The monitoring of highway infrastructures through internet of things sensors allows the storage of high volumes of data. These data can be exploited through artificial intelligence techniques to extract useful information on the assets condition. Operational Modal Analysis (OMA) employing vibration-based techniques stands out among Structural Health Monitoring (SHM) systems, garnering popularity owing to its nondestructive approach, comprehensive damage assessment capacity, and relatively straightforward automation with minimal intrusion. There is a broad literature about structural health monitoring applied to bridges, but its integration within a traffic management system has not received the deserved attention yet. To achieve high performance in terms of network reliability and level of service, the infrastructure manager must make decisions about the traffic flow, considering the percentage of heavy vehicles circulating in the network. The paper presents a decision support model to suggest the best mitigation strategy according to the status of the bridges present in the network and the traffic demand. More in detail, bridge conditions are assessed through a digital twin-oriented platform, the CEMBOX platform, which conducts real-time structural evaluations. According to the degradation level of the bridges, traffic limitation strategies are defined and compared, knowing the transport demand and the traffic composition. The considered mitigation strategies consist of the rerouting of heavy or/and light vehicles, the closure of one or more lanes or the closure of the entire carriageway. The application of the proposed approach to a real-world case study is presented, showing the usefulness of the proposed approach in dealing with bridge and traffic management. The results show that this innovative platform, by providing continuous assessments of bridge integrity, enhances the accuracy of structural health monitoring and the efficiency of traffic management decision support.
On supporting traffic management decisions according to bridges structural health monitoring within a digital twin-oriented platform
Abbasi M.;Consilvio A.;
2025-01-01
Abstract
The monitoring of highway infrastructures through internet of things sensors allows the storage of high volumes of data. These data can be exploited through artificial intelligence techniques to extract useful information on the assets condition. Operational Modal Analysis (OMA) employing vibration-based techniques stands out among Structural Health Monitoring (SHM) systems, garnering popularity owing to its nondestructive approach, comprehensive damage assessment capacity, and relatively straightforward automation with minimal intrusion. There is a broad literature about structural health monitoring applied to bridges, but its integration within a traffic management system has not received the deserved attention yet. To achieve high performance in terms of network reliability and level of service, the infrastructure manager must make decisions about the traffic flow, considering the percentage of heavy vehicles circulating in the network. The paper presents a decision support model to suggest the best mitigation strategy according to the status of the bridges present in the network and the traffic demand. More in detail, bridge conditions are assessed through a digital twin-oriented platform, the CEMBOX platform, which conducts real-time structural evaluations. According to the degradation level of the bridges, traffic limitation strategies are defined and compared, knowing the transport demand and the traffic composition. The considered mitigation strategies consist of the rerouting of heavy or/and light vehicles, the closure of one or more lanes or the closure of the entire carriageway. The application of the proposed approach to a real-world case study is presented, showing the usefulness of the proposed approach in dealing with bridge and traffic management. The results show that this innovative platform, by providing continuous assessments of bridge integrity, enhances the accuracy of structural health monitoring and the efficiency of traffic management decision support.
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