Feasibility of Robotic Technology for The Advancement of Construction Industry in Sri Lanka

Gamlath, Madhawa; Waidyasekara, K.G.A.S.; Pandithawatta, Sonali

doi:10.1109/mercon50084.2020.9185384

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…The emergence of digital technology and its promotion in the construction industry has the propensity to facilitate and improve the construction processes and operations towards quality. In the studies, the areas of digital technology application in QA processes in the "do" phase have been limited to improving maintenance management, construction/production process management, and operations [20,27,32]. Furthermore, digital technologies, such as AI, computer vision, photogrammetric vision, thermal cameras, etc., can be integrated into QA processes to ensure a reduction in errors and defects and check accuracy during the execution of projects [75][76][77].…”

Section: Domentioning

confidence: 99%

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Digital Technologies for Quality Assurance in the Construction Industry: Current Trend and Future Research Directions towards Industry 4.0

Ghansah,

Edwards

2024

Buildings

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Despite the growing rich and fragmented literature focusing on quality assurance (QA) and Industry 4.0, the implementation of associated individual digital technologies has not been fully evaluated and synthesised to achieve adequate QA in the construction industry; hence, it has received limited focus. This study, thus, aimed to organise, evaluate, and synthesise the current literature on individual digital technology applications in QA in the construction industry and propose future research directions. A literature review approach was adopted for this study along with Deming’s cycle framework to address four research questions: (1) What is the status of the state-of-the-art in the literature? (2) What digital technologies have been applied for QA in the construction industry? (3) Which areas in QA processes have experienced digital technology applications, and what are the applications? (4) What are the limitations of the existing studies and future research directions of digital technologies for QA in the construction industry? The findings showed an increasing trend of research on digital technology for QA in construction since 2017. This cuts across 23 countries with six different research methods published across 18 different publication sources. Four categories of digital technologies were revealed to have been adopted for QA in construction based on the functionality of the technologies: data collection technologies, decision-oriented technologies, collaborative technologies, and transparency and security-related technologies. Evaluation with Deming’s cycle framework revealed that digital technologies have a high level of application at the “do” phase, improving the quality management process during construction towards achieving pre-stated quality requirements. This includes mostly collaborative technologies, consisting of BIM technologies. Limitations of the existing studies were further identified, and this led to five research directions: interoperability of technology development, integrated digital technologies for QA of prefabricated and modular construction, integrated digital technologies for QA of cross-border construction logistics and supply chain, digital innovation for sustainable QA, and moving beyond the technical solution. The study showed a significant contribution to both academia and the industry in the built environment.

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Section: Domentioning

confidence: 99%

“…The proposed system was noted to be robust for project stakeholders to follow rules of standards on managing quality whilst ensuring efficient collaboration and communication among the stakeholders. However, digital technologies in QA are generally at a low technology readiness level, with limited application and evaluation [20].…”

Section: Introductionmentioning

confidence: 99%

Digital Technologies for Quality Assurance in the Construction Industry: Current Trend and Future Research Directions towards Industry 4.0

Ghansah,

Edwards

2024

Buildings

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“…However, Deep Learning's ability to imagine categorisation and pattern recognition provides compelling solutions. Embracing this technology, like other sectors, offers effective warehouse automation, cost savings, and increased response to changing market needs [25], [26]".…”

Section: Literature Reviewmentioning

confidence: 99%

“…The identified research gaps are as follows: The following are the research gaps that have been identified: (a) in comparison to other industries, the construction sector lacks digitalisation and adapts slowly to new technologies [26], (b) inadequate research and initiatives focusing on automation in construction inventory management [26]- [28], (c) the prevalence of improper inventory management techniques in the construction industry [13], [29], (d) the disadvantages of relying on manual and traditional inventory management solutions (on-site warehouses) [17], (e) Deep Learning's untapped potential for pattern identification, picture classification, and image modelling in construction inventory management [9], [22], [24], (f) the numerous applications for Deep Learning in construction inventory management [7], [10], [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of automation opportunities in warehouse management in construction supply chains using convolutional neural networks

Dissanayake,

Sugathadasa,

De Silva

2023

J. S. Asian Logistics Transport

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The building industry is strongly reliant on materials, which account for 55%-60% of its expenses. However, due to outdated and time-consuming approaches, inadequate inventory management prevails. This is where developing technologies such as Deep Learning (DL) might help uncover solutions. Surprisingly, very little scientific research on DL has been conducted for this purpose. As a result, this study looks into the prospect of automating construction warehouse management by employing CNN for object detection and counting. During the initial investigation, 23 studies out of 26 used Convolutional Neural Networks (CNN) for image processing and object detection. Secondly, a model was developed and compared its accuracy to that of human counting and discovered that the model outperformed people. Industry professionals were interviewed to discuss the findings. Industry professionals highlighted the advantages and disadvantages of using such an automated system in construction warehouses. In conclusion, this study shows that the CNN base model outperforms people in counting materials, and the proposed automated inventory management system has significant industry potential.

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Review of Robotics’ Role in Unsafe Site Conditions in the Construction Industry

Kumar,

Murugesan,

Sathyanarayanan

et al. 2023

Lecture Notes in Civil Engineering

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Feasibility of Robotic Technology for The Advancement of Construction Industry in Sri Lanka

Cited by 3 publications

References 12 publications

Digital Technologies for Quality Assurance in the Construction Industry: Current Trend and Future Research Directions towards Industry 4.0

Digital Technologies for Quality Assurance in the Construction Industry: Current Trend and Future Research Directions towards Industry 4.0

Investigation of automation opportunities in warehouse management in construction supply chains using convolutional neural networks

Review of Robotics’ Role in Unsafe Site Conditions in the Construction Industry

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