Driven Digital Decision Support for Operational Efficiency in Sustainable Construction
Keywords:
Large Language Models, Digital Decision Support, Sustainable Construction, Operational EfficiencyAbstract
The increasing complexity of construction operations requires decision-support mechanisms capable of integrating heterogeneous information, interpreting operational conditions, and supporting timely managerial decisions. Large Language Models (LLMs) provide a potentially important interface for such decision support because they can transform natural-language information into structured recommendations, explanations, and decision alternatives. However, the effectiveness of an LLM-driven system depends not only on language-generation capability but also on the underlying computational architecture, learning strategy, data interpretation, and reliability of automated decisions. This research and review paper develops a conceptual framework for LLM-driven digital decision support in sustainable construction by synthesizing the computational foundations represented in the provided literature. The reviewed studies demonstrate the evolution of neural-network architectures, evolutionary computation, genetic programming, deep learning, and automated architecture optimization. These foundations are interpreted as enabling technologies for constructing adaptive decision-support pipelines, while the application of LLMs to sustainable construction is positioned as a forward-looking extension rather than a capability directly demonstrated by the cited studies. The proposed framework integrates data acquisition, LLM-based interpretation, computational optimization, decision evaluation, human validation, and sustainability-oriented operational feedback. The analysis indicates that LLM-driven decision support can potentially improve information accessibility, scenario comparison, coordination, and operational responsiveness, but its value depends on model reliability, explainability, data quality, computational efficiency, and human oversight. The study contributes a theoretically grounded framework for connecting advanced learning systems with operational decision-making in sustainable construction and identifies research directions for validating such systems in real construction environments.
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Copyright (c) 2026 Chinedu Okafor, Fatima Ibrahim (Author)
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