Research Progress on Intelligent Dam Engineering in The Smart Era

I. Background and Significance

In the context of the smart era, rapid advancements in artificial intelligence (AI), the Internet of Things (IoT), and big data are driving the intelligent transformation of traditional hydraulic engineering. As the core structure of water infrastructure, intelligent dam construction (ICDAM)—encompassing data-intelligence integrated design, smart construction, and intelligent operation and maintenance (O&M)—has become pivotal for enhancing project quality, efficiency, and safety while fostering "high-tech, high-efficiency, high-quality" productivity. Policy initiatives in China, such as the Guidelines for Smart Water Conservancy Development and the Digital China Strategic Plan, further accelerate the leap from "digital dams" to "smart dams."

II. Bibliometric Analysis

1. Research Trends

1.1 Timeline Analysis:

• Pre-2015: Focused on digitization and informatization.

• Post-2015: Surge in intelligent research (deep learning, digital twins, autonomous equipment).

1.2 Regional Focus:

• Chinese literature: Emphasizes engineering practices (smart water management, digital twins).

• International literature: Prioritizes algorithm development (neural networks, deep learning).

1.3 Global Collaboration:

• China leads global research with partnerships (U.S., Iran, India), producing 3× more outputs than other nations.

2. Hot Research Areas

Intelligent monitoring, digital twins, large language models (LLMs), generative AI, unmanned compaction fleets.

III. Core Concepts and Features of Intelligent Dam Construction

1. Development Stages

• Stage 1.0: Task-specific data-driven systems (e.g., smart monitoring, unmanned compaction).

• Stage 2.0: Hybrid data-mechanism models for reliable intelligence (e.g., smart simulation, cluster collaboration).

• Stage 3.0: Autonomous intelligence (IDAM-AGI) enabling full lifecycle adaptive decision-making.

2. Key Features

2.1 Data-Mechanism Integrated Design:

• Optimized workflows using surrogate models and physics-informed neural networks (PINN).
  2.2 Smart Construction:

• Bidirectional AI-data synergy: unstructured data fusion, AI-enhanced data quality.

• Autonomous equipment: collaborative unmanned rollers, intelligent vibratory robots.
  2.3 Virtual-Physical Symbiosis:

• Digital twins dynamically mirror physical dams for predictive optimization.

IV. Innovative Advances

1. Intelligent Design

1.1 Optimization Algorithms:

• Swarm intelligence (ACO, PSO) for dam morphology optimization, reducing costs and environmental impacts.
  1.2 Novel Structures:

• Functionally graded partition structures (FGPS) enhance impermeability.

• MgO-based cement mitigates temperature control conflicts.

2. Smart Construction

2.1 Dynamic Simulation:

• Real-time parameter updates via digital twins (e.g., Lianghekou Dam).
  2.2 Intelligent Monitoring:

• Gravel uniformity detection (machine vision), truck identification (improved YOLO models).

• Vibration quality analysis (ResNet-50, vibration video recognition).
  2.3 Autonomous Equipment:

• Unmanned roller fleets (Lianghekou, Shuangjiangkou projects).

• AI-powered vibrating robots (Baihetan Dam).
  2.4 Smart Grouting:

• LLM-based cross-modal parameter prediction, 3D fracture modeling (NURBS-TIN-Brep).

3. Intelligent Operation

3.1 Behavioral Analysis:

• ML-driven prediction of deformation/seepage risks (XGBoost, LSTM).
  3.2 Flood Forecasting:

• Spatiotemporal attention mechanisms improve Three Gorges Reservoir accuracy.
  3.3 Maintenance:

• Data-driven fault prediction and strategy optimization.

V. Challenges

1. Data & Models:

• Manual data entry limits timeliness; universal foundation models lack domain-specific generalization.

2. Technology:

• Complex coordination of heterogeneous equipment clusters; fragmented digital twin platforms.

3. Standards:

• Current codes inadequately address intelligent needs (e.g., dynamic temperature control).

VI. Future Directions

1. Universal AI Models:

• Develop IDAM-AGI integrating multi-source data and knowledge graphs.

2. Human-Machine Symbiosis:

• Evolve self-adaptive equipment clusters (e.g., autonomous rollers).

3. Deep Data-Mechanism Fusion:

• Enhance reliability via PINN; hybrid-driven O&M optimization.

4. Policy & Standards:

• Establish codes incorporating SR Method (full-process simulation + strength reduction).

VII. Conclusion

Intelligent dam construction has transitioned from Stage 1.0 (digitization) to Stage 2.0 (smart systems), significantly improving design, construction, and O&M efficiency. Advancing to Stage 3.0 will require universal AI models, autonomous equipment, and deep data-mechanism fusion, ultimately realizing a "crack-free" high-safety, high-efficiency dam ecosystem. This evolution will provide foundational support for China’s high-quality hydraulic engineering development and set a global benchmark for intelligent infrastructure.

BGT Hydromet committed to the field of intelligent perception safety monitoring of DAMS, we have participated in the construction of nearly 3,000 small reservoirs in China for rainwater conditions and dam safety monitoring services, providing accurate information guarantee for reservoir flood control dispatching, forecasting and early warning.