Bridge Monitoring System Research Articles

Study on the Effect of Temperature on the Alignment of a Long-Span Steel–Concrete Composite Beam Track Cable-Stayed Bridge

In order to study the effect of temperature on the alignment of a long-span special track bridge, this paper provides a theoretical basis and technical support for bridge design, construction and later operation. This research established the section model of the steel-hybrid beam by COMSOL, and the internal temperature field, transverse temperature, and vertical temperature gradient were analyzed. The Midas Civil bridge model analysis system was established to investigate the influence of temperature difference and temperature gradient on the vertical deformation of the whole bridge. Based on the temperature and displacement monitoring system of the Chongqing Nanjimen track bridge, the temperature and displacement data in 2023 were obtained for comparative analysis. The results show that the temperature field inside the composite beam presents a nonlinear distribution, the daily temperature difference can reach 26.0 °C, and there is a significant temperature gradient between the steel beam and the concrete. The highest temperature is 60.3 °C at 15:00 when the temperature difference between the upper and lower edges of the concrete slab is 11.1 °C, and the daily transverse temperature gradient is 3.2 °C, 5.3 °C and 7.4 °C, respectively. Under the temperature difference in the system, the maximum displacement of the main beam is 92.3 mm, and the mid-span displacement is 132.1 mm under the positive temperature difference. Based on the measured data for the whole year, it is found that the displacement of the main beam under the combined action of ambient temperature and solar radiation significantly exceeds the influence of a single temperature. The research shows that temperature change has an important impact on the stability and durability of the bridge, and temperature monitoring and management should be strengthened in the design and operation stage to ensure the bridge’s safety and smooth operation of the train.

Explainable machine learning model for load-deformation correlation in long-span suspension bridges using XGBoost-SHAP

The deformation of long-span suspension bridges in multiple loads is an important indictor to reflect their operation state. However, the correlation between multiple loads and structural deformation is difficult to quantify. Therefore, this study proposes an explainable machine learning model for the load-deformation correlation in long-span suspension bridges using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Firstly, the structural health monitoring system for a suspension bridge was used to construct the dataset for the training and testing of XGBoost model. Herein, temperature, wind and vehicle loads were used as the input variables, while midspan deflections and expansion joint displacements were treated as outputs. Subsequently, the hyperparameters of XGBoost model were optimized using grid search and 5-fold cross-validation to ensure its prediction performance. Then, the prediction results were compared with other four machine learning methods (i.e., linear regression, artificial neural networks, gradient boosted decision trees and CatBoost). Finally, the correlation between different loads and displacement responses were explained by the SHAP method to identify the contribution of the loads on deformation. The results show that the XGBoost model has the highest prediction accuracy. Compared to vehicle and wind loads, temperature significantly affects the deformation of long-span suspension bridges during daily operation. The effects of temperature and wind on bridge deformation are independent, and there is no significant interaction between these two factors.

Framework for Identification of Bridges in Need of Monitoring Based on Maintenance History

Recently, advanced Internet-of-Things (IoT) technology has been implemented in structural health monitoring to help increase safety of infrastructure and the reliability of maintenance decisions. In this context, preliminary studies are required to identify infrastructure in need of monitoring. This study establishes a basis for the identification of bridges that require monitoring and selects the bridges for measurement in order to build an efficient IoT-based monitoring system for small and medium bridges. The bridges selected based on various maintenance histories and risk factors are expected to be utilized in comprehensively assessing the characteristics of the overall bridge network.

Over 25-year monitoring of the Tsing Ma suspension bridge in Hong Kong

AbstractBridges in service are subjected to environmental and load actions, but their status and conditions are typically unknown. Health monitoring systems have been installed on long-span bridges to monitor their loads and the associated responses in real time. Since 1997, the Tsing Ma suspension bridge in Hong Kong has been the world’s first of the type equipped with a long-term health monitoring system. For the first time, this study reports the first-hand field monitoring data of the bridge from 1997 to 2022. The 26-year data provide an invaluable and rare opportunity to examine the long-term characteristics of the loads, bridge responses, and their relationships, thereby enabling the assessment of the bridge’s load evolution and structural condition over time. Results show that traffic loads have remained stable after 2007, highway vehicles kept increasing until the COVID-19 pandemic in 2020, the annual maximum deck temperature continued to increase at a rate of 0.51 °C/decade, typhoon durations increased by 2.5 h/year, and monsoon speeds decreased and became dispersed and variable. For the bridge responses, deck displacement is governed by the varying temperature. Natural frequencies in the past 26 years were almost unchanged. The overall condition of the bridge is very satisfactory. Current status and recent update of the health monitoring system are also reported. Lastly, prospects of bridge health monitoring are discussed. This study is the first to report the over one-quarter century status of a structural health monitoring system and the behavior of a long-span suspension bridge. This research provides a benchmark for many other bridge monitoring systems worldwide.

Application of digital transformation in real-time bridge monitoring systems: a case study with GNSS, accelerometer and IoT solution

Abstract Digital transformation is a term that has recently become more popular in all industries such as economics, financial fields, and engineering. The development of the Internet of Things, smart sensor systems, and cloud computing is the basis for forming effective solutions in construction monitoring from cyclic monitoring and post-processing to real-time monitoring and processing; from physical storage systems to virtual systems, and connecting every component in the project. This article presents a study and application of advanced devices including GNSS, acceleration sensors and IoT solutions in bridge construction monitoring. The research aims to apply smart devices and IoT to build real-time monitoring modules for bridge projects following the digital transformation trend. The study uses a GNSS N3 Comnav receiver with a sampling interval of 1Hz, an MPU 6050 acceleration sensor with a sampling frequency of 250 Hz, and an ESP Wi-Fi module. The result of the research is a real-time wireless monitoring system that operates effectively and economically and initially processes data with GNSS and high-frequency acceleration sensors.

Data Processing of Bridge IoT Monitoring Based on Task Scheduling of Cloud Service Listening Signal

For the long-term continuous monitoring of bridge-related indicators, it is necessary to arrange relatively perfect acquisition equipment on the bridge, which can feedback various information parameters of the bridge. However, there are many parameters to feedback the bridge information, which leads to the complex and overstaffed structure of the monitoring system. Furthermore, the huge amount of data collected and the complex calculation process also increase the difficulty of the operation of the monitoring system. In this regard, we should choose more scientific and reasonable indicators, lightweight data structure, stable data transmission, and analysis programs to improve the accuracy of continuous monitoring. To establish a stable and efficient bridge monitoring system, we use the distance coefficient-effective independent algorithm to optimize. Then, we calculate the relevant information of the strain environment with the help of a neural network model, strengthen the training of deep learning through the YOLOv5s model, and improve the task scheduling strategy of attention concentration. Through that, we solve the problem of embedded systems with relatively low computing power. Different weights are assigned to each fused feature map, and the nodes at the highest level and the lowest level are deleted so that a concise and efficient lightweight network model is constructed. Multiple iterations are performed to achieve deeper feature fusion. Therefore, the complexity of the model is effectively reduced, and the monitoring performance can be effectively improved. Finally, through the experimental analysis, it is proved that compared with the traditional fusion model, the number of parameters of the improved fusion network structure in bridge health monitoring is reduced by 7.37%. The detection speed is increased by 18.2%. The amount of computation is reduced by 42.92%, and the average detection accuracy is required to reach 95.33%. It is verified that the proposed method can effectively improve the accuracy and risk control ability of the detection data by learning from the samples with small labels. It also has great practical significance and market value for the design and optimization of the bridge health monitoring system, which is suitable for the monitoring data of large-scale construction projects.

Whole-process analysis and implementation of a self-powered wireless health monitoring system for railway bridges: Theory, simulation and experiment

In this work, a self-powered wireless health monitoring system (SP-WHMS) combined with the Internet-of-Things (IoT) is proposed and implemented for a long-term and long-distance health monitoring of railway bridges. This system frees the wireless sensor nodes (WSNs) from their dependence on chemical batteries by using the piezoelectric energy harvester (PEH) to harvest the vibration energy from bridges to power the WSNs. The whole-process of the SP-WHMS including energy conversion, energy storage, energy management and application is analyzed at the first time through theory, simulation and experiment. For the energy conversion, a reported PEH model with high energy conversion efficiency (called D-M PEH) is refabricated, which can achieve a maximum average output power of 0.9 W under unit harmonic acceleration excitation. For the energy storage, a high-precision iteration analysis procedure (IAP) is proposed to predict the charging process of the strong-coupled PEHs and the charging process under non-harmonic excitations. The comparison results show that the IAP’s prediction deviates less than 3% from the experiment result and even less than 1% from the simulation result. For the energy management, an AP64500 chip with two adjustable input voltage thresholds and a stable output voltage ensures the normal operation of the SP-WHMS and makes the SP-WHMS suitable for different types of WSNs. For the application, the synergies between energy conversion, storage and management are considered, and the effectiveness of the SP-WHMS used on railway bridges is tested through an activation experiment. This work provides a technical guidance and framework for the implementation of the self-powered wireless monitoring on railway bridges.

Bayesian model-based damage detection in engineering systems using Invertible Neural Networks

This study explores the application of BayesFlow, a state-of-the-art deep learning technique for approximate Bayesian inference, in the context of Structural Health Monitoring (SHM), and specifically, model-based damage detection. BayesFlow diverges from other likelihood-free methods by not relying on predefined summary statistics, but learning optimal ones from the simulations via invertible neural networks. It is also capable of performing almost instantaneous inference for an arbitrary number of datasets, which is ideal for real-time applications such as bridge monitoring systems. The effectiveness of the proposed approach is demonstrated with a small scale bridge use case with synthetic measurements for different testing scenarios. Across all sub-cases, BayesFlow is able to determine both the damage location and severity with a high accuracy, while the credible intervals are close to those obtained under an exact Bayesian inference approach with the true likelihood.

IoT Based Monitoring and Maintenance of Highway Bridges using WSN

This paper presents a WSN-based IOT-based bridge monitoring system. The automated real-time bridge health monitoring system has been made possible by sophisticated modifications to sensor technology; this system will aid in disaster management. The technology used in this development is a wireless sensor network (WSN). This system uses a variety of sensors to continually monitor the state of the bridge. These sensors include an accelerometer to identify jerks in the bridge, a vibration sensor to identify vibrations occurring on the bridge, a flex sensor to identify bends in the bridge, and a water level sensor to identify the water level. To determine the weight on the bridge, use the load sensor. The barrier is detected by an IR sensor. Only the buzzer will activate when these are high. Additionally, a microcontroller processes the data from many sensors and transmits it to a server and management system so that real-time bridge condition monitoring via a mobile device using the GSM model is possible

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

Real-Time Deformation Measurement of Long-Span Bridge using Multi-Inertial Camera System

Abstract. Monitoring the deformation of long-span bridges is essential for assessing structural health and safety. The existing single-camera deformation measurement method is unable to meet the high-precision measurement requirement for the deformation of large-span bridges. This paper proposes a real-time deformation measurement method for the long-span bridge using multi-inertial camera system. The method is based on visual measurement which the target deformation is measured by multiple cameras. Meanwhile, inertial measurement is used to estimate the relative pose of the camera to compensate for camera motion errors. It is applied to the health monitoring system of a long-span bridge. The experimental results show that the method proposed in this paper is highly consistent with the hydrostatic leveling measurement, with a RMSE of 4.83mm. The method can accurately and real-time measure the deformation of large-span bridges with a promising engineering value.

Optimization Of Structural Health Monitoring For Steel Bridges Using Wireless Sensor Networks and Machine Learning Algorithms

This study presents an advanced structural health monitoring (SHM) system for steel bridges based on wireless sensor networks (WSN) integrated with machine learning algorithms. The proposed system monitors and predicts structural integrity under various load conditions. The research focuses on developing a machine learning model capable of real-time anomaly detection, allowing for early warnings of potential failures. Experimental results from both simulation and field tests demonstrate the system’s effectiveness in prolonging bridge lifespan while reducing maintenance costs.

Self-powered wireless environmental monitoring system for in-service bridges by galloping piezoelectric-triboelectric hybridized energy harvester

Self-powered wireless environmental monitoring system for in-service bridges by galloping piezoelectric-triboelectric hybridized energy harvester

Computer Vision-Based Real-Time Identification of Vehicle Loads for Structural Health Monitoring of Bridges

Coupling effects of various loading conditions can cause deflections, settlements and even failure of in-service bridges. Although it is one of the most critical loads, unfortunately, loading conditions of moving vehicles are difficult to capture in real time by bridge monitoring systems currently in place for sustainable operation. To fully understand the status of a bridge, it is essential to obtain instantaneous vehicle load distributions in a dynamic traffic environment. Although there are some methods that can identify overweight vehicles, the captured vehicle-related information is scattered and incomplete and thus cannot support effective bridge structural health monitoring (BSHM). This study proposes a noncontact, vision-based approach to identification of vehicle loads for real-time monitoring of bridge structural health. The proposed method consists of four major steps: (1) establish a dual-object detection model for vehicles using YOLOv7, (2) develop a hybrid coordinate transformation model on a bridge desk, (3) develop a multiobject tracking model for real-time trajectory monitoring of moving vehicles, and (4) establish a decision-level fusion model for fusing data on vehicle loads and positions. The proposed method effectively visualizes the 3D spatiotemporal vehicular-load distribution with low delay at a speed of over 30FPS. The results show that the hybrid coordinate transformation ensures that the vehicle position error is within 1 m, a 5-fold reduction compared with the traditional method. Wheelbase is calculated through dual-object detection and transformation and is as the primary reference for vehicle position correction. The trajectory and real-time speed of vehicles are preserved, and the smoothed speed error is under 5.7%, compared with the speed measured by sensors. The authors envision that the proposed method could constitute a new approach for conducting real-time SHM of in-service bridges.

An IoT-Based Road Bridge Health Monitoring and Warning System.

Recent earthquakes worldwide have led to significant loss of life and structural damage to infrastructure, especially road bridges. Existing bridge monitoring systems have limitations, including restricted detection capabilities, subjectivity, human error, labor-intensive inspections, limited access to remote areas, and high costs. Aging infrastructures pose a critical concern for organizations and government funding policies, showing signs of decay and impending structural failure. To address these challenges, this research proposes an IoT-based bridge health status monitoring and warning system that is wireless, low-cost, durable, and user-friendly. The proposed system builds upon engineering standards and guidelines to classify bridge health status into categories ranging from excellent to collapse condition. It incorporates deflection, vibration, temperature, humidity, and infrared sensors, combined with IoT and a fuzzy logic algorithm. The primary objective is to reduce bridge maintenance costs, extend lifespans, and enhance transportation safety through an early warning system via a mobile application. Additionally, a Google Maps interface has been developed to display bridge conditions along with real-time traffic video. To validate the proposed system, a 3-D prototype model was constructed and tested. Practical testing of the fuzzy logic algorithm aligned with the simulation outcomes, demonstrating expected accuracy in determining bridge health status.

Virtual Sensing in Steel Bridges: Time Series Deep Learning for Stress Prediction

This study introduces an innovative approach for predicting stress responses in steel bridges, specifically focusing on a railway bridge in Vänersborg, Sweden. Four deep learning models have been evaluated: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and a hybrid LSTM-TCN. Training on stress history data from a multiscale Finite Element (FE) model and a validation with real-world data from a bridge monitoring system revealed high prediction accuracy near sensor locations, surpassing an R-squared score of 0.9, comparable to the polynomial local response function method.The comparative analysis provides critical insights into the great potential of deep learning-based sequence models for identifying intricate, temporally dependent stress patterns across the bridge, including predictions at points distant from direct sensor measurements. These models demonstrate a notable capability for capturing highly non-linear relationships between stress histories. While sequence models (LSTM, TCN, and hybrid LSTM-TCN) tended to provide conservative estimates impacting fatigue life predictions, the MLP model occasionally underestimated critical stress cycles.This research emphasizes the potential of deep learning techniques for time series to enhance bridge monitoring systems, improve virtual sensing, and enable real-time monitoring capabilities. Our proposed methodology provides a comprehensive understanding of stress data in steel bridges, which is crucial for ensuring their maintenance and safety.

Effective structural health monitoring (SHM) system for bridges: a case study

In the last few years, an increasing amount of resources have been invested in the maintenance of existing infrastructure systems in Italy because of the recent catastrophic collapses of structures. In fact, it is now clear that bridges require proper management by the administering entities, which foresee a detailed control of the health condition of the structure over time, as well as the execution of the required works for good maintenance.Therefore, the assessment of the structural “health” state of such systems has a crucial role. The monitoring of vibration modes has proved to be an excellent tool for SHM systems since they are related to intrinsic properties of the structure (i.e., mass, stiffness, and damping) and, therefore, to possible damage on the structure.In a much broader perspective, not only the monitoring of "old" infrastructures is considered but also of new construction so as to have data traceable to healthy structures that serve as a basis for comparison in the examination of the development of damage.Therefore, in the present work the layout of the SHM system with the innovative sensors of a recently built steel bridge was designed. A preliminary numerical model based on the information from the design documents has been developed and used for the application of OSP (optimal sensor placement) techniques to identify the optimal positions of the sensors to be used for a future SHM system.

Structural monitoring of a historic masonry bridge

Structural Health Monitoring (SHM) aims to develop autonomous integrated systems allowing inspection and detection of damage with minimal human intervention. It thus represents a process of implementing a damage identification strategy through which, by periodic observation of the structure, it is possible to arrive at an assessment of certain characteristics of the system, as well as to define its current state of health. The case study under consideration describes a new static and dynamic monitoring system of a five-arch masonry railway bridge, realized by a network of sensors installed according to a placement strategy. The general objective of monitoring is the acquisition of all the information concerning the current condition of the bridge and thus the structural behaviour under static and dynamic actions to ensure an adequate level of public safety. Any change in the dynamic characteristics over time is indeed an indicator of ongoing degradation in the structure, due for instance to material damage phenomena, or geometrical. The collected data are visualized in real time with the help of the Web Platform where various operating parameters including sampling rates, resolution, trigger thresholds, alarm thresholds, etc. can be set. The data are finally analysed following a scheduled plan or upon reaching pre-established alarm thresholds.

Long-term indirect monitoring system for short and medium span bridges

Bridge health monitoring has been widely discussed for establishing the rational maintenance strategies of existing bridges. Meanwhile, we need to provide discussions and recommendations on how to establish fully automatic data collection system and how to manage the big data for holistic bridge maintenance decisions. The author developed a vehicle-based (coupling of a moving vehicle and a bridge) bridge health monitoring system by using public buses which is one practical solution to the problem for condition assessment of short and medium span bridges (called “bus-based monitoring system”). The proposed system includes some new ideas about making use of the under rear-wheel spring acceleration sensors installed on an in-service fixed-route bus operating on the public transit system (long-term and indirect methods), define “characteristic deflection” as an indicator that may be useful in efficiently detecting a structural anomaly of a bridge, etc. The main objectives of this paper are 1) to introduce the details of how to assess the bridge condition based on the results of long-term application (total more than 10 years) to actual bridges on Ube City bus network in Japan, as a specific example for verify the system, 2) to discuss what is possible steps in realizing practical application of the system to existing bridges for consideration systematically into the maintenance strategies in the future. This paper is a continuation of previously published ones with new materials from the practical application viewpoint.

Acoustic Event-Based Prestressing Concrete Wire Breakage Detection

The integrity of concrete bridges, essential for public safety and infrastructure longevity, can be risked by the breakage of prestressed wires, potentially leading to catastrophic failures. In response to this challenge, this study introduces a novel approach to detect prestressed wire breakage by employing dynamic signal representations: the Short-Time Fourier Transform (STFT, a technique for time-frequency analysis) and Mel-frequency cepstrum coefficients (MFCCs, capturing the timbral aspects of sounds). Acoustic emission signals from two Italian bridges were collected and processed to extract relevant features using STFT and MFCCs. The study employs a multilayer perceptron (MLP) classifier enhanced with the MixUp data augmentation technique—a method that blends samples to enhance training data diversity and volume—addressing the challenge of limited data and improving model robustness. The promising results achieved by the MLP classifier in detecting prestressed wire breakages underscore its efficacy. These results highlight the method’s potential, specifically using MFCC, for integration into real-time bridge monitoring systems, offering an efficient solution for enhancing infrastructure safety.