Enterprise AI Analysis
Systematic Literature Review of Human-AI Collaboration for Intelligent Construction
This study conducts a systematic review of prior research on human-AI collaboration (HAIC) in intelligent construction. It screens 191 highly cited articles, identifies four research streams (construction robotics, productivity and safety, intelligent algorithms and modeling, and construction workers), and develops a three-dimensional knowledge framework (technical, application, management layers). The review reveals the co-evolutionary path of AI and industry digital transformation, highlighting gaps and future directions in HAIC.
Executive Impact & AI-Driven Value
Intelligent Construction is a key pillar of the global economy, yet it faces significant challenges. AI, particularly Human-AI Collaboration (HAIC), offers a disruptive revolution. This analysis provides actionable insights for digital transformation, enhancing safety, productivity, and decision-making across the construction lifecycle.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Construction Robotics
Explores the application of construction robots, human-AI collaboration technologies, and development trends in construction robotics. Focuses on improving efficiency and safety in various construction scenarios.
- Robotic disc cutter replacement in shield machines [22]
- Teleoperation challenges in excavator automation [27]
- Unmanned rolling compaction system [13]
- Hybrid human-machine manufacturing systems [28]
- Mixed reality for human-AI inspection collaboration [29]
- Deep reinforcement learning for safe robot path planning [33]
Productivity and Safety
Addresses the intertwined issues of productivity enhancement and worker safety, exploring strategies and methods to achieve improvements through ergonomics, physical/mental safety analysis, and safety training/monitoring.
- Ergonomics in construction and worker health [39]
- Noise annoyance assessment for shield tunneling machine drivers [41]
- Human-drone collaboration risks [42]
- Unsupervised Multi-Anomaly GAN for unsafe behavior detection [44]
- Improved weighted fuzzy CREAM model for human reliability assessment [47]
- Federated learning for cyber faults in smart buildings [49]
Intelligent Algorithms and Modeling
Investigates the combination of AI with BIM, digital twins, machine learning, and visualization technologies for planning, construction, and management, emphasizing data-driven optimization and transparency.
- BIM and AR for human-building interaction [51]
- Digital twin model consistency in Human-AI collaboration [52]
- Machine learning in building energy management [57]
- SVM and PSO for shield tunneling parameter matching [59]
- Extended TAM model for VR acceptance in construction [65]
- Trustworthy AI and robotics in the AEC industry [62]
Construction Workers and Human-AI Collaboration
Focuses on human factors, including physiological, psychological, and behavioral characteristics, to achieve efficient and safe human-AI collaboration, addressing trust dynamics, human error, and collaborative models.
- Agent-based modeling for human-AI collaboration in bricklaying [67]
- Evaluation of human-AI collaboration in masonry work using immersive virtual environments [68]
- Emotional dynamics in collaborative decision-making [70]
- EEG-based trust recognition in collaborative robots [72]
- Interrelation between human-factor-related accidents and work patterns [76]
- Human error identification and analysis for shield machine operation [77]
191
Highly Cited Articles Reviewed
The study rigorously screened 191 highly cited articles from the past five years across Scopus, Google Scholar, and WOS, representing the top 10% by citation count.
Systematic Review Methodology
Search & Exclusion
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Literature Analysis
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Results & Discussion
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Integration Framework
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Future Research
| Challenge Area | Current AI Limitations | Proposed AI/HAIC Solutions |
|---|---|---|
| Construction Robotics Application |
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| Productivity & Safety |
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Human-AI Trust Dynamics in Virtual Construction
A study examined trust dynamics in human-robot collaboration during virtual construction tasks. It identified key robot interaction factors influencing trust through psychophysiological responses (EDA, EEG emotional metrics). This highlights the critical need for ergonomic design parameters in intelligent planning for multi-purpose utility tunnels, considering workers' physiological and psychological characteristics to create comfortable, efficient, and safe environments.
Source: Chauhan et al. [73]
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Key Research Streams Identified
Four dominant research streams were identified through co-citation analysis and clustering: construction robotics, productivity and safety, intelligent algorithms and modeling, and construction worker factors.
Calculate Your Potential AI Impact
Estimate the potential efficiency gains and cost savings for your enterprise by implementing Human-AI Collaboration solutions.
Projected Annual Savings
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Your HAIC Implementation Roadmap
A strategic phased approach to integrating Human-AI Collaboration into your construction operations.
Phase 1: Needs Assessment & Pilot (3-6 Months)
Conduct a comprehensive audit of current construction workflows, identify key pain points for AI integration, and select a pilot project. Define clear KPIs for efficiency, safety, and human-AI interaction. Initiate development of explainable AI (XAI) modules for selected tasks and begin initial worker training in VR/AR environments for robot collaboration protocols.
Phase 2: System Development & Integration (6-12 Months)
Develop and integrate AI algorithms for dynamic task allocation and real-time risk assessment, leveraging IoT sensors and digital twin technologies. Implement human-centered interfaces for collaborative robots, focusing on intuitive control and feedback. Conduct iterative testing and refinement of HAIC systems based on pilot project data and worker feedback, adjusting for ergonomic and psychological factors.
Phase 3: Scalable Deployment & Continuous Improvement (12-24 Months)
Roll out HAIC systems across broader construction operations. Establish a continuous monitoring framework for trust, safety, and productivity metrics. Develop robust AI governance policies, including ethical guidelines and data security protocols. Implement advanced training programs and foster a culture of human-AI collaboration, ensuring adaptability to diverse regional needs and evolving technological landscapes.
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