The construction industry is increasingly characterized by complex resource coordination, fragmented workflows, schedule uncertainty, safety constraints, and growing sustainability requirements. Artificial intelligence (AI), robotics, cloud manufacturing principles, reinforcement learning, and intelligent resource-allocation mechanisms provide an emerging technological basis for addressing these challenges. This research develops an AI-Robotics Framework for Operational Efficiency and Sustainability in Construction Management by synthesizing concepts from cloud manufacturing, resource-service composition, intelligent scheduling, reinforcement learning, multi-objective optimization, and automated negotiation. The study adopts a conceptual research and review methodology based exclusively on the supplied literature and translates its manufacturing-oriented principles into a construction-management context. The proposed framework consists of five interconnected layers: data and sensing, intelligent resource orchestration, AI decision optimization, robotic execution, and sustainability-performance feedback. The analysis indicates that operational efficiency can be improved when AI-based decision mechanisms and robotic systems are integrated rather than deployed as isolated technologies. Cloud-oriented resource coordination provides scalability, reinforcement learning enables adaptive allocation, multi-objective optimization supports simultaneous consideration of productivity and sustainability, and automated negotiation can facilitate coordination among multiple project stakeholders. The framework further emphasizes continuous feedback between construction-site operations and management decisions. Its principal contribution is a theoretically grounded architecture for integrating AI and robotics with construction-resource management while maintaining sustainability as an explicit optimization objective. The study also identifies limitations associated with interoperability, data quality, organizational readiness, computational complexity, and the transferability of manufacturing-oriented models to construction environments.
An AI-Robotics Framework for Operational Efficiency and Sustainability in Construction Management
Abstract
References
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