摘要
为制定低碳可持续的路网级桥梁全寿命期维养策略,提升环境、经济与安全综合效益,研究提出一种基于深度确定性策略梯度(DDPG)强化学习的优化方法。该方法结合桥梁结构特征、网络拓扑、交通数据以及风险态度,构建强化学习智能体,系统性优化了桥梁维养决策。环境、经济和安全指标的评估综合考虑资源消耗、潜在结构失效引发的后果及车辆绕行的影响等,量化了桥梁维养对可持续性能的贡献。在构建强化学习的奖励函数时,将可持续性指标转化为单调递减的效用值,以反映优化过程中的偏好与约束;基于强化学习框架,设计了包含深度神经网络的DDPG智能体,利用路网级桥梁的结构退化特征和交通数据进行试错学习,从而逐步优化维养决策策略。验证结果表明,所构建的强化学习方法在环境、经济和安全指标之间取得了更优的平衡,智能体通过试错学习捕捉了桥梁性能变化特征,有效优化了维养优先级和资源分配策略,为提升基础设施管理的智能化与可持续性提供了科学依据。
To develop a low-carbon and sustainable life-cycle maintenance strategy for network-level bridges and enhance the comprehensive benefits in environmental,economic,and safety dimensions,this study proposed an optimization method based on Deep Deterministic Policy Gradient(DDPG)reinforcement learning.This approach integrates bridge structural characteristics,network topology,traffic data,and risk attitudes to construct a reinforcement learning agent that systematically optimizes bridge maintenance decisions.Environmental,economic,and safety indicators were evaluated by comprehensively considering resource consumption,the consequences of potential structural failures,and impacts of vehicle detours,thereby quantifying the contribution of bridge maintenance to sustainability performance.In constructing the reward function for reinforcement learning,sustainability indicators were transformed into monotonically decreasing utility values to reflect the preferences and constraints in the optimization process.Based on a reinforcement learning framework,a DDPG agent with deep neural networks was designed,leveraging the structural degradation features and traffic data of network-level bridges for trial-and-error learning to progressively optimize maintenance decision strategies.The validation results indicated that the reinforcement learning method developed in this study achieves a better balance between environmental,economic,and safety metrics.Through trial-and-error learning,the agent captures the performance variation characteristics of bridges,optimizes maintenance priorities,and allocates resources efficiently.This approach provides a scientific basis for advancing intelligence and sustainability in infrastructure management.
作者
雷晓鸣
孙利民
董优
夏勇
LEI Xiao-ming;SUN Li-min;DONG You;XIA Yong(Department of Bridge engineering,Tongji University,Shanghai 200092,China;Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hong Kong 999077,China)
出处
《中国公路学报》
北大核心
2026年第1期78-86,共9页
China Journal of Highway and Transport
基金
香港研究资助局项目(C5004-23GF)
福建省科技计划项目(2023Y0040)
国家自然科学基金项目(52078448)。
关键词
桥梁工程
桥梁管养
强化学习
智能体
路网级桥梁
全寿命期
维养优化
bridge engineering
bridge management
reinforcement learning
agent
network-level bridge
life cycle
management optimization
