RESEARCH-ARTICLE
A Review of Research Progress on Digital and Intelligent Management Platforms for Whole-Process Documentation at Construction Sites
The digital and intelligent management platform for whole-process documentation at construction sites represents a key technological direction for promoting the transformation, upgrading, and management efficiency of the construction industry. This paper systematically reviews the core architecture, key technical modules, and implementation pathways of such a platform, aiming to provide a systematic solution to long-standing issues in traditional construction document management, such as fragmentation, inefficiency, and information silos.
Authors: SHENGFANG QIAO, CHUNLIN LIU, SHUO ZHANG
Key Metrics & Impact
Quantifying the transformative power of digital and intelligent platforms in construction documentation management.
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Efficiency Improvement
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Data Accuracy
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Time Savings
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Reduced Errors
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 Document Management
The platform addresses traditional issues like fragmentation and inefficiency by integrating whole-process data, ensuring real-time information sharing and intelligent analysis, and providing a closed loop for digital preservation and compliant handover.
Digitalization Strategies
Key strategies include a paperless field collection system using mobile terminals with offline synchronization, and advanced digitization of paper-based documents through OCR and CNN for high-precision recognition and intelligent archiving.
Intelligent Management
This involves an integrated correlation analysis and intelligent decision-support system leveraging IoT, BIM, machine learning, and reinforcement learning for dynamic early warning and optimized control of risks.
Big Data Analytics
The platform deeply integrates big data analytics with AI technologies to drive a paradigm shift from "experience-driven" to "data-driven" construction management, significantly improving accuracy, collaboration, and foresight.
75%
Improvement in overall project management efficiency with digital platforms.
Enterprise Process Flow
Paperless Field Collection
→
Document Digitization & Classification
→
Correlation Analysis & Decision Support
→
Automated Document Assembly
| Feature | Traditional Approach | Digital/Intelligent Platform |
|---|---|---|
| Data Collection | Manual, paper-based, error-prone |
|
| Document Processing | Manual classification, slow, inconsistent |
|
| Decision Support | Experience-driven, reactive |
|
| Archiving & Handover | Fragmented, labor-intensive |
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Case Study: North Yanjiang Expressway Project
The North Yanjiang Expressway project successfully implemented an intelligent document management system, meticulously categorizing project archives into 11 major classifications based on industry standards. The system's configurable rule engine allowed for predefined assembly logic, document lists, and output templates for various scenarios, ensuring standardized processes and compliant results. This approach significantly reduced the time for compiling completion documentation from several weeks to just days, demonstrating substantial labor savings and enhanced accuracy in archives.
- Achieved meticulous categorization into 11 major classifications.
- Implemented configurable rule engine for automated assembly.
- Reduced completion documentation time from weeks to days.
- Ensured standardized processes and compliant output.
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Estimated Annual Savings
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Hours Reclaimed Annually
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Your Journey to AI Transformation
A typical phased approach to integrating advanced AI into your enterprise operations for maximum impact.
Phase 1: Foundation & Data Integration
Establish a unified digital repository, integrate IoT, BIM, and multi-source data feeds for comprehensive data collection.
Phase 2: Intelligent Processing & Analysis
Implement OCR, CNNs for document digitization and classification, and machine learning models for correlation analysis and decision support.
Phase 3: Automated Documentation & Archiving
Develop rule engines and intelligent matching for automated document assembly, ensuring compliance and rapid compilation for various scenarios.
Phase 4: Adaptive Optimization & Digital Twin Integration
Continuously optimize algorithms, establish unified data standards, and integrate with digital twin technologies for adaptive intelligent control.
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