Construction Safety AI Intelligence

　　I. Core Challenges of AI Implementation in Construction Safety in 2025

　　Data Governance Bottlenecks: Construction site data is multi-source and heterogeneous, encompassing video streams, sensor data, and equipment records. Unstructured data accounts for over 70%, and there is a lack of unified collection standards. Cross-section data integration efficiency is less than 30%. Data cleaning and scenario-based annotation costs account for 45%-55% of total AI project investment, severely limiting model iteration efficiency.

　　Technical Adaptation and Cost Pressures: Traditional construction environments present challenges such as dust, vibration, and variable lighting, leading to a high false alarm rate of 15%-20% for AI visual recognition. Small and medium-sized construction companies face an average initial investment of 500,000 to 2 million yuan for deploying AI systems. The cost of leasing public cloud computing power increases with the number of monitoring points, resulting in generally low cost acceptance.

　　Implementation and Ethical Gap: The supply-demand ratio for talent with combined "AI technology + safety management" skills is 1:12, and over 35% of projects face inadequate adaptation of AI models to construction processes. At the same time, ethical issues such as accident responsibility attribution and personal privacy protection lack clear regulations, raising concerns about application.

　　The Dilemma of Lack of Standardization: Currently, there is no unified industry standard for AI applications in building safety. Data interfaces and recognition thresholds vary significantly across systems, making cross-enterprise data sharing and regulatory coordination difficult, hindering large-scale deployment.

　　II. Core Technology Support Solution

　　Edge-AI Collaborative Computing Architecture: Utilizing a hybrid "edge node + cloud hub" model, edge devices (such as smart cameras and inspection robots) are equipped with lightweight models for local real-time inference (response time ≤ 0.5 seconds), while the cloud is responsible for model training and data accumulation. This reduces computing power and transmission costs by 40%-50% compared to pure cloud-based architectures and is also adaptable to complex scenarios such as dust and nighttime conditions.

　　Multimodal Fusion Recognition Technology: Integrating computer vision, sensor data, and acoustic monitoring, deep learning algorithms enable three-dimensional recognition of "behavior-environment-device." For example, by combining visual algorithms to identify people not wearing hard hats and linking them with vibration sensors to detect abnormal equipment noise, the recognition accuracy rate has increased to over 92%, while the false alarm rate has dropped to below 8%.

　　The low-code AI training platform provides a visual tool for the entire process from "hazard identification - model fine-tuning - deployment iteration." It comes with 12 pre-installed construction safety templates, including "electricity violations" and "channel occupancy." It supports construction workers uploading field data for model optimization, reducing the scenario adaptation cycle from three months to two weeks.

　　The intelligent linkage and governance system establishes a closed-loop mechanism for "identification - early warning - action." The AI system can link with equipment such as distribution boxes and sprinklers to automatically mitigate risks. Federated learning technology makes data "available but invisible," and the SHAP algorithm is combined to enhance model interpretability, meeting privacy protection and compliance requirements.

　　III. Application Practices in Key Scenarios

　　Full-Process Personnel Safety Control: Multispectral cameras monitor personnel behavior 24/7, accurately identifying behaviors such as not wearing a helmet, smoking violations, and not wearing a safety belt while working at height. Upon detection, on-site voice warnings and mobile alerts are triggered within 10 seconds. After implementation on a large project, personnel violations decreased by 68% and related accident rates by 50%.

　　Equipment and Electrical Safety Monitoring: This system provides real-time identification of limiters and brakes on equipment such as tower cranes and hoists, automatically generating monthly safety assessment reports and achieving over 90% accuracy in equipment failure warnings. The system integrates visual recognition with current sensors to detect violations such as submerged cables and overloaded power, enabling coordinated power shutdown in high-risk scenarios and reducing electrical accidents by 72%.

　　Environmental and Material Control: This system automatically identifies issues such as excessive material stacking heights and the mixing of flammable materials. It monitors safety aisle widths to ensure they meet the 1.2m or higher standard, and immediately issues corrective actions when occupancy is detected. Application on one project reduced the response time for correcting aisle blockage hazards from one hour to 15 minutes.

　　Intelligent Inspection and Fire Prevention: Inspection robots equipped with lidar and high-definition cameras autonomously plan routes for comprehensive inspections, increasing efficiency by over three times compared to manual labor. Integrating smoke and flame recognition technology, they immediately activate fire sprinklers upon detecting potential hazards, increasing rescue time and improving fire hazard management efficiency by 80%.

　　IV. Development Trends 2025-2027

　　Deep Integration of BIM and AI: AI technology will be embedded in Building Information Models (BIM), enabling dynamic mapping of construction progress and safety risks. By simulating construction scenarios through digital twins, risks such as cross-operation collisions and equipment interference can be predicted in advance, reducing risk prediction cycles from hours to days.

　　Scaling Industry-Customized Models: The transformation of general-purpose large models into construction-specific models will be achieved, with optimized training data for specific scenarios such as high-rise buildings and underground projects. Model recognition accuracy will exceed 95%, reducing scenario adaptation costs by 30%-40%.

　　Standardization and Regulatory Collaboration: The industry will introduce unified standards for AI recognition thresholds and data interfaces, establish a cross-enterprise secure data sharing platform, and realize a coordinated AI system model of "enterprise self-inspection and government oversight," improving regulatory response efficiency by over 50%.

　　Promoting Green and Low-Energy Technologies: The development of low-energy AI models will be accelerated. Model distillation technology will reduce parameter size by over 70%. Combined with photovoltaic-powered edge devices, this will reduce AI system energy consumption by 40%, meeting the carbon neutrality needs of the construction industry.