| | | Justification for the Research | National, as well as international economies, are greatly influenced by building. In affluent nations, the industry accounts for 10% of GDP; in underdeveloped ones, it accounts for 25% or more (Noghabaei et al., 2020). On the other hand, construction uses significantly less automation than some other sectors, such as manufacturing. This has a negative impact on production and the safety of the workforce. The construction sector has several automation and robotics implementation challenges that may be addressed. Robotic controlling, detection, vision, translation, mapping, and planning components have made considerable advancements during the last several decades due to the fast growth of computer software and hardware. This research aims to give a glimpse into the innovative management strategies influencing modern infrastructure and building construction and how they can inspire businesses and academic institutions to adopt automation and robotic systems out of their practices. This article concentrates specifically on research and studies that illustrate how robotic process automation technology and tools may be implemented in the building of structures and facilities. There are several challenges that may be solved via automation or robotics or sophisticated management approaches among the papers presented. This research offers a wide range of new research and studies aimed at establishing new benchmarks in the industry.
There are many jobs that robots can do, such as those in the plastics and hardware manufacturing industries, that are laborious and uninteresting for humans to do. Robotic arms outfitted with magnetic sheets, grippers, and suction cups are often used in conjunction with belt conveyors to assist throughout the assembling of industrial machinery (Delgado et al., 2019). Although immobile, these robotic arms have numerous degrees of freedom, allowing them to move in many different directions and angles with simplicity and agility.
Robots may also be programmed to carry out complex tasks needing a high degree of precision, which is generally beyond the capability of humans. A job may be repeated with little or no mistake if programmed correctly. With the abundance of sensors for proximity and pressure, some of these robots can achieve astonishing precision and operate with great care on exceedingly delicate materials (Cross, Hortensius, and Wykowska, 2019).
Building information modelling (BIM) and light detection and ranging (LiDAR) were combined in a system developed for on-site data gathering and quality monitoring during construction. Instead of wasting time inspecting at particular locations, quality managers may use this tool to swiftly and reliably detect and handle issues (Xu et al., 2021). With the use of numerous case studies, researchers discovered that a laser-scanned as-built model might be used in quality assurance procedures of Liquid and Gas (LNG) plant development. These technologies are not the only ones that can be utilized to assist the management make better re-planning choices as early as feasible during construction. Cloud services may be used to build communications across these settings. Researchers Cheng et al. established the Construction Quality Supervision Collaboration System (CQSCS), which seems to be a SaaS private cloud-integrated system for improving construction quality management and oversight (Tay et al., 2017).
By providing an ontological and syntactic approach, several researchers studied the possibility of merging process management with risk management. The suggested risk-oriented ontology model was tested using a case study of a bored pile retaining wall. To provide a healthy working environment, safety education is also required. A framework has been developed for adaptive safety assessments over time concerning surface road operations. It is possible to use it as a safety assurance tool in an ever-changing construction environment. |
| | | References
Cross, E.S., Hortensius, R. and Wykowska, A., 2019. From social brains to social robots: applying neurocognitive insights to human–robot interaction.
Delgado, J.M.D., Oyedele, L., Ajayi, A., Akanbi, L., Akinade, O., Bilal, M. and Owolabi, H., 2019. Robotics and automated systems in construction: Understanding industry-specific challenges for adoption. Journal of Building Engineering, 26, p.100868.
Edwards, A., Edwards, C., Westerman, D. and Spence, P.R., 2019. Initial expectations, interactions, and beyond with social robots. Computers in Human Behavior, 90, pp.308-314.
Flick, U. ed., 2017. The Sage handbook of qualitative data collection. Sage.
Liu, Z., Lu, Y. and Peh, L.C., 2019. A review and scientometric analysis of global building information modeling (BIM) research in the architecture, engineering and construction (AEC) industry. Buildings, 9(10), p.210.
Mohajan, H.K., 2018. Qualitative research methodology in social sciences and related subjects. Journal of Economic Development, Environment and People, 7(1), pp.23-48.
Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G. and Cavallo, F., 2019. A survey of behavioral models for social robots. Robotics, 8(3), p.54.
Noghabaei, M., Heydarian, A., Balali, V. and Han, K., 2020. Trend analysis on adoption of virtual and augmented reality in the architecture, engineering, and construction industry. Data, 5(1), p.26.
Tay, Y.W.D., Panda, B., Paul, S.C., Noor Mohamed, N.A., Tan, M.J. and Leong, K.F., 2017. 3D printing trends in building and construction industry: a review. Virtual and Physical Prototyping, 12(3), pp.261-276.
Xu, S., Wang, J., Shou, W., Ngo, T., Sadick, A.M. and Wang, X., 2021. Computer vision techniques in construction: a critical review. Archives of Computational Methods in Engineering, 28(5), pp.3383-3397. |
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