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    Industry Perception of the Knowledge and Skills Required to Implement Sensor Data Analytics in Construction

    Source: Journal of Civil Engineering Education:;2024:;Volume ( 150 ):;issue: 001::page 04023010-1
    Author:
    Mohammad Khalid
    ,
    Abiola Akanmu
    ,
    Homero Murzi
    ,
    Sang Won Lee
    ,
    Ibukun Awolusi
    ,
    Daniel Manesh
    ,
    Chinedu Okonkwo
    DOI: 10.1061/JCEECD.EIENG-1902
    Publisher: ASCE
    Abstract: Construction, one of the largest industries in the world, consistently underperforms and faces barriers in leveraging the full potential of applying analytics to sensor data due to a lack of a skilled workforce. The prospects for data-driven solutions to address emerging construction challenges and enhance performance across project life cycles are therefore constrained. Through mixed-method research utilizing a survey and focus group, this study investigates the knowledge and skills required for graduating construction engineering and management students to implement sensor data analytics in the construction sector. The findings revealed that sensor data analytics knowledge and skills are required to systemically process and analyze data from sensing technologies and present them in formats for effective decision-making. The presented key knowledge areas, specific skills, and their significance can aid the construction industry and academics to streamline professional development efforts to match the actual demands, allowing for more efficacy in workforce training. The future construction workforce is expected to gain a competitive edge with sensor data analytics knowledge and skills as the ubiquitous integration of sensing technologies continues to drive the tremendous growth of sensor data.
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      Industry Perception of the Knowledge and Skills Required to Implement Sensor Data Analytics in Construction

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    contributor authorMohammad Khalid
    contributor authorAbiola Akanmu
    contributor authorHomero Murzi
    contributor authorSang Won Lee
    contributor authorIbukun Awolusi
    contributor authorDaniel Manesh
    contributor authorChinedu Okonkwo
    date accessioned2024-04-27T22:44:27Z
    date available2024-04-27T22:44:27Z
    date issued2024/01/01
    identifier other10.1061-JCEECD.EIENG-1902.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4297384
    description abstractConstruction, one of the largest industries in the world, consistently underperforms and faces barriers in leveraging the full potential of applying analytics to sensor data due to a lack of a skilled workforce. The prospects for data-driven solutions to address emerging construction challenges and enhance performance across project life cycles are therefore constrained. Through mixed-method research utilizing a survey and focus group, this study investigates the knowledge and skills required for graduating construction engineering and management students to implement sensor data analytics in the construction sector. The findings revealed that sensor data analytics knowledge and skills are required to systemically process and analyze data from sensing technologies and present them in formats for effective decision-making. The presented key knowledge areas, specific skills, and their significance can aid the construction industry and academics to streamline professional development efforts to match the actual demands, allowing for more efficacy in workforce training. The future construction workforce is expected to gain a competitive edge with sensor data analytics knowledge and skills as the ubiquitous integration of sensing technologies continues to drive the tremendous growth of sensor data.
    publisherASCE
    titleIndustry Perception of the Knowledge and Skills Required to Implement Sensor Data Analytics in Construction
    typeJournal Article
    journal volume150
    journal issue1
    journal titleJournal of Civil Engineering Education
    identifier doi10.1061/JCEECD.EIENG-1902
    journal fristpage04023010-1
    journal lastpage04023010-15
    page15
    treeJournal of Civil Engineering Education:;2024:;Volume ( 150 ):;issue: 001
    contenttypeFulltext
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