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

摘要： 现行道路工程全线建设安全风险预测与控制方法存在一定的滞后性、二维化和策略单一粗暴等缺陷,不能较好地满足当前中大型道路工程建设管理需求。文章基于历史真实案例与特征提取评价方法,构建道路工程施工内容与事故数据库,并运用神经网络进行工程进度-风险联合预测;从风险溯源和动态优化角度,在三维建模基础上结合工程管理目标构建闭环管理的7D一体式道路工程全线建设安全风险预测与控制方法模型,并以西阜高速全线建设为实例展开论证。证明中大型道路工程的安全风险预测与控制能力,不仅受限于作业环境与工艺工序、器械条件等内容,也与施工进度、成本计划及监管方式等息息相关,具有多维内在联系,需要综合考量;同时也论证了基于道路工程全线建设综合管理目标的方法模型对于实际施工的指导优化作用和应用价值,厘清了工程进度和风险间的内在联系,并明确了安全管理策略对于工程作业的保障机制。

关键词: 道路工程, 安全风险, 预测与控制, 7D模型