An Operation and Maintenance Strategy of Intelligent Building Information Model Data Based on Cloud Computing

Peng, Zimao

doi:10.18280/isi.250408

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…Connecting environmental sensor data with BIM through Dynamo, Arduino, and Revit API, combining BIM with other tools like building maintenance management tools to gain comprehensive control over project monitoring and integrating BIM with geographic information system (GIS) for better management of building information have been done in this area [ 27 , 28 , 29 ]. Furthermore, some maintenance strategies have been done to manage massive data, visualization quality and data storage processes to serve as reference in other fields of study [ 30 ].…”

Section: Content Analysismentioning

confidence: 99%

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Panah

Kioumarsi

2021

Sensors

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Improvements in the science of health monitoring and maintenance have facilitated the observation of damage and defects in existing structures and infrastructures, such as bridges and railways. The need to extend sensing technology through the use of wireless sensors as well as the lack of description tools for understanding, visualizing, and documenting sensor outputs has encouraged researchers to use powerful tools such as Building Information Modelling (BIM) systems. BIM has become important because of conducting tools widely used in the Architecture, Engineering, and Construction (AEC) industry to present and manage information on structural systems and situations. Since combining health monitoring and maintenance results with BIM models is a new field of study, and most projects utilize various aspects of it, we have conducted a review of important work related to this subject published from 2010 to November of 2020. After reviewing 278 journal articles, research trends, approaches, methods, gaps, and future agenda related to BIM in monitoring and maintenance were highlighted. This paper, through a bibliometric and content analysis, concludes that besides main improvements, some limitations now exist which affect the modeling and maintenance process. These limitations are related to extending the IFC schema, optimizing sensor data, interoperability among various BIM platforms, optimization of various sensing technologies for fault detection and management of huge amounts of data, besides consideration of environmental effects on monitoring hazards and underground objects. Finally, this paper aims to help to solve the mentioned limitation through a comprehensive review of existing research.

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Section: Content Analysismentioning

confidence: 99%

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Panah

Kioumarsi

2021

Sensors

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“…In recent years, building information modeling (BIM) has developed quickly as a technique applicable to construction and related industries [1][2][3]. e technique boasts rich and consistent semantic information, as well as re ned geometric structure.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction and Extraction of Engineering Remote Sensing Data Based on Building Information Modeling and Geographical Information System

Wang

Zhang

et al. 2022

Scientific Programming

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The high dimensionality of the modern remote sensing data of construction land makes it complicated to extract image data. This paper proposes a dimensionality reduction and extraction strategy for the remote sensing data of construction land, with the aid of building information modeling (BIM) and geographical information system (GIS). Firstly, the BIM was employed to reduce the size of the remote sensing data of construction land and to obtain the information of each element. Next, the remote sensing data of construction land were parsed, and the key BIM elements were extracted through semantic filtering. In addition, the remote sensing data were converted into a triangulated irregular network (TIN), which can be processed by the geographical information system (GIS). In the end, random projection was utilized to reduce the dimensionality and compress the remote sensing data, and realize the data extraction. Experimental results show that our approach can compress and extract the information from construction land images in the remote sensing data with a high accuracy.

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“…Cloud computing is the research hotspot direction in distributed computing. It inherits the characteristics of grid computing, utility computing, and other classic distributed computing frameworks [1][2][3][4][5]. Cloud computing provides users with on-demand infrastructure services, platform services, and software services through the network [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Assignment Method of the Cloud Computing Platform Based on Artificial Intelligence

Yong-chang

Geng

2022

Computational Intelligence and Neuroscience

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To realize load balancing of cloud computing platforms in big data processing, the method of finding the optimal load balancing physical host in the algorithm cycle is adopted at present. This optimal load balancing strategy that overly focuses on the current deployment problem has certain limitations. It will make the system less efficient and the user’s waiting time unnecessarily prolonged. This paper proposes a task assignment method for long-term resource load balancing of cloud platforms based on artificial intelligence and big data (TABAI). The maximum posterior probability for each physical host is calculated using Bayesian theory. Euler’s formula is used to calculate the similarity between the host with the largest posterior probability and other hosts as a threshold. The hosts are classified according to the threshold to determine the optimal cluster and then form the final set of candidate physical hosts. It improves the resource utilization and external service capability of the cloud platform by combining cluster analysis with Bayes’ theorem to achieve global load balancing in the time dimension. The experimental results show that: TABAI has a smaller processing time than the traditional load balancing multi-task assignment method. When the time is >600 s, the standard deviation of TABAI decreases to a greater extent, and it has stronger external service capabilities.

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An Operation and Maintenance Strategy of Intelligent Building Information Model Data Based on Cloud Computing

Cited by 8 publications

References 20 publications

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Dimensionality Reduction and Extraction of Engineering Remote Sensing Data Based on Building Information Modeling and Geographical Information System

Multi-Task Assignment Method of the Cloud Computing Platform Based on Artificial Intelligence

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