Construction of a BIM smart building collaborative design model combining the Internet of Things

Man Feng; Hanmei Wu

doi:10.1515/nleng-2024-0063

Artikel Open Access

Construction of a BIM smart building collaborative design model combining the Internet of Things

Man Feng und Hanmei Wu

Veröffentlicht/Copyright: 21. April 2025

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Aus der Zeitschrift Nonlinear Engineering Band 14 Heft 1

Abstract

The research aims to solve the problem of data synchronization and redundancy in building information model co-design with blockchain technology. A hyper-ledger fabric federated blockchain, combined with a revolving door compression algorithm, is used for the construction of an intelligent building co-design model. Experiments showed that the method outperformed other blockchain technologies in terms of throughput and response time, with block-out time reduced by 19.31% and transaction throughput increased by 12.38%. The research proposes an innovative cycle division mechanism and utilizes a revolving door compression algorithm for data maintenance of the model, thereby enhancing the security of the design data and the efficiency of collaboration. This is of positive significance for intelligent building design. However, the limitation of the study is that only the blockchain-based model is designed, and further development and example validation are needed in the future.

Keywords: BIM; IoT; collaborative design; blockchain; hyperledger fabric federated blockchain; cycle division mechanism; SDT algorithm

1 Introduction

As information technology develops, the construction industry is undergoing a profound transformation. Building information modeling (BIM) has accelerated the transformation of this change [1]. BIM mainly represents the physical and functional characteristics of buildings and infrastructure by creating digital information models. This improves design efficiency, construction quality, and intelligent operation and maintenance [2,3]. BIM has the characteristics of 3D visualization, interdisciplinary integration, rich information, design optimization, and conflict detection [4,5]. BIM has a wide range of application significance in the construction industry, project design, and other fields. However, BIM still faces problems such as information silos and low collaboration efficiency in practical applications. The existing BIM collaborative design (BIM co-design) often uses centralized methods for data storage, which can easily lead to data security issues such as loss and tampering with building data [6,7]. The Internet of Things (IoT) provides new ideas for addressing these issues. Blockchain, the most prevalent data storage method utilized in the IoT domain, has been progressively integrated into BIM co-design [8].

BIM has been extensively applied and developed in construction, which brings great opportunities and challenges to building operation and maintenance. BIM co-design, as the foundation and core of BIM collaborative operation and maintenance, has been extensively studied by scholars both domestically and internationally. Reichle et al. proposed a data processing pipeline to address the interruption of information flow for exchanging data between the construction and manufacturing industries. A closed-loop BIM co-design was achieved by utilizing the geometric design information stored in industry basic classes to convert it into product model data exchange specification data format [9]. Meng et al. developed a key technology for the 3D comprehensive design of railway engineering based on a domestic graphics engine to address the software supply interruption in BIM platforms. By constructing a BIM co-design mechanism with a hybrid mode of “central model + link,” theoretical support was provided for the development of BIM software for the entire railway and various specialties [10]. Traditional structural design methods suffer from poor energy-saving and sustainable performance, as well as low energy utilization efficiency. Wang et al. proposed a design method for large-scale building energy-saving structural systems based on the green BIM. The BIM application platform was equipped with functions such as building energy consumption monitoring, safety evacuation, and space management. Ultimately, an energy-saving sustainability coefficient of over 8 was achieved [11]. Chen et al. established a six-dimensional collaborative behavior matrix for the analysis of BIM co-design. This provided a better understanding of the collaboration conditions for BIM construction projects and identified typical collaboration situations for BIM projects. Collaboration data from different construction projects were surveyed through questionnaires, and potential feature analysis was conducted. Finally, a situation-oriented BIM co-design strategic selection framework was developed [12]. Xiao and Bhola compared BIM co-design with traditional design methods to promote BIM application in prefabricated building design. A conceptual model for the collaborative design of professional component processes was established using BIM. The effectiveness of BIM co-design in architecture was validated in parametric design tools [13].

BIM co-design often stores data through centralization, which can lead to security issues such as data loss, while blockchain can securely and reliably store data. Therefore, blockchain has been extensively applied in BIM co-design. Dounas et al. proposed a decentralized architectural design framework to address the constraints of collaboration and trust in BIM. By integrating the BIM design process with blockchain mechanisms, BIM design optimization was achieved, creating new operational and business models for architectural design [14]. Faraji et al. developed a comprehensive project delivery method to better manage construction disputes and improve construction. By generating a blockchain-based dispute management model based on blockchain review, alternative disputes for comprehensive project delivery contacts were resolved [15]. Hijazi et al. proposed a BIM single real source data model using blockchain technology to address the difficulty in identifying and effectively separating valuable building supply chain data. Intervention research on knowledge-induced cases was conducted using data flow graphs, classification methods, and entity relationship graphs. This developed model ensured reliable delivery of building supply chain data [16]. Gigli et al. proposed a sensor-to-cloud monitoring architecture to ensure the efficiency of BIM co-design collaboration. By seamlessly integrating sensors and software technology, different structural health monitoring system components’ software and hardware were jointly designed. Ultimately, the leakage probability in edge-to-cloud data transmission was reduced by over 90% [17].

In summary, relevant scholars have conducted various studies and explorations on BIM co-design and blockchain data storage in BIM. They have achieved many results. However, issues such as real-time updates and repeated transmission of BIM design data among different users, as well as poor security and privacy protection efficiency, have not been fully addressed. Therefore, a BIM smart building design method based on blockchain technology is proposed to solve the real-time updating and collaborative office efficiency of BIM data while simultaneously enhancing the security of design data. The research innovation lies in adopting spinning door transformation (SDT) for incremental updating and maintenance of BIM design data to reduce the redundancy of design data. To further illustrate the validity of the methods proposed by the study, the characteristics of the different methods are compared, as shown in Table 1.

Table 1

Comparison of different methods

Research purpose	Method	Shortcomings	Reference
Adaptive planning for data exchange in the construction industry	Data processing pipeline	Data redundancy not addressed	Reichle et al. [9]
To develop 3D comprehensive design technology for railway engineering	Domestic graphics engine	Pure design, not an optimized design method	Meng et al. [10]
To design energy-saving structures for large buildings based on the green BIM concept	Green BIM concept	Theoretical research	Wang et al. [11]
To understand collaboration conditions in BIM-enabled construction projects	Collaboration behavior matrix	No improvement in the BIM data redundancy problem	Chen et al. [12]
To promote the application of BIM in prefabricated building design	Conceptual model comparison	Applied research	Xiao and Bhola [13]
To propose a decentralized architectural design framework	Blockchain integration	No synchronization of BIM design data	Dounas et al. [14]
To develop a blockchain-based dispute management model for project delivery	Blockchain review	Unsolved BIM data redundancy problem	Faraji et al. [15]
To propose a data model integrating BIM and blockchain for the construction of supply chain data delivery	Knowledge induction case	Unsolved BIM data redundancy problem	Hijazi et al. [16]
To propose a next-generation edge-cloud architecture for structural health monitoring	Sensor integration	Unsolved BIM data redundancy problem	Gigli et al. [17]
Solving data synchronization and redundancy problems in BIM co-design	Hyperledger fabric union blockchain, BIM intelligent building co-design model	—	This paper

The study proposes a BIM co-design smart building method based on hyperledger fabric federated blockchain (FB) for the data synchronization and redundancy problems of BIM co-design. Blockchain technology is utilized to enhance the data storage security in the BIM co-design process to prevent data loss and tampering. The incremental update of BIM model design data through the SDT algorithm effectively reduces data redundancy and improves the efficiency of data updates. Compared with other methods, the research method demonstrates superior performance in terms of block-out time and transaction throughput. The cycle division mechanism proposed by the study ensures the consistency of BIM model data among different designers. The proposed method has a positive contribution to the field of intelligent building design and provides new ideas and methods for solving collaboration problems in practical design.

This study consists of three parts. First, a BIM co-design smart building model combining IoT is designed. Second, experiments and analysis are conducted on the proposed BIM Co-design smart building model. Finally, the experimental results are summarized, and future research directions are identified.

2 Methods and materials

To enhance the BIM co-design data security, first, BIM co-design’s framework design using blockchain is studied. The BIM co-design is analyzed and described. Second, based on the model framework, model details and processes are developed and designed.

2.1 Establishment of BIM smart building collaborative design model combined with IoT

IoT has brought new opportunities for the development of smart buildings. BIM blockchain storage has become a common method in current architectural design. However, in response to the inability to synchronize data and high redundancy in current BIM co-design solutions based on blockchain, a blockchain-based BIM co-design is proposed. This model is implemented in hyperledger fabric FB. Figure 1 shows the specific framework.

Figure 1

Blockchain-based BIM co-design modeling framework.

In Figure 1, the BIM co-design mainly consists of the BIM manager, BIM designer, internet protocol file system (IPFS), smart contract, hyperledger fabric FB, and certification authority (CA). BIM managers are mainly responsible for designing data based on the integrated BIM. BIM designers are mainly responsible for obtaining and designing BIM and uploading the calculated BIM design data. IPFS is responsible for storing the complete BIM. FB is responsible for integrating BIM design data and storing BIM summary records, design data records, and integrated BIM design data records. Meanwhile, FB performs data encryption, signature, and verification operations. CA is responsible for generating public-private key pairs between the responsible person and the designer. Under this framework, the BIM co-design process is divided into three cycles: model review and upload design, and design data fusion. The BIM manager corresponds to the model review and upload cycle, as represented by the following equation:

(1) BIM o → { BIM update , A D update , B DFDI record , C DAI record } .

In Eq. (1), BIM o refers to the BIM manager, BIM update refers to BIM updates, A D update refers to uploading updated data D update , B DFDI record refers to the record DFDI record of BIM design data obtained after fusion, and C DAI record refers to storing BIM summary records DAI record . The BIM designer corresponds to the model design cycle, represented by the following equation:

(2) BIM d → { B D new , design , A DDI } .

In Eq. (2), BIM d refers to the BIM designer, B D new refers to obtaining the latest BIM D new , d e s i g n stands for designing BIM, and A DDI refers to uploading BIM design data DDI through smart contracts. The data fusion cycle of the model design is mainly implemented by BIM d and FB, as represented by the following equation:

(3) BIM d → [ A DDI ] → BC BC → { F DDI , DFDI record } .

In Eq. (3) BC refers to hyperledger fabric FB. Each cycle is set to be relatively separate. The switching between cycles is mainly achieved based on the position changes of the system’s relevant states. If the current status shows that the model can be updated and uploaded, the next cycle can be entered. Similarly, when the model design status displays that design operations can be performed, the design cycle can be entered. In the three divided cycles, it is set that BIM d has already obtained the D new uploaded by BIM o from the system at the beginning of the design cycle before further design can be carried out. This can ensure real-time synchronization of BIM co-design data. Meanwhile, the study further combines FB and IPFS as information storage for BIM data. FB serves as on-chain storage. IPFS serves as off-chain storage for the complete BIM data. Figure 2 shows the content stored on FB.

Figure 2

Data content stored in FB on the chain.

From Figure 2, FB mainly stores BIM design data, summary records, and fused BIM design data. In different cycles, different roles upload different data to BC to ensure the security and reliability of all data storage, achieving hybrid storage of FB + IPFS. Based on the above, Figure 3 shows the BIM co-design operation using the proposed blockchain.

Figure 3

Blockchain-based BIM co-design model runtime process.

As shown in Figure 3, this model consists of five stages, including global information configuration, initialization of BIM, acquisition, and design of BIM, fusion of BIM design data, and updating and uploading of BIM. The global information configuration in the first stage is mainly generated by CA for public–private key pairs. The second stage is the model review and upload cycle. The third stage is the BIM design cycle. The fourth stage corresponds to the BIM design data fusion cycle. Finally, the corresponding model review and upload cycle is completed by BIM o to verify and upload BIM design data.

2.2 Partial detail design of BIM smart building collaborative design model

Based on the BIM co-design and operation proposed in the previous section, further detailed design is carried out. In the first stage of CA global configuration, when a new BIM o or BIM d enters BIM co-design, CA will directly generate a dedicated public key encryption and private key signature system for BIM d . The second stage mainly starts from the review upload cycle. The model initialization is completed by BIM o . Figure 4 shows a two-stage process.

Figure 4

Execution flow of public–private key pairs and model initialization.

As shown in Figure 4(a), when a new person in charge or designer joins the model, CA inputs a random string and calculates its length to determine if it exceeds 36. If the conditions are met, CA selects the elliptic curve corresponding to the length of the random string from the Eliptic library. At the same time, CA calls the Generate Key function in the Eclsa library based on elliptic curves and random strings to generate public and private key pairs for newly added users. Otherwise, the random string input will be repeated until its corresponding length exceeds 36. Figure 4(b) shows BIM o ’s initialization operation on this model. BIM o first uploads the given initial BIM design data to the IPFS system and calculates DAI record based on the corresponding BIM summary upload information. The data information included in DAI record is represented by the following equation:

(4) DAI record = { No . , BIM uptime , BIM o key , g D , S g _ D , Addr IPFS } .

In Eq. (4), No . refers to the numbering information of BIM, BIM uptime refers to the BIM upload time information, BIM o key refers to BIM o ’s public key, g D refers to BIM content’s hash value, S g _ D refers to g D ’s private key signature, and Addr IPFS refers to the hash storage address of BIM in IPFS. In the third stage of BIM acquisition and design, BIM d mainly obtains and designs D new based on BIM local storage information and BIM hash values. Figure 5 shows the main process.

Figure 5

BIM acquisition and design process.

In Figure 5, the experiment sets D new as data information without any BIM design conditions. It is assumed that BIM d has obtained specific design information through other means and has understood and clarified the design details. When BIM d already has a BIM locally and g D has not been tampered with, g D and locally saved hash values are compared. When BIM d does not save the BIM locally and g D is not tampered with, the experiment obtains D new from IPFS based on BIM’s hash storage address Addr IPFS in IPFS and conducted tampering verification. The main judgment process in the BIM acquisition and design process is the verification of digital signatures related to g D and D new . Figure 6 shows the specific verification process.

Figure 6

Digital signature verification process.

As shown in Figure 6, this verification process first decrypts the extracted information of the encrypted digital signature using the corresponding user’s public key. The current summary information is compared and analyzed with the previous one through a hash operation to determine whether the information obtained before and after is consistent. If these two messages are consistent, D new has not been compromised or attacked. On the contrary, D new content has been tampered with and requires BIM o to handle it. Finally, the study utilizes SDT calculation to obtain design data DDI within the BIM design cycle. SDT first determines the line segment of the door axis, represented by the following equation [18,19]:

(5) x = k t + b .

In Eq. (5) x refers to the data size, k stands for slope, k refers to the timeline, and b stands for intercept, usually taken as 0. Based on the calculated data size, the upper and lower door pivot points and slopes are further determined. The upper and lower door pivot points are represented by the following equation:

(6) u 0 = x 0 + Δ E d 0 = x 0 − Δ E .

In Eq. (6), u 0 and d 0 correspond to the upper and lower door axles’ positions, x 0 represents the current storage point location, which is the most recently saved data point value, and Δ E represents the door’s initial width, which is the preset threshold. The upper and lower doors’ slopes are represented by the following equation:

(7) u k = ( x t − u b ) / Δ t d k = ( x t − d b ) / Δ t .

In Eq. (7), u k and d k correspond to the upper and lower doors’ slopes, x t represents new data point value, u b and d b correspond to the upper and lower door axles’ intercept, and Δ t is the time interval. Based on the upper and lower doors’ slopes, SDT opens and closes the gates to compress and store data points, removing redundant data. After the BIM acquisition and design phase is completed, in the fourth phase of integrating BIM design data, relevant fusion rules are set for DDI fusion. The experiment deletes all conflicting design date content, retains non-conflicting DDI , and merges them to obtain the fused BIM design data. Finally, BIM retrieves the smart contract and uploads the relevant data to FB. In the fifth stage of BIM update and upload, BIM o performs model update and upload operations. Meanwhile, Raft is utilized as the consensus algorithm for FB, and its consensus process includes two major steps: leader election and log replication. Combining the above, BIM co-design based on blockchain not only solves data synchronization but also utilizes SDT to solve data redundancy.

3 Results

The proposed blockchain-based BIM co-design in architecture was verified. First, the study introduced the experimental simulation environment configuration and compared different blockchains. Second, the model performance was validated. Finally, BIM co-design proposed by other scholars was introduced for performance comparison.

3.1 Experimental simulation environment configuration

The model is tested and analyzed in an experimental environment built with multiple virtual institutions. The blockchain is hyperledger fabric FB. The simulation equipment consists of one 16 GB, 8-core i7 processor laptop, two 2 GB RAM Raspberry Pi 4B, and three virtual machines running 2 cores and 2 GB RAM. The study employs a simulation of the processes involved in the initialization, acquisition, and design of a BIM, as well as the integration of design data, the updating of the model, and the uploading of the updated model with the associated data. The simulation includes the comparison of the BIM model before and after the design phase, as well as the fusion of the design data within the BIM model. The cycle division mechanism and SDT algorithm are used to ensure the consistency and data synchronization of the BIM model. The Raft consensus algorithm has five Orderer sorting services, five Org organizations, and five Peer nodes each, with each Org organization consisting of one CA service and one Peer node. Figure 7 shows the specific deployment.

Figure 7

Deployment diagram of the simulation environment.

From Figure 7, the study simulates one manager and four designers. Hyperledger fabric consists of an organization of five related nodes and one transmission channel. In addition, all five users are set up locally with IPFS nodes, forming an IPFS private network. In simulation testing, the number of nodes directly affects the concurrent processing capacity and load distribution of the system. An increase in the number of nodes within a system will result in an improvement in throughput. However, this will also lead to an increase in both network delay and the time required to reach consensus. The frequency of sending transactions determines the performance of the system under high load. The high frequency of sending transactions can test the extreme performance and stability of the system. Therefore, the study is mainly adapted to the needs of real application scenarios to achieve the best performance balance.

3.2 Hyperledger fabric FB performance validation

First, the study examined the throughput capability and data storage latency of hyperledger fabric FB. Blockchain and cross-chain technologies based on edge computing were introduced for performance comparison [20,21]. The study conducted 1,500 data storage transactions within a set time frame. Each transaction actually occupied 0.9 kB of storage space. Figure 8 shows the specific comparison results.

Figure 8

Performance comparison of different blockchains. (a) Throughput comparison with different numbers of nodes. (b) Comparison of average storage latency with different numbers of nodes.

In Figure 8(a), the study utilized transaction per second (TPS) to represent different blockchains’ throughput and measured their concurrent processing capabilities. As the nodes increased, the throughput of hyperledger fabric FB showed a continuously increasing trend, but a decrease occurred after 16 nodes. The throughput of blockchain based on edge computing dropped after 12 nodes. The cross-chain technology dropped after 16 nodes. This may be because in FB, such as hyperledger fabric, consensus mechanisms based on Raft or PBFT were usually used, which provide high throughput when the number of nodes is small. However, when the number of nodes increases to a certain level, the above problem might lead to performance degradation. Overall, the maximum throughput of hyperledger fabric FB was 1189.13 TPS, which was 345.06 and 621.32% higher than the other two blockchains, respectively. From Figure 8(b), as nodes increased, the storage latency of the three blockchains continued to increase. However, overall, hyperledger fabric FB had lower latency than the other two blockchains. This indicated that the use of hyperledger fabric FB for BIM Co-design data storage and security protection in research was reasonable and reliable. Figure 9 compares the control latency and response time of three types of blockchains.

Figure 9

Comparison of control latency and response time of three blockchains. (a) Comparison of average control latency with different numbers of nodes. (b) Comparison of average response time with different numbers of nodes.

Figure 9(a) shows three blockchains’ control latency at different nodes. With the increasing blockchain, hyperledger fabric FB’s control latency was always lower than the other two blockchains. This may be due to the superior throughput and multidimensional scalability of hyperledger fabric FB, which leads to weakening the control latency. From Figure 9(b), the response time required for hyperledger fabric FB was reduced by an average of 48.24% compared to the other two blockchains. This indicated that utilizing hyperledger fabric FB for data security storage had superior capabilities. On this basis, the study further examined the ability of different blockchains to securely protect BIM co-design data, using the F1 score as the evaluation indicator. Table 2 shows the specific results.

Table 2

Blockchain’s key metrics F1 score (%)

Blockchain	Authentication mechanisms	Access control	Cryptosystem	Privacy protection	Cryptographic algorithm	Resistance to attack	Smart contract security	Node distribution
Hyperledger fabric	97.64	100.00	99.88	99.87	98.02	98.78	97.89	95.48
Edge computing	90.24	98.98	95.43	97.68	97.68	99.02	97.68	97.65
Cross-chain	88.76	95.24	90.24	96.60	98.27	97.68	97.88	95.55

From Table 2, hyperledger fabric FB had a better F1 score than the other two types of blockchains in terms of authentication mechanism, access control, encryption system, and privacy protection. Hyperledger fabric provides channel functionality that allows the creation of private communication paths between different business entities, thus enhancing privacy protection. The blockchain based on edge computing had more advantages in anti-attack capability and node distribution. This may be because edge computing has improved the blockchain’s ability to identify and resist malicious attacks, thus improving the blockchain’s ability to resist attacks. Cross-chain technologies are needed to deal with interoperability issues between different blockchains, which may be responsible for increasing the complexity and potential security risks of edge computing. However, overall, hyperledger fabric FB was more suitable for BIM co-design.

3.3 Blockchain-based BIM co-design verification

Simulation verification was conducted in the hyperledger fabric FB testing framework to further validate the proposed BIM co-design. The experiment mainly validated the model’s block time testing and transaction throughput. Existing references were introduced for performance comparison. The comparison models all used the hyperledger fabric FB testing framework [22,23,24]. The maximum block time for the model was 2 s. The transaction volume of model data records issued by a single BIM designer per unit time was 400–1,800. Figure 10 shows the specific results.

Figure 10

Performance comparison of different methods. (a) Comparison of chunking times for different methods. (b) Comparison of throughput for different methods.

Figure 10(a) shows the Chunk time comparison of BIM co-design constructed using different methods. All methods were less than the maximum chunk time set by the study. The study of Knippers et al. [22] had an average chunk time of 0.92 s, the study of Tan et al. [23] had an average chunk time of 0.62 s, and the study of Kiu et al. [24] had an average chunk time of 0.77 s. The proposed method had a chunk time of 0.53 s, which was 42.39, 14.53, and 31.17% lower than other methods, respectively. Figure 10(b) shows a comparison of transaction throughput for different methods. The proposed BIM co-design was superior, with a higher maximum number of transactions that could be processed and confirmed within a certain time interval, which had better stability and security. BIM co-design had a transaction throughput of up to 1,115 times, with an average improvement of 12.38% compared to the other three methods. Finally, the study further validated the proposed model’s consistency. Four BIM designers were set up to conduct 1,000 rounds of collaborative design on the given model. Each round ensured that at least one designer operated on the model. There were no restrictions on the model design content, but the content had to be different from the BIM before design. Table 3 shows the specific test results.

Table 3

BIM consistency simulation test results

Test round	Number of coherent BIM
1–352	4
353–407	3
408–641	4
642–678	3
679–1,000	4

According to Table 3, in the 353–407 and 642–678 rounds of testing, the BIM consistency was 3. The nodes in other rounds were all 4. This may be due to network disconnections, crashes, and other issues affecting consistency at individual nodes. After node fault recovery, the BIM consistency was restored to 4. This indicated that the BIM co-design had good robustness. BIM co-design could allow all BIM designers to design in the same BIM, meeting the BIM consistency requirements.

4 Conclusion and future work

A BIM co-design was developed based on hyperledger fabric FB to address the data synchronization and high redundancy in BIM co-design based on current blockchain technology. The BIM consistency between different designers was ensured through the design cycle division mechanism. SDT was utilized for incremental data updates. In experimental verification, hyperledger fabric FB’s throughput increased by 345.06 and 621.32% compared to the other two blockchain technologies, with a maximum throughput of 1189.13 TPS. The response time required for hyperledger fabric FB was reduced by an average of 48.24% compared to the other two blockchains. The proposed BIM co-design method required a chunk time of 0.53 s, which was 42.39, 14.53, and 31.17% less than other methods, respectively. This method had a maximum transaction throughput of 1,115 times, which was an average improvement of 12.38% compared to other methods. The BIM verification consistency confirmed this proposed model’s robustness. Using blockchain technology to implement BIM co-design can improve the model design’s data storage security and efficiency. The proposed hyperledger fabric FB-based BIM co-design model effectively solves the data synchronization and redundancy problems by introducing the cycle division mechanism and SDT algorithm. This approach not only improves the security of design data but also significantly improves the efficiency of BIM co-design. Aiming at the challenges in IoT data management, the proposed Hashgraph-based IoT data management approach significantly improves the data throughput and consensus efficiency by utilizing the high concurrency characteristics of the DAG structure. This approach provides an effective solution for handling large-scale IoT data. Furthermore, the research presents a novel BIM co-design model and IoT data management method, offering a fresh perspective and solution for the operation and maintenance of digital twin smart buildings.

However, the study only conducts BIM co-design based on blockchain technology. The proposed method is based on blockchain and IoT technologies, the maturity and stability of which may vary across regions and industries. This may have an impact on the implementation and performance of the model. Although blockchain offers data security and immutability, additional privacy safeguards need to be considered when handling sensitive data, especially in terms of compliance with data protection regulations. In the case of large-scale deployments and dramatic increases in data volumes, the processing power and response time of the system may be affected, requiring further optimization to handle larger data sizes. Future experiments will consider introducing intelligent algorithms, C-language, and other technologies for model system development. Examples of model optimization and application can be introduced. Future research aims to develop more effective data management algorithms to improve system performance and scalability in handling large-scale data, and enhance system security. Especially in terms of data encryption and user authentication, it is necessary to ensure higher privacy protection standards are met.

Funding information: Authors state no funding involved.
Author contributions: Man Feng: data collection, statistical analysis, visualization, and writing the manuscript draft. Hanmei Wu: study design, data collection, statistical analysis, visualization, and revision of the manuscript. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: All data generated or analyzed during this study are included in this published article.

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Received: 2024-08-01

Revised: 2024-09-19

Accepted: 2024-10-29

Published Online: 2025-04-21

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

https://doi.org/10.1515/nleng-2024-0063

Schlagwörter für diesen Artikel

BIM; IoT; collaborative design; blockchain; hyperledger fabric federated blockchain; cycle division mechanism; SDT algorithm

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