meissner, D.; Stamati, D. The Ecosystem as Helix: An Exploratory Theory-Building Study of Regional Co-Opetitive Entrepreneurial Ecosystems as Quadruple/Quintuple Helix Innovation Models. R D Manag. 2018, 48, 148–162. [Google Scholar] [CrossRef]
  30. Xue, X.; Tan, X.; Ji, A.; Xue, W. Measuring the Global Digital Technology Innovation Network in the Construction Industry. IEEE Trans. Eng. Manag. 2024, 71, 11138–11165. [Google Scholar] [CrossRef]
  31. Wu, H.; Shen, G.; Lin, X.; Li, M.; Zhang, B.; Li, C.Z. Screening Patents of ICT in Construction Using Deep Learning and NLP Techniques. Eng. Constr. Archit. Manag. 2020, 27, 1891–1912. [Google Scholar] [CrossRef]
  32. Pan, X.; Zhong, B.; Wang, X.; Xiang, R. Text Mining-Based Patent Analysis of Bim Application in Construction. J. Civ. Eng. Manag. 2021, 27, 303–315. [Google Scholar] [CrossRef]
  33. Cuellar, S.; Grisales, S.; Castaneda, D.I. Constructing Tomorrow: A Multifaceted Exploration of Industry 4.0 Scientific, Patents, and Market Trend. Autom. Constr. 2023, 156, 105113. [Google Scholar] [CrossRef]
  34. Xie, H.; Xin, M.; Lu, C.; Xu, J. Knowledge Map and Forecast of Digital Twin in the Construction Industry: State-of-the-Art Review Using Scientometric Analysis. J. Clean. Prod. 2023, 383, 135231. [Google Scholar] [CrossRef]
  35. Wang, G.; Zhou, Y.; Cao, D. Artificial Intelligence in Construction: Topic-Based Technology Mapping Based on Patent Data. Autom. Constr. 2025, 172, 106073. [Google Scholar] [CrossRef]
  36. Hou, J.; Tang, S.; Zhang, Y. A Novel Technology Life Cycle Analysis Method Based on LSTM and CRF. Scientometrics 2024, 129, 1173–1196. [Google Scholar] [CrossRef]
  37. CDA: A Clustering Degree Based Influential Spreader Identification Algorithm in Weighted Complex Network-All Databases. Available online: https://webofscience.clarivate.cn/wos/alldb/full-record/WOS:000430792700013 (accessed on 13 June 2025).
  38. Hwang, B.G.; Shan, M.; Looi, K.Y. Key Constraints and Mitigation Strategies for Prefabricated Prefinished Volumetric Construction. J. Clean. Prod. 2018, 183, 183–193. [Google Scholar] [CrossRef]
  39. Luo, T.; Xue, X.; Tan, Y.; Wang, Y.; Zhang, Y. Exploring a Body of Knowledge for Promoting the Sustainable Transition to Prefabricated Construction. Eng. Constr. Archit. Manag. 2021, 28, 2637–2666. [Google Scholar] [CrossRef]
  40. Hu, X.; Chong, H.-Y.; Wang, X.; London, K. Understanding Stakeholders in Off-Site Manufacturing: A Literature Review. J. Constr. Eng. Manag. 2019, 145, 03119003. [Google Scholar] [CrossRef]
Buildings 15 02300 g001
Figure 1. Research framework.
Figure 1. Research framework.
Buildings 15 02300 g001
Buildings 15 02300 g002
Figure 2. Geographical distribution heatmap of PCT innovation actors.
Figure 2. Geographical distribution heatmap of PCT innovation actors.
Buildings 15 02300 g002
Buildings 15 02300 g003
Figure 3. Types of PCT innovation actors.
Figure 3. Types of PCT innovation actors.
Buildings 15 02300 g003
Buildings 15 02300 g004
Figure 4. Top 15 PCT innovation actors.
Figure 4. Top 15 PCT innovation actors.
Buildings 15 02300 g004
Buildings 15 02300 g005
Figure 5. Temporal distribution of PCT patent applications.
Figure 5. Temporal distribution of PCT patent applications.
Buildings 15 02300 g005
Buildings 15 02300 g006
Figure 6. Collaboration network of innovation actors.
Figure 6. Collaboration network of innovation actors.
Buildings 15 02300 g006
Buildings 15 02300 g007
Figure 7. Technology collaboration network of PCT innovation.
Figure 7. Technology collaboration network of PCT innovation.
Buildings 15 02300 g007
Buildings 15 02300 g008
Figure 8. Dynamics of actor collaboration network.
Figure 8. Dynamics of actor collaboration network.
Buildings 15 02300 g008
Buildings 15 02300 g009
Figure 9. Dynamics of technology collaboration network.
Figure 9. Dynamics of technology collaboration network.
Buildings 15 02300 g009
Table 1. Top 10 primary IPC classes.
Table 1. Top 10 primary IPC classes.
IPC ClassDescriptionNumber of PatentsPercentage
E04Building structures (e.g., prefabricated components, prefabricated walls, connection joints)—Representing the core PCT domain.565195.02%
B66Lifting/Hoisting equipment (e.g., tower cranes, elevators, construction lifts)—Supporting component hoisting and positioning during assembly processes.641.08%
E02Earthworks and foundation engineering (e.g., subgrade, underground structures, shoring systems)—Critical for early-stage groundwork and structural support.570.96%
G06Computing and digital technologies (e.g., BIM, construction information systems, intelligent scheduling)—Providing the PC infrastructures.360.61%
B28Concrete/Gypsum/Cement processing (e.g., precast concrete production, curing techniques)—Pertaining to PC material fabrication.320.54%
C04Cement/Refractory and inorganic materials (e.g., low-carbon concrete, PC binding materials)—Innovative directions in component material.310.52%
F24HVAC and thermal systems (e.g., modular mechanical installations, integrated equipment)—Mechanical–electrical assembly techniques.230.39%
G01Measurement and inspection (e.g., assembly quality control, structural displacement monitoring)—Precision assurance methods.200.34%
E01Road construction and maintenance (e.g., prefabricated pavement, modular urban infrastructure)—Prefabricated municipal engineering applications.170.29%
E06Building envelope systems (e.g., windows/doors, curtain walls, integrated interior systems)—Prefabricated interior and exterior enclosure solutions.160.27%
Total 594798.35%
Table 2. Metrics of actor collaboration network.
Table 2. Metrics of actor collaboration network.
Average
Degree		Average Weighted DegreeGraph
Density		ModularityAverage Clustering Coefficient
2.0143.0480 0020.9480.75
Table 3. Network metrics of top 10 innovation actors by weighted degree.
Table 3. Network metrics of top 10 innovation actors by weighted degree.
NodeDegreeWeighted DegreeCloseness CentralityBetweenness CentralityEigenvector Centrality
Central Research Institute of Building and Construction Co., Ltd. MCC Group (Beijing, China)15770.24589485.08331
CSSC International Engineering Co., Ltd. (Beijing, China)9470.2129233.58330.8445
Xi’an University of Architecture and Technology (Xi’an, China)10470.2131386.83330.8679
Beijing University of Technology (Beijing, China)12380.225263940.8882
China Shipbuilding Industry Group Co., Ltd. (Beijing, China)7340.211600.7830
Central Research Institute of Building and Construction (Shenzhen) Co., Ltd. MCC Group (Shenzhen, China)4300.200712.50.2119
Southeast University (Nanjing, China)15280.180047720.4212
China Railway Construction Group Co., Ltd. (Beijing, China)10260.203563280.2870
China Jingye Engineering Co., Ltd. (Beijing, China)2230.197800.1663
State Grid Corporation of China (Beijing, China)17230.156721750.4119
Table 4. Metrics of technology collaboration network.
Table 4. Metrics of technology collaboration network.
Average
Degree		Average Weighted DegreeGraph
Density		ModularityAverage Clustering Coefficient
10.490.10 0650.0820.803
Table 5. Network metrics of top 10 IPC subclasses by weighted degree.
Table 5. Network metrics of top 10 IPC subclasses by weighted degree.
NodeDegreeWeighted
Degree		Closeness CentralityBetweenness CentralityEigenvector Centrality
E04B13442110.86415552.91031
E04G8921780.69431921.55700.8109
E04C8119280.67091606.55700.7597
E04H8216630.67371484.61640.7869
E04F685500.636976.33130.6831
E02D303480.552064.85620.4513
B28B212930.535432.35660.3189
E04D412900.5740329.09520.5318
G06F221780.537245.89330.3490
F24F341750.5599162.69890.4506
Table 6. Metrics of actor collaboration network across three phases.
Table 6. Metrics of actor collaboration network across three phases.
PhasesAverage DegreeAverage Weighted DegreeNetwork DiameterGraph DensityModularityAverage Clustering Coefficient
P2012–20151.3881.75520.0290.880.872
P2016–20211.8662.91250.0030.9540.781
P2022–20241.8732.41970.0050.9690.791
Table 7. Metrics of technology collaboration network across three phases.
Table 7. Metrics of technology collaboration network across three phases.
PhasesAverage DegreeAverage Weighted DegreeNetwork DiameterGraph DensityModularityAverage Clustering Coefficient
P2012–20154.45521.72730.2120.1540.794
P2016–20218.89962.34830.0650.0930.811
P2022–20248.03445.98330.070.0270.814
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Y.; Huang, A.; Dou, Y. Investigating Prefabricated Construction Technology Innovation Dynamics: Evidence from a Patent Analysis in China. Buildings 2025, 15, 2300. https://doi.org/10.3390/buildings15132300

AMA Style

Wang Y, Huang A, Dou Y. Investigating Prefabricated Construction Technology Innovation Dynamics: Evidence from a Patent Analysis in China. Buildings. 2025; 15(13):2300. https://doi.org/10.3390/buildings15132300

Chicago/Turabian Style

Wang, Yuna, Anqi Huang, and Yudan Dou. 2025. "Investigating Prefabricated Construction Technology Innovation Dynamics: Evidence from a Patent Analysis in China" Buildings 15, no. 13: 2300. https://doi.org/10.3390/buildings15132300

APA Style

Wang, Y., Huang, A., & Dou, Y. (2025). Investigating Prefabricated Construction Technology Innovation Dynamics: Evidence from a Patent Analysis in China. Buildings, 15(13), 2300. https://doi.org/10.3390/buildings15132300

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop