The digital transformation of construction is increasingly shaped by the convergence of intelligent perception, automated physical systems, machine learning, and language-based artificial intelligence. This research and review paper examines how robotics and large language models (LLMs) can be conceptually integrated into sustainable construction operations through a layered digital transformation framework. Because the supplied literature primarily addresses computer vision, automated defect detection, machine learning, image classification, and human fatigue rather than construction-specific LLM applications, the paper develops a theoretically grounded synthesis that transfers these capabilities into construction contexts without attributing unsupported findings to the cited studies. The proposed framework connects four functional layers: visual perception and condition assessment, intelligent interpretation and reasoning, robotic execution, and sustainability-oriented operational feedback. Computer-vision research demonstrates the feasibility of automated visual recognition and defect classification, while machine-learning studies establish the importance of automated inspection and pattern recognition (Ghorai et al., 2012; Krummenacher et al., 2017). Large-scale image-recognition research further provides a foundation for scalable perception systems (Deng et al., 2009; Russakovsky et al., 2015). The paper argues that LLMs can function as an orchestration and knowledge-interface layer between human operators, perception systems, and robotic platforms, while robotics provides the physical execution capability required to transform digital decisions into construction actions. The resulting framework emphasizes interoperability, human oversight, safety, explainability, data quality, and sustainability metrics. Findings indicate that the strongest transformation potential arises not from replacing individual construction functions, but from integrating perception, reasoning, and physical action into a closed-loop cyber-physical workflow. The study contributes a research framework for future empirical validation of robotics-LLM systems in sustainable construction.

Robotics, Large Language Models, Digital Transformation, Sustainable Construction

JAI, 2022, vol. 4, no. 4, 257.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conf. on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 248–255, 2009.

S. Ghorai, A. Mukherjee, M. Gangadaran and P. K. Dutta, “Automatic defect detection on hot-rolled flat steel products,” IEEE Transactions, vol. 62, no. 3, pp. 612–621, 2012.

X. Feng, X. Gao and L. Luo, “X-SDD: A new benchmark for hot rolled steel strip surface defects detection,” Symmetry, vol. 13, no. 4, pp. 706, 2021.

P. Kapsalas, P. Maravelaki, M. Zervakis, E. Delegou and A. Moropoulou, “Optical inspection for quantification of decay on stone surfaces,” NDT & E International, vol. 40, no. 1, pp. 2–11, 2007.

G. Krummenacher, C. Ong, S. Koller, S. Kobayashi and J. Buhmann, “Wheel defect detection with machine learning,” IEEE Transactions, vol. 19, no. 4, pp. 1176–1187, 2017.

Krizhevsky, I. Sutskever and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communication ACM, vol. 60, no. 6, pp. 84–90, 2017.

Kuo, C. Lai, C. Kao and C. Chiu, “Integrating image processing and classification technology into automated polarizing film defect inspection,” Optical Lasers Engineering, vol. 104, pp. 204–219, 2018.

J. Mascord and R. A. Heath, “Behavioral and physiological indices of fatigue in a visual tracking task,” Journal of Safety Research, vol. 23, no. 1, pp. 19–25, 1992.

O. Russakovsky et al., “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision, Santiago, Chile, vol. 115, pp. 211–252, 2015.

J. Seghers, A. Jochem and A. Spaepen, “Posture, muscle activity and muscle fatigue in prolonged VDT work at different screen height settings,” Ergonomics, vol. 46, no. 7, pp. 714–730, 2003.

Y. Xie, Y. Ye, J. Zhang, L. Liu and L. Liu, “A Physics-based defects model and inspection algorithm for automatic visual inspection,” Optical Lasers Engineering, vol. 52, pp. 218–223, 2014.

Ramamurthy, K. (2023). “AI-Driven Test Automation Frameworks for the Modern Software Quality Engineering.” International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 257–269.

Geo Philip, Paulson, Robotics-Enabled Sustainable Construction Management: A Socio-Technical Framework for Operational Efficiency, Digital Integration, and Sustainability Performance. Available at SSRN: https://ssrn.com/abstract=6845167 or http://dx.doi.org/10.2139/ssrn.6845167