Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, the accelerometers stream raw vibration data, which are processed in the frequency domain and analyzed using machine learning to detect anomalies that indicate potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge for which vibration data were collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks that indicate potential changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warnings of bridge damage and also supports the use of in-house-designed sensors with lower cost and edge computing capabilities such as those used in the demonstration. The successful on-premises–cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers

This paper presents a study about the integration of Piezoelectric Energy Harvesting Systems (PE-EHS) in building envelopes for powering Fiber Bragg Grating (FBG) sensors, enabling efficient and low-consumption monitoring with the objective to leverage Structural Health Monitoring (SHM). The research includes preliminary tests conducted in a real environment to validate the PE-EHS when fully integrated into the façade, capturing mechanical vibrations generated mainly by wind loads. Based on these activities, the final configuration of PE-EHS is defined to provide a complete system for façade monitoring. This integrated system includes the Piezoelectric Generator (PEG), Supercapacitor (SC), Power Conditioner Circuit (PCC), Fibre Optic Sensing (FOS) Interrogator, and the IoT Gateway trans-mitting measurement data within an Internet of Things (IoT) monitoring platform. This configuration is tailored to address the challenges related to the structural integrity of building envelopes. Results demonstrate a potential for a stand-alone solution in the façade sector, but, raising issues for certain limitations, requiring further investigation. In particular, the study emphasizes constraints related to the energy production of PE-EHS for façade integration. It highlights the necessity to carefully consider these limitations within the broader context of their applicability, providing insights for the informed deployment of Piezoelectric Energy Harvesting technology in building envelope monitoring. 

Integration of Piezoelectric Energy Harvesting Systems in Building Envelope for Structural Health Monitoring with Fiber Optic Sensing Technology

Secure communications in connected vehicles (CV) is essential to ensure the safety of drivers, passengers and pedestrians. As vehicles are becoming more connected and autonomous, they are reliant on communication and data exchange with infrastructure and other vehicles. Although Public Key Infrastructure has the potential to offer secure communication, it does not have ground truth information of vehicle location. Reputation-based communication can provide a more reliable approach to securing communication in a dynamic and constantly changing environment. This paper proposes a neuro-inspired reinforcement learning (RL) approach for reward estimation in CV networks. Vehicles estimate the reputation of neighboring vehicles by comparing broadcasted kinematic data with onboard sensor estimates along with connectivity topology inspired from brain, thereby forming a local graphical representation with reputation distribution. This information is shared with a centralized RL agent, which provides reward signals to each vehicle from combined reputation scores to incentivize accurate reputation estimates.

Leveraging Neuro-Inspired Reinforcement Learning for Secure Reputation-based Communication in Connected Vehicles

Throughout their lifetimes, bridges are exposed to various forces that may lead to displacement and deformation, potentially compromising their structural integrity. Monitoring their structural integrity under environmental factors is essential for safety and maintenance under these conditions. This aspect is a fundamental component of Structural Health Monitoring (SHM). Many studies focus on Structural Health Monitoring (SHM), employing various theories, methodologies, and technologies that have advanced rapidly due to the expansion of information technology. The objective of this study is to pinpoint areas where research is lacking in the existing literature on the environmental factors that impact the displacement of bridges, along with the techniques and technology used to monitor these structures. To achieve this objective, the most critical environmental factors and technologies, particularly those that are sensor-based, have been identified through a systematic search of the most popular databases. Subsequently, the study utilized a bibliometric analysis, exploring the challenge and prospective research areas reflected in the specialized literature. The findings indicate a lack of scholarly investigation of environmental factors that influence the Structural Health Monitoring (SHM) of bridges, in particular studies regarding the effect of uneven sunlight on structures. The research provides a comprehensive understanding of the Structural Health Monitoring (SHM) of bridges and has practical implications for developing effective monitoring methodologies. 

Structural Health Monitoring of Bridges under the Influence of Natural Environmental Factors and Geomatic Technologies: A Literature Review and Bibliometric Analysis

Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally.

Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

The fast-evolving Intelligent Transportation Systems (ITS) are crucial in the 21st century, promising answers to congestion and accidents that bother people worldwide. ITS applications such as Connected and Autonomous Vehicle (CAVs) update and broadcasts road incident event messages, and this requires significant data to be transmitted between vehicles for a decision to be made in real-time. However, broadcasting trusted incident messages such as accident alerts between vehicles pose a challenge for CAVs. Most of the existing-trust solutions are based on the vehicle's direct interaction base reputation and the psychological approaches to evaluate the trustworthiness of the received messages. This paper provides a scheme for improving trust in the received incident alert messages for real-time decision-making to detect malicious alerts between CAVs using direct and indirect interactions. This paper applies artificial intelligence and statistical data classification for decision-making on the received messages. The model is trained based on the US Department of Technology Safety Pilot Deployment Model (SPMD). An Autonomous Decision-making Trust Scheme (ADmTS) that incorporates a machine learning algorithm and a local trust manager for decision-making has been developed. The experiment showed that the trained model could make correct predictions such as 98% and 0.55% standard deviation accuracy in predicting false alerts on the 25% malicious data

A Context-Based Decision-Making Trust Scheme for Malicious Detection in Connected and Autonomous Vehicles

The continual increase in the number of vehicles on the roads in the UK has impacted the journey travel time and often causes severe congestion. Connected vehicles can reduce these challenges while increasing traffic safety, by sharing information about problems on the roads. This paper proposes an incident-based vehicle rerouting approach for efficient traffic management. Contrary to other methods where vehicles take possible alternative routes on receiving an incident alert, the system proposed applies the average incident clearance time on the route to compute and update route travel times based on the real-time traffic information. It compares the updated travel times with other available routes before making any rerouting decisions. Datasets from Highways England and the Office of National Statistics were used to develop a central template for classes of incidents and clearance time and integrated into the application. The solution was implemented in Objective Modular Network Testbed in C++ with a Realistic Vehicular Simulator and Sub-Urban Mobility. The solution reduced the congestion and improved travel times by 17.86% on average. Overall, vehicles with positive, negative, and no impact on travel were 48.30%, 30.30%, and 21%, respectively. This approach also reduced traffic emissions, and other researchers can apply the central incident clearance time template to improve traffic management.

An Event-based Intelligent Vehicle Rerouting for Efficient Traffic Management for Connected Vehicles

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Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

A Context-Based Decision-Making Trust Scheme for Malicious Detection in Connected and Autonomous Vehicles

An Event-based Intelligent Vehicle Rerouting for Efficient Traffic Management for Connected Vehicles