A Study on AI-Empowered Behavior Risk Identification and Early Warning in Nuclear Power Engineering Construction

Any risks arising during the construction phase of nuclear power projects become permanently embedded in the power station’s lifecycle, evolving into inherent and difficult-to-alter potential hazards. Consequently, identifying behavioral risks in this phase is critical to the successful delivery of nuclear power engineering projects. This paper proposes a behavior risk identification and early warning methodology for nuclear power construction operations based on artificial intelligence algorithms. The research employs text mining techniques to construct a risk indicator system for nuclear power construction operations; based on the You Only Look Once (YOLOv8) algorithm, it incorporates modules such as Deformable Convolutional Network (DCN), Generalized Lightweight Attention Network (GELAN), Efficient Channel Attention (ECA), and Atrous Spatial Pyramid Pooling (ASPP) to develop the DCN -GELAN-ECA- ASPP-YOLO for Nuclear Power Engineering (DGEAYoLo-NPE) model, and designs and develops a supporting behavior risk identification and early warning methodology. Results show that the precision of nuclear power construction behavioral risk detection reaches 94.3%, with a 2.2% improvement in precision. This study confirms that artificial intelligence technology can effectively enhance the behavior risk prevention and control capabilities of nuclear power construction operations.