Abstract: To address the challenges associated with the operation and maintenance of the Jiluo Road Yellow River Tunnel, including complex maintenance objects, heterogeneous data sources, enclosed high-humidity and high-pressure conditions, and stringent requirements for rapid emergency response, an intelligent operation and maintenance management platform integrating sensing, analysis, prediction, decision-making, coordination, and feedback was developed. An overall architecture comprising a perception layer, a data layer, a model layer, and an application layer was adopted, and a unified identification and information mapping mechanism for operation and maintenance objects was established, thereby enabling standardized governance and deep integration of structural, equipment, environmental, traffic, and maintenance management data. Multi-source sensing, machine learning, rule engines, and lightweight BIM technologies were integrated, and key functions such as traffic state prediction, equipment fault diagnosis, environmental risk early warning, three-dimensional visualization, and coordinated emergency response were supported. The results indicated that operation and maintenance efficiency, predictive warning capability, and emergency response performance in underwater tunnels were effectively improved, and an engineering reference for the intelligent operation and maintenance of urban underwater tunnels was provided.
丁宁, 王肖建, 毛升, 孔维信, 龚晨阳. 济泺路穿黄北延隧道智能运维管理平台研发及应用[J]. 隧道与地下工程灾害防治, .
Ding Ning, Wang Xiaojian, Mao Sheng, Kong Weixin, Gong Chenyang. Development and application of an intelligent operation and maintenance management platform for the Jiluo Road North Extension Yellow River Tunnel. Hazard Control in Tunnelling and Underground Engineering, 0, (): 1-14.
