Human Factors in Introducing On‐Site Construction Automation

Navon, Ronie; Kelly, Paul; Johnston, David W.

doi:10.1061/(asce)0733-9364(1993)119:4(801)

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…With the full implementation of robotics, fewer manual jobs will be available, and production can be increased with high quality (Akinradewo et al , 2018). Workers affected by this technology may be required to change trade or learn new skills (Navon et al , 1993). In addition, most laborers in construction do not have higher education, as they have acquired specific skills on job sites, such as bricklaying and carpentry.…”

Section: Barriers Impeding the Adoption Of Roboticsmentioning

confidence: 99%

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Modeling the robotics implementation barriers for construction projects in developing countries

Oke

Kineber

Albukhari

et al. 2021

IJBPA

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PurposeThe purpose of this paper is to evaluate the barriers militating against the adoption of robotics in the construction industry.Design/methodology/approachRobotics implementation barriers were obtained from the previous studies and then through questionnaire survey construction stakeholders in Nigeria evaluate these barriers. Consequently, these barriers were examined via the exploratory factor analysis (EFA) technique. Furthermore, a model of these barriers was implemented by means of a partial least square structural equation modeling (PLS-SEM).FindingsThe EFA results showed that these barriers could be categorized into two: cost and technology. Results obtained from the proposed model showed that platform tools were crucial tools for implementing cloud computing.Originality/valueThe novelty of this research work will be provided a solid foundation for critically assessing and appreciating the different barriers affecting the adoption of robotics.

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Section: Barriers Impeding the Adoption Of Roboticsmentioning

confidence: 99%

“…Therefore, acquiring new skills for other jobs may be challenging because a more academic background is required (Akinradewo et al , 2018). Managers may, therefore, doubt their workers ability to be able to operate new robots and construction machines (Navon et al , 1993).…”

Section: Barriers Impeding the Adoption Of Roboticsmentioning

confidence: 99%

Modeling the robotics implementation barriers for construction projects in developing countries

Oke

Kineber

Albukhari

et al. 2021

IJBPA

View full text Add to dashboard Cite

show abstract

“…This phenomenon includes client needs that are sometimes imprecise and changing, causing significant change in costs; little overall learning because of few repetitions; high risks due to novelty; technical, climatic, and even societal uncertainties; coordination and complex decision-making processes between the teams involved; and changing conditions of realization. The weak capital budgets in R&D and the reluctance of strategies related to construction automation are other important factors [5].…”

Section: Introductionmentioning

confidence: 99%

“…There are humans walking around, etc. All these have not improved for so long because construction industries are the least digitized sector [3,5].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Unsupervised Robotics in Building Constructions: A Priority Area of the Fourth Industrial Revolution (4IR)

Eze¹,

Ozioko²,

Nwigwe³

2023

Avantgarde Reliability Implications in Civil Engineering

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Many effective quality systems to maintain the robots’ autonomous task expansion process in construction industries for various applications over the years have yet to be well established. This study, therefore, presents a simple deep/neural network algorithm to diverse robotics tasks on building construction—bricklaying, grasping, cutting materials, and aerial robot obstacle avoidance and highlight the strengths of these algorithms in real-world robotics applications in building sites. Our findings revealed that the amount of tasks robots encountered in real-world environments is extremely challenging for existing robotic control algorithms to handle. Also, our algorithm when evaluated against other conventional learning algorithms can be a more powerful tool with the capacity to learn features directly from data, making it an excellent choice for such robotics applications in building construction. In other words, our algorithm can teach robots the ability to “work,” “think,” “know,” and “understand” their surroundings. It can also improve customer satisfaction, speed up the building process, and improve the productivity of building development teams. This chapter, however, contributes to classifications of autonomous robotics application development in construction literature. Although the problem addressed in this chapter is based on building construction, the algorithms presented are designed to be generalizable to related tasks.

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Bottlenecks to the Implementation of Automation and Robotics in the Construction Industry

Boya,

Akinradewo,

Aigbavboa

et al. 2023

Lecture Notes in Civil Engineering

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Human Factors in Introducing On‐Site Construction Automation

Cited by 13 publications

References 13 publications

Modeling the robotics implementation barriers for construction projects in developing countries

Modeling the robotics implementation barriers for construction projects in developing countries

Deep Neural Networks for Unsupervised Robotics in Building Constructions: A Priority Area of the Fourth Industrial Revolution (4IR)

Bottlenecks to the Implementation of Automation and Robotics in the Construction Industry

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