An intelligent monitoring system for roads and bridges based on Internet of Things technology that provides an intelligent and scalable technical solution for the full life-cycle management of roads and bridges, applicable to the regular monitoring and emergency response of large-scale transportation infrastructure such as expressway bridges, urban overpasses, and long tunnels.
Abstract
With the continuous improvement of the networked layout of transportation infrastructure, the structural safety and operation efficiency of roads and bridges, as the core hubs, have become key factors affecting the sustainable development of transportation. Traditional monitoring methods have limitations such as weak real-time performance, insufficient data coverage, and delayed anomaly warnings, which render them unable to meet the requirements of precise management under complex conditions. Therefore, this paper designs an intelligent monitoring system for roads and bridges based on Internet of Things technology. This system integrates a multi-source sensor fusion architecture, edge-cloud collaborative computing, adaptive data processing algorithms, and an improved attention-mechanism Long Short-Term Memory (LSTM) anomaly warning model, achieving full-dimensional, high-precision, and real-time monitoring of bridge structural strain, vibration, settlement, and environmental parameters. The system constructs a five-level architecture comprising perception, transmission, processing, warning, and application, and introduces a sensor node dynamic deployment model, a multi-modal data weighted fusion algorithm, and a load-adaptive scheduling mechanism. This approach effectively solves problems such as heterogeneous data transmission conflicts, edge-node computing power bottlenecks, and low accuracy of anomaly identification under complex conditions. Experimental results show that the average error of sensor data collection is controlled within 0.32%, the data transmission delay is as low as 18.7 ms, and the anomaly warning accuracy reaches 97.6%, which is 15.3%, 42.6%, and 21.8% higher than those of traditional monitoring systems, respectively. In actual bridge operation scenarios, the system can effectively identify potential risks such as crack expansion and structural settlement, shorten the fault response time to within 3 min, and maintain a stable operation rate of 99.2% even under extreme weather conditions, such as heavy rain and strong winds, as well as under high-traffic conditions. This system provides an intelligent and scalable technical solution for the full life-cycle management of roads and bridges, applicable to the regular monitoring and emergency response of large-scale transportation infrastructure such as expressway bridges, urban overpasses, and long tunnels.
With the development of smart cities, the intelligentization of transportation infrastructure such as highways and highspeed railways connecting smart cities has become an inevitable requirement. However, influenced by factors such as geological conditions, precipitation, and construction techniques, slope collapses and landslides pose significant risks to urban and transportation infrastructure safety. To effectively prevent these risks from escalating into disasters that cause severe losses to people's lives and property, traditional measurement methods struggle to meet the operational requirements of infrastructure for "real-time perception, intelligent analysis, and rapid assessment." Based on new technologies like 5G, the Internet of Things, and measurement robots, intelligent monitoring technology has emerged as a novel solution for safety risk prevention. This technology integrates functions such as data collection, data transmission, data calculation and analysis, assessment, and forecasting, enabling intelligent prevention of risks throughout the entire lifecycle of slopes. Grounded in the context of smart city development, this study explores and practices intelligent monitoring technology for transportation infrastructure slopes, providing theoretical support and practical experience for its large-scale application.
Lijing Cao, Siyuan Yao· International Conference on...· 0 citations
Manual monitoring has inherent shortcomings, including low efficiency, inability to achieve real-time data acquisition, and relatively high error rates that prevent 24-hour uninterrupted observation. To solve the above problems, this paper constructs a four-layer intelligent monitoring system on the basis of Internet of Things (IoT), which includes perception layer, transmission layer, processing layer and application layer. The system is designed in accordance with internationally recognized IoT architectural standards, which forms a closed-loop process covering signal acquisition, data transmission, and data analysis. This paper details the key functions, composition, technical principles, and coordination mechanisms of each layer. This architecture integrates edge computing and cloud computing, builds a multi-protocol transmission mechanism, and addresses the limitations of traditional monitoring systems, which rely on overly simplistic data processing models and suffer from prolonged response times. Practical application demonstrates that the four-layer framework offers robust real-time responsiveness and scalability, and can greatly improve monitoring efficiency, demonstrating the practical value of the proposed system.
Mayifei Wu· Applied and Computational En...· 0 citations
The proposed framework significantly improves response speed, monitoring accuracy, and early-warning capability while reducing network dependence and deployment complexity and provides an effective engineering solution for distributed sensing, intelligent monitoring, and real-time information processing in large-scale industrial environments.
The increasing integration of IoT sensors, fiber-optic sensing and wireless communication links into urban architecture has made smart city infrastructure more vulnerable to disruptions as these complex, dynamic operating environments grow increasingly diverse with connectivity. Traditional static management frameworks cannot solve the problem of real-time perception and comprehensive planning of cross-system hazards. This paper proposes a dynamic monitoring and emergency dispatch model based on high-precision spatio-temporal big data. A data twin view of an integrated system based on heterogeneous data in the form of IoT sensing, remote sensing, GIS, and social sensing is established with a common spatiotemporal reference point. Accordingly, multi-layer complex networks are used to describe infrastructure dependencies, and spatiotemporal graph convolutional networks are integrated to obtain dynamic risk propagation simulations. In addition, a better non-dominated sorting genetic algorithm is adopted for multi-objective adaptive emergency resource scheduling. The results show that the model’s anomaly identification accuracy is 95.3, risk propagation prediction accuracy is 88.7, and only the time to generate the schedule is 4.2 seconds. The proposed data fusion and scheduling logic is consistent with smart-city systems that rely on wireless sensing, remote sensing and communication infrastructure.
The rapid expansion of offshore wind farms has introduced significant challenges to operation and maintenance (O&M), particularly under harsh marine environments where reliable electromagnetic information transmission and constrained wireless communication resources directly affect intelligent monitoring performance. Traditional Supervisory Control and Data Acquisition (SCADA) systems relying on cloud-centric architectures often encounter excessive latency and bandwidth bottlenecks when transmitting high-frequency vibration signals, limiting real-time fault diagnosis. To address these issues, this study proposes an Internet of Things (IoT)-based intelligent monitoring and fault detection method built upon an Edge-Cloud collaborative architecture. A lightweight Adaptive One-Dimensional Convolutional Neural Network (A-1D-CNN) is developed for deployment on edge gateway devices, enabling direct extraction of fault characteristics from raw vibration signals without manual feature engineering. Combined with an “ Edge-Training, Cloud-Update” strategy, the proposed framework continuously optimizes diagnostic performance while substantially reducing communication overhead across wireless sensing and electromagnetic transmission infrastructures. Experimental evaluation on a standard bearing fault dataset demonstrates that the proposed method achieves a fault diagnosis accuracy of 99.25% with a compact model size of only 0.45 MB, providing an effective balance between diagnostic precision and deployment efficiency. The results indicate that the proposed framework offers a practical solution for real-time intelligent monitoring in bandwidth-limited offshore environments and provides technical support for reliable electromagnetic-enabled sensing networks and distributed fault diagnosis in next-generation offshore energy systems.
Y. Ouyang, W. Liang· Advanced Electromagnetics· 0 citations