The convergence of large language models (LLMs), artificial intelligence (AI), and construction robotics presents an emerging pathway for improving operational sustainability in construction environments. Construction projects generate heterogeneous information through schedules, inspection records, equipment observations, safety reports, environmental measurements, and operational communications. Conventional automation systems generally perform specialized tasks but have limited capability to interpret unstructured information and translate it into coordinated operational decisions. This research develops a conceptual socio-technical framework in which LLM-based intelligence functions as an interpretive and decision-support layer between construction information and robotic execution. The methodological foundation is derived exclusively from the supplied literature, particularly studies demonstrating AI-based image analysis, deep-learning classification, feature extraction, and digital dashboard-based monitoring. Although the provided references primarily concern healthcare and public-health applications rather than construction, their technical principles provide transferable evidence concerning data-driven detection, multimodal interpretation, automated monitoring, and decision support. The proposed framework integrates data acquisition, LLM reasoning, robotic task planning, sustainability assessment, human oversight, and feedback mechanisms. The analysis indicates that LLM-robotics integration can potentially improve operational coordination, resource utilization, anomaly detection, and adaptive task management, while simultaneously introducing challenges involving data quality, model reliability, explainability, cybersecurity, human responsibility, and domain transferability. The paper contributes a research-oriented framework for understanding how language intelligence can complement robotic capabilities rather than replace human construction expertise. Future empirical research should validate the framework through construction-specific datasets, controlled robotic experiments, and quantitative sustainability indicators. 

Large Language Models, Construction Robotics, Artificial Intelligence, Operational Sustainability

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