关键词: 道路工程, 安全风险, 预测与控制, 7D模型

Abstract: The existing methods for predicting and controlling construction risks in the scope of entire road engineering projects exhibit certain limitations, such as latency, lack of comprehensive analysis, and reliance on a single-dimensional approach. These shortcomings fail to effectively address the safety demands of medium to large-scale road engineering projects. This paper aims to address these issues by exploring the influencing factors, evolution mechanisms, development trends, and management strategies of engineering risks, based on the objectives of safety risk prediction and control in the construction of road engineering projects, and supplemented with numerous real historical engineering cases.
Initially, the paper conducts a work breakdown structure (WBS) analysis and quantifies key features of the case data to elucidate the primary influencing factors of engineering construction activities and accidents. Subsequently, it employs backpropagation neural network (BPNN) to jointly predict project progress and risks, and establishes a problem traceability system, countermeasure library, and early warning response system. Furthermore, it designs a BIM+GIS three-dimensional dynamic safety management system, integrating digital twins and virtual reality technology to construct a visual 7D model for the integrated management of safety risk prediction and control based on on-site three-dimensional modeling, engineering progress, risks, costs, and quality, using the example of the full-line construction of the Xifu Expressway for practical demonstration.
The construction of the Xifu Expressway encompasses various terrains, including mountainous, hilly, plain, swamp, river, and wetland areas, with diverse weather conditions throughout the year. It involves a wide range of complex tasks, including extra-large tunnel and bridge projects, making it a representative case for extensive research. The method model proposed in this paper has significantly optimized the practical application of the full-line construction of the Xifu Expressway: achieving over 96% accuracy in identifying engineering construction progress, exceeding 97% accuracy in joint prediction of risks based on progress control, and reducing the construction accident rate by 93% compared to similar domestic projects. Additionally, the resources utilization rate of construction enterprises has increased by 12%, the average operating efficiency has improved by 9%, and the average unit construction cost has decreased by 7%. This demonstrates that effective safety risk management leads to precise prediction and control of accident precursors and risk signs, resulting in high quality, a shorter construction period, and lower investment compared to similar projects at home and abroad.
The research results indicate that the closed-loop 7D integrated engineering three-dimensional (BIM+GIS)-progress-risk-cost-quality integrated management method model offers several advantages, including accurate analysis, information sharing, scientific control, and the combination of virtual and real aspects, thus possessing high scientific and practical value. This model, based on real case data and multi-dimensional information linkage, overcomes the subjective limitations of traditional expert experience, analytic hierarchy process (AHP), and fuzzy comprehensive evaluation methods. By jointly analyzing engineering progress and risks, it improves the predictive shortcomings of previous neural network algorithms. Through the integration of engineering quality and cost, it refines the prevention and optimization of construction safety risks and clarifies the causal relationship between project management goals and engineering safety risks.
Furthermore, the research emphasizes the importance of rational optimization and scientific decision-making measures in reducing the probability of accidents. It underscores the necessity of supporting management systems and system construction. With the continuous progress of information technology and the development of new theories, technologies, and algorithm models, the research on safety risk prediction and control in full-line road engineering construction is expected to become more comprehensive and in-depth, better meeting engineering needs.

Key words: road engineering, safety risk, prediction and control, 7D model