BIM compatibility and its differentiation with interoperability challenges as an innovation factor

Shirowzhan, Sara; Sepasgozar, Samad M. E.; Edwards, David John; Li, Heng; Chen, Wang

doi:10.1016/j.autcon.2020.103086

Cited by 111 publications

(62 citation statements)

References 127 publications

(198 reference statements)

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“…In addition, Hadoop and machine learning algorithms are used by banks for analysis of loan data to check the reliability of lending organizations, thereby increasing profitability and innovation [139]. Recent studies in the field of construction, city, and property management specially reported that compatibility, interoperability, value, and reliability are critical factors of digital technology adoption and implementation [140][141][142][143][144]. The network intrusion traffic challenge was resolved efficiently by Suthaharan et al [145] using machine learning and big data technologies.…”

Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

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“…In this case, the integration of GIS and BIM will bring more advantages to the data sets including the combination of large-and small-scale built environments, looking at BIM models in a broader context of 3D geographic location, producing a more realistic model of built environments including buildings, vegetation, terrain, road network, and agents. For such integration, there are interoperability issues and factors, as Shirowzhan, Sepasgozar [11] discussed recently.…”

Section: Integration Of Gis and Bimmentioning

confidence: 99%

“…They also recommend investigating the behaviour of smart city agents and the identification of the details of subsystem behaviour as well as efficient ways of information exchange. In addition, for such applications, the interoperability of systems such as GIS with BIM and virtual/augmented reality applications is still challenging [11].…”

Section: Smart Cities Including Smart Elementsmentioning

confidence: 99%

“…Adopting this categorization, the useful technologies for smart cities are (i) network and office work technologies as highly penetrated information technologies (IT) and information and communication technologies (ICT) influencing human productivity, e-commerce, urban services [37], e-government [45], agriculture, and e-banking in smart cities. Recently, many communication technologies have arisen, such as social media [46], Skype, Teams, and Zooms which are known to be useful for working from home or working remotely which are critical for smarter cities; (ii) design technologies such as Building Information Modelling (BIM) [11,47], Geographic Information Systems (GIS), and CyberGIS [18,48]; (iii) sensing technologies such as wearable sensors [49], RFID [50,51], IoT sensors, real time locating systems (RTLS) [52], laser scanners [22], GPS, Radar, cameras for smart transportation, smart parking, and smart construction (job-site management, tracking materials, site management, physical progress monitoring, and productivity, safety, emission [53,54], security, and remote controlling devices and diagnostic systems attached or imbedded in heavy equipment such as Grader or Crane); (iv) production technologies such as 3D Printing [55,56]; and (v) virtual technologies such as mixed reality and digital twin.…”

Section: Smart Cities Including Smart Elementsmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Shirowzhan

Tan

Sepasgozar

2020

IJGI

Self Cite

109

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Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This raises the question of assessing the impact of a new infrastructure project on the community prior to its commencement—what type of technologies can potentially be used for creating a virtual representation of the city? How can a smart city be improved by utilizing these technologies? There are a wide range of technologies and applications available but understanding their function, interoperability, and compatibility with the community requires more discussion around system designs and architecture. These questions can be the basis of developing an agenda for further investigations. In particular, the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities is discussed. In line with smart city technology development, this Special Issue includes eight accepted articles covering trending topics, which are briefly reviewed.

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“…As an example of collecting 'information' and 'usability' of system, Chiu and Ko [64] developed a personalized intelligent music selection system for psychological health based on heart rate variability and machine learning. Usability, need, and usefulness of a system have been ignored to be examined and validated in some contexts, but it is recommended as one of the key factors of successful technology adoption and implementation in the technology/information system literature [70][71][72][73]. This system intends to utilize an intelligent mechanism for automatic music selection for the users according to their emotions and moods.…”

Section: Group 1: Iot and Usersmentioning

confidence: 99%

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

et al. 2020

Self Cite

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This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.

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BIM compatibility and its differentiation with interoperability challenges as an innovation factor

Cited by 111 publications

References 127 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

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