Integrating RFID and BIM technologies for mitigating risks and improving schedule performance of prefabricated house construction

Li, Clyde Zhengdao; Zhong, Ray Y.; Xue, Fan; Xu, Gangyan; Chen, Ke; Huang, GQ; Shen, Geoffrey Qiping

doi:10.1016/j.jclepro.2017.07.156

Cited by 210 publications

(163 citation statements)

References 13 publications

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“…Altaf et al [16] e production planning and control system of prefabricated components based on BIM-RFID technology is proposed e system is limited to the theoretical framework, and the improvement of actual production efficiency needs to be verified Li et al [34] e RFID technology is used to track the construction components and improve the construction schedule…”

Section: Main Contributions Limitationsmentioning

confidence: 99%

Visualization of Component Status Information of Prefabricated Concrete Building Based on Building Information Modeling and Radio Frequency Identification: A Case Study in China

Guo

Jiang

Shang

2019

Advances in Civil Engineering

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In view of the problems such as the basic properties, usage, and location of prefabricated concrete building components, which are easy to be omitted, missed, and difficult to query in the field management, this study introduces building information modeling (BIM) and radio frequency identification (RFID) technologies to visualize the state information of prefabricated concrete components, such as component type, manufacturer, location, and temperature. In the design stage, a new RFID family can be built in the actual model in order to solve the lack of definition of RFID family through the Industry Foundation Class (IFC) standard, and the databases of BIM and RFID can be connected with C# language, realizing the effective integration of the two engineering technologies. In the application stage, through the secondary development of Revit, the information connection between PC terminal and RFID equipment is completed, and the component data collected by RFID tags are transmitted to the BIM model to realize the integration and visualization of prefabricated component state information. In this study, the traceability of prefabricated components of prefabricated concrete buildings can be improved, providing a basis for quality responsibility tracking in the later period, reducing unnecessary waste of human and material resources and helping to maximize economic benefits.

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Section: Main Contributions Limitationsmentioning

confidence: 99%

Visualization of Component Status Information of Prefabricated Concrete Building Based on Building Information Modeling and Radio Frequency Identification: A Case Study in China

Guo

Jiang

Shang

2019

Advances in Civil Engineering

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“…The seven elements have the granularity matrices: Based on the granularity matrix, the entropy space is shown in Fig. 2 For R(5), using the same steps from the proposed algorithm, we can get the entropy set 2, 3, 4), (5,6,9), (7,8,10)…”

Section: An Experimental Casementioning

confidence: 99%

“…3. Granularity space topology for R (5) For R (3), there is only one element 3 1 (1,2,3,4,5,6,7,8,9,10) x  , the matrix is then as follows: (13) 3 1 1 ((2,1,1), (5, 3, 2) ((3, 2,1), (7, 5, 3)) 1 ((5, 3, 2), (8,6,3) (1,1,1,1), (2,1,1,1), (3, 2,1,1), (4, 2,1,1), (5,3,2,1)…”

Section: An Experimental Casementioning

confidence: 99%

“…We can get the ((1,1,1,1), (10, 7, 3,1)) s N  , as 33 1 xy  , the connection from 3 1 P could be extracted for inserting to Ns = ((3, 2, 1), (7,5,3)) will be used and the following result could be obtained: ((1,1,1,1), ((3, 2,1), (7,5,3)), (10, 7, 3,1)) [3,7], s N …”

Section: An Experimental Casementioning

confidence: 99%

“…The need for network service has exceeded the service capacity of the network, and the Quality of Service (QoS) is gradually declining [2]. The purpose of optimizing network resources is to make full use of the resources under the premise of satisfying the quality requirements of the service and to avoid the situation where some resources are not fully utilized [3][4][5][6][7]. The traditional 63…”

Section: Introductionmentioning

confidence: 99%

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QoS Routing Algorithm Based on Entropy Granularity in the Network Transmission

2019

Cybernetics and Information Technologies

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With the rapid development of computer networks, more hosts are connected to the Internet where they could communicate with each other. The need for network service has exceeded the service capacity of the network, and the Quality of Service (QoS) is gradually declining. Based on existing Shortest Path First (SPF) algorithm, this paper proposes a new QoS required transmission path approach by considering the overhead balance of network resources. This paper uses the entropy granularity as the main line in the application of routing protocols. Firstly, it researches the optimization of routing algorithms for network load balancing resources, routing algorithms based on link traffic distributing weights, link weight optimization based on adaptive genetic algorithm and computational intelligence based on entropy granularity theory. This research proposes a method to apply entropy granularity to Open Shortest Path First (OSPF) routing, including the implementation of the method. After that, a case study is presented by using some examples.Shortest Path First (SPF) algorithm (such as Dijkstra's algorithm) mainly focuses on how to find a best path to meet the request or have the largest possibility to meet the request path [8,9]. However, it may ignore the overall network resources and cause low comprehensive utilization. There may even be some problems such as network congestion, which may cause the decreasing in the overall number of requests and affect the performance of the network [10].Bellman-Ford algorithm considering two variables (path bandwidth, link transmission delay) was proposed to solve the QoS routing problem [11]. This algorithm makes assumptions about the network model, assuming that a node-based router adopts a rate-based scheduling algorithm. From the calculation formula of the maximum end-to-end delay of the scheduling algorithm, the factors that determine the path delay including link transmission delay and path bandwidth can be identified. Therefore, the network topology maps based on different bandwidths was introduced to calculate the shortest path according to the link length on different topological maps [12]. The algorithm maximizes the use of network resources at the time and satisfies a certain QoS request at that time. However, the algorithm does not consider the cost paid by the network. For example, when the available network topology map is constructed to meet a traffic request, some bandwidth is insufficient, and the link is pruned [13,14]. When there are more requests, especially in the cast of heavy network load, subsequent requests may not be satisfied because of the previous occupancy of bandwidth, which may lead to a decrease in overall reception rate [15]. In addition, if the load balancing algorithm pursues the greatest degree of balanced use of network resources, not only delay sensitive services cannot accept the computational delay caused by complex calculations, but also it is not necessary to achieve the absolute equilibrium state of the network.Based on existing SPF algor...

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The establishment of BIM‐embedded knowledge‐sharing platform and its learning community model: A case of prefabricated building design

Lee¹,

Lo²,

Wen

et al. 2022

Comp Applic In Engineering

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Building information modeling (BIM) not only can be a medium for 3D display and simulation but also can be embedded in the knowledge ontology to connect more comprehensive information. Despite the advantages of the BIM‐embedded knowledge ontology that has been explored in engineering practices, it is still less discussed in engineering education. Therefore, this paper aims to establish a BIM‐embedded knowledge‐sharing platform and its corresponding learning community model to improve learning outcomes. The BIM‐embedded knowledge‐sharing platform is established by the MediaWiki to connect the knowledge entries with the related BIM components to expand the BIM‐embedded knowledge ontology. The learning community model is developed on problem‐based learning and project‐based learning to encourage students to build their own knowledge by creating, sharing, and modifying knowledge entries. In the experimental teaching, twenty students are invited to participate in the learning community for a class on prefabricated building design. Students are guided to build and share their knowledge entries and the related BIM components by following the learning community model. Besides this, they also use the BIM‐embedded knowledge‐sharing platform to complete a real design project. Results show the proposed BIM‐embedded knowledge‐sharing platform and the learning community model are beneficial for the learning outcomes.

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Integrating RFID and BIM technologies for mitigating risks and improving schedule performance of prefabricated house construction

Cited by 210 publications

References 13 publications

Visualization of Component Status Information of Prefabricated Concrete Building Based on Building Information Modeling and Radio Frequency Identification: A Case Study in China

Visualization of Component Status Information of Prefabricated Concrete Building Based on Building Information Modeling and Radio Frequency Identification: A Case Study in China

QoS Routing Algorithm Based on Entropy Granularity in the Network Transmission

The establishment of BIM‐embedded knowledge‐sharing platform and its learning community model: A case of prefabricated building design

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