Bridge Structural Health Research Articles

Adaptive sensor placement selection and response reconstruction for bridge structural health monitoring

Abstract Bridge structural health monitoring (SHM) measures the real-time responses of bridges via instrumented sensors. This paper focuses on the bridge displacement reconstruction at unmeasured locations of interest (LoIs) using measurements from sensors. Increasing the number of sensors on the bridge can capture more structural information. However, considering the complex structure and scale of bridges, minimizing the number of sensors and selecting the critical sensing locations (CSL) to place sensors for measurements are significant in bridge SHM. To achieve efficient bridge SHM under limited number of sensors, this paper proposes an adaptive sensor selection and reconstruction neural network (ASSRNN) for determining the CSLs and reconstructing at all LoIs. In particular, a novel adaptive location selection mechanism (ALSM) as well as a unified network model and its learning process is proposed. In the experiments, the proposed model is validated based on a real-world case study of a long-span bridge in California, USA. Through the comparison of different sensor placement schemes. The experimental results show that the proposed model is effective and accurate in reconstructing the structural displacement responses via adaptive sensor selection for bridge SHM.

AI-Enhanced IoT System for Assessing Bridge Deflection in Drive-By Conditions.

The increasing traffic on roads poses a significant challenge to the structural integrity of bridges and viaducts. Indirect structural monitoring offers a cost-effective and efficient solution for monitoring multiple infrastructures. The presented work aims to explore new sensing strategies based on digital MEMS sensors integrated into an intelligent IoT infrastructure to predict the bridge deflection behaviour for indirect Bridge Structural Health Monitoring purposes. An experimental setup comprising a bridge model and vehicle equipped with a smart sensing node has been used to generate the dataset. Various models for bridge deflection estimation are deployed on the sensorized vehicle, exploiting edge AI capabilities of smart sensors. This study shows the potential of leveraging data-driven technologies to enhance the performance of low-cost sensors. Additionally, it demonstrates the viability of assessing static deflection shapes of bridges through indirect measurements on board vehicles, underlining the potential of this approach to make SHM more cost-effective and scalable.

Prognostic‐Based Active Battery Degradation Management in Wireless Sensor Networks for Group Replacement

ABSTRACTBatteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio.

Tail-End Evaluation Based on the Cost-Effectiveness of Bridge Life Cycles

The number of bridges in China is increasing year by year. The contradiction between the declining performance of bridge structures and the increasing traffic volume in China is becoming increasingly prominent, causing a concentrated upsurge in operation and maintenance needs during the service life of the bridges. In accordance with the goal and vision of the comprehensive construction of the Bridge Structural Health Monitoring (BSHM) system, the study of bridge cost-effectiveness now focuses on the installation, operation, and maintenance costs of the BSHM system, the renovation costs of bridge structure repair and maintenance, and the benefits of the BSHM system in assisting in expansion of the service life of bridges. In this study, a new cost-effectiveness framework is proposed that could be used for the operation and maintenance period. The cost-effectiveness framework is constructed from five perspectives: installation, operation, and maintenance costs of the BSHM system; repair and renovation costs during the operation and maintenance period; demolition costs; operation benefits during the operation and maintenance period; and the recovery benefits. The difference between costs and benefits is calculated to evaluate the cost-effectiveness, the service life, and the structural reliability of bridges, in order to provide auxiliary decision making for bridges. A bridge in China is used as an example; the difference between the reliability and operation of the bridge and the maintenance benefits and costs corresponding to different operating times is calculated, and maintenance decision analysis is performed. This can greatly extend the service time of the bridge without major repairs when the bridge reliability meets the requirements. Therefore, the BSHM operation and maintenance cost-effectiveness framework reduces resource waste, reduces operation and maintenance costs, and maximizes resource utilization and benefits, while ensuring the safety of the bridge structure and extending the service life of the bridge.

Anomaly detection in bridge structural health monitoring via 1D-LBP and statistical feature fusion

Over the past few years, numerous bridges have been equipped with structural health monitoring (SHM) systems to continuously monitor critical structural parameters, enabling early detection of potential issues and timely maintenance. However, the monitoring data frequently contain anomalies due to various interferences during the acquisition and transmission processes. To address this, this paper proposes a robust and efficient anomaly detection and classification method. The method extracts one-dimensional local binary pattern (1D-LBP) features and time-domain statistical features from the monitoring data, fusing them into a comprehensive feature representation. These fused features are then input into an extreme gradient boosting (XG-Boost) classifier for anomaly detection. Additionally, a 1D-LBP feature simplification method is introduced to enhance detection efficiency. The effectiveness of the proposed method was validated using monitoring data from a long-span cable-stayed bridge SHM system. Experimental results demonstrate the excellent performance of the method in terms of detection accuracy and efficiency.

A deep kernel regression-based forecasting framework for temperature-induced strain in large-span bridges

The strain data from health monitoring systems of large-span bridges is influenced by various load effects, with the extraction and forecasting of temperature-induced strain being particularly significant for precise analysis and early warning of monitoring data. This paper presents a forecasting framework for temperature-induced strain in large-span bridges, employing a deep kernel regression (DKR) approach that integrates deep learning with Bayesian regression to enhance accuracy and certainty. Initially, this paper addresses the influence of additional response increment induced by vehicle strain effects and employs a robust data smoothing algorithm to extract temperature-induced effect components from measured strain data offline. Subsequently, a DKR model is proposed, integrating a long short-term memory (LSTM) layer with a fully connected layer. The output of the deep learning module serves as the kernel function parameter for the Gaussian process regression (GPR) module, and the GPR module with updated hyperparameters is used for time series forecasting. This method effectively extracts and utilizes time series features from historical data alongside key environmental factors, enabling real-time forecasting of strain effects and significantly improving the performance and utility of health monitoring systems in large-span bridges. Compared to commonly used time series forecasting algorithms, the algorithm proposed in this paper exhibits significantly improved accuracy, stability, and certainty. Through comparing the inference time, it was verified that the algorithm can meet the performance requirements of real-time inference, which underscores the model's potential as a robust tool in bridge structural health monitoring.

Experimental and theoretical evaluation of axial forces in short steel ropes

AbstractAssessing rope force in bridge structural health monitoring, particularly for shorter lengths, poses challenges. The vibration method, commonly utilized for taut strings, yields inaccurate results for short ropes due to neglecting bending stiffness. To address this, the differential equation of lateral vibration of a prismatic beam possessing bending stiffness EI, evenly distributed mass m under the tension force N is solved approximately and numerically using FEM for greater accuracy. Nonlinear fitting via the Gauss‐Newton aids in refining results. Laboratory experiments, varying axial forces and rope characteristics, validated these methods, offering recommendations for improved accuracy.

Enhancing acceleration data cleaning through time–frequency feature integration using deep learning

In recent years, the safety issues of bridge has garnered significant attention from various sectors of society. Bridge structural health monitoring (SHM) systems have emerged as essential tools for real-time monitoring and assessing the operational safety of bridges. Despite the substantial implementation and operation of monitoring systems, challenges have arisen from abnormal data caused by equipment malfunctions, power outages, and environmental interference. These anomalies are challenging to manually remove within the vast dataset. To address the challenge of automated detection and classification of anomalies, this paper focuses on acceleration data and proposes a data cleaning method that integrates time–frequency features and one-dimensional convolutional neural network (1D CNN). The method utilizes extreme value envelopes and wavelet scattering transforms to extract time–frequency features from acceleration data, thereby achieving data dimensionality reduction and feature extraction goals. Subsequently, these features are employed to train and test a 1D CNN. The proposed method’s validation is conducted using acceleration monitoring data obtained from a large-span arch bridge. Through extensive testing, the model demonstrates a classification accuracy exceeding 98% on both the test set and unseen data, showcasing high classification precision and generalization capabilities. This approach provides an effective solution for automated data cleaning and anomaly identification within the context of bridge health monitoring systems.

Low-Cost, Low-Power Edge Computing System for Structural Health Monitoring in an IoT Framework.

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm.

Research on Bridge Structural Health Monitoring System Based on Sensor Data

Research on Bridge Structural Health Monitoring System Based on Sensor Data

A preliminary investigation of the inverse Finite Element Method applied to bridge structures

Deflection monitoring plays a key role in bridge Structural Health Monitoring, as deflection data may offer valuable insights into potential structural issues, such as cracks and corrosion. However, monitoring bridge deflections under operative loads presents challenges, particularly in scenarios where establishing a fixed reference point for displacement sensors is impractical, such as when a bridge spans a waterway or the sensors may be vulnerable to vandalism. In recent years, the inverse Finite Element Method (iFEM) has emerged as a possible solution. This method provides a load- and material-independent means of computing structural displacements from strain measurements, in real time at a minimal computational cost. Additionally, the iFEM can serve as a virtual sensing approach to monitor strains at locations lacking sensors, enabling the assessment of the fatigue life consumption at virtually any point in the structure. Despite its potential, applications of the iFEM in the civil engineering field are very limited, with no validation on full scale bridges has been performed to date. This study addresses this gap by investingating the application of the iFEM for bridge deflection monitoring through distributed strain measurements. The proposed approach is tested on experimental case studies, involving cross-validatiion of reference-free deflections inferred by the iFEM with those obtained from displacement sensors. This work contributes to the advancements of structural health monitoring methodologies in civil engineering, demonstrating the practicality and reliability of iFEM in bridge applications.

Review and Prospect of Research on Structural Health Monitoring Technology for Bridges

As a crucial infrastructure in the transport system, the safe operation of bridges is directly related to all aspects of people’s daily lives. The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges. This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring. Firstly, the existing monitoring technologies at home and abroad are sorted out, and the advantages and problems of various methods are compared and analyzed, including nondestructive testing, stress measurement, vibration characteristic identification, and other commonly used monitoring technologies. Secondly, the key technologies and equipment in the bridge health monitoring system, such as sensor technology, data acquisition, and processing technology, are introduced in detail. Finally, the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application. In the future, with the development of emerging technologies such as big data, cloud computing, and the Internet of Things, it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.

Applying matrix regularization to identification of traffic-induced equivalent loads and multi-vehicle weight on beam-like bridge

Identification of traffic-induced forces is an important topic in the field of bridge structural health monitoring (B-SHM). It can provide reliable data to support the great demand for monitoring and evaluating the working conditions of bridges. Bridges may serve under consistent moving vehicles and this requires that the methods of moving force identification (MFI) should be applicable under circumstances such as multiple-vehicle loading and long-term monitoring. Centering on cases containing multiple vehicles, this study proposed a matrix regularization-based method for the identification of traffic-induced equivalent loads and multi-vehicle weights. Herein, multiple moving forces in the spatial domain are converted into a certain number of equivalent loads which virtually act at fixed locations on the bridge as a kind of coordinate transformation. The MFI problem then can be transformed into tasks of identifying some concentrated forces. To solve the problem of equivalent load identification, the structural input-output relationship is firstly reorganized in a matrix-matrix-matrix form with the help of segmentation via time windows and consideration of unknown structural initial conditions. Then the matrix regularization with a sparse penalty term is introduced to formulate an identified equation so that, it can ensure the strong noise robustness of the identified results. Finally, the total weight of multiple vehicles can be empirically estimated from the sum of the identified equivalent loads. Numerical simulations and model experiments are performed to verify the proposed method's performance. The effects of vehicle speeds, measuring noise, number of modal orders, number of vehicles, and vehicle spacings are investigated. The illustrated results show that the proposed method turns out to be effective and precise in identifying the traffic-induced equivalent loads and the weight of multiple vehicles.

Refinement and Validation of the Minimal Information Data-Modelling (MID) Method for Bridge Management.

Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge's natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure's temperature changes. Data models can be used to overcome this problem by predicting normal changes to a structure's natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management.

Extraction and evaluation of cable forces of a cable-stayed bridge based on amplitude and phase estimation method

In order to identify the time-varying frequency and amplitude of structural vibration based on the bridge structural health monitoring data and obtain the cable force of cable-stayed bridges in real time, a spectrum analysis method based on amplitude and phase estimation (APES) was proposed in this study. The amplitude spectrum of the acceleration data is first calculated by the APES method, the real-time spectrogram of the cable is obtained by the sliding window method. Then the modal frequency and amplitude are automatically extracted from the real-time spectrum by using a frequency extrusion post-processing technique, which can be regarded as the average value of the instantaneous frequency and amplitude respectively. Next, the fundamental frequency of the cable is extracted by using an automatic identification method, and the performance of the proposed method is verified. Finally, real-time scoring of cable forces and structural condition assessment is achieved with consideration of the moderation index model as well as the material strength. The results show that the APES method can use shorter calculation samples than the traditional Fast Fourier Transform (FFT) to obtain higher resolution and more accurate amplitude, which provides a theoretical basis for the real-time identification of fundamental frequency based on short-term monitoring data. The frequency extrusion post-processing-algorithm can reduce the spectrum recognition delay and improve timeliness of the cable force evaluation. The time-varying cable force with an interval of 10 s can be used to reflect the health status of the cable in time. The research results can provide technical support for the real-time extraction of cable force of long-span cable-stayed bridges, and can also provide an effective basis for component condition evaluation and bridge maintenance decision-making.

A Frequency Identification Approach for Damaged Heavy-Haul Railway Bridge with Bogie Accelerations

Bridge structural health monitoring (BSHM) is extensively employed to assess whether bridge damage exceeds maintenance limits and helps maintenance decision-making. Frequency identification is an essential part of BSHM, which utilizes the first-order modal frequency as an evaluation indicator of bridge health status. This paper proposes a method for bridge damage identification based on extracting the first-order modal frequency of the bridge from dynamic responses of vehicle bogies. Firstly, the feasibility of extracting bridge modal frequency from bogie accelerations is theoretically deduced via a vehicle–bridge interaction model. The result indicates that the bogie accelerations encompass three frequency components, namely vehicle driving frequency, vehicle pitching frequency and bridge first-order modal frequency. Then, a multi-domain conjoint analysis method incorporating the time domain, frequency domain and time–frequency domain is proposed to extract the first-order frequency of damaged bridge from bogie accelerations. By defining a damage sensitivity index of the sliding window, the time-domain sensitive boundaries of bogie acceleration to bridge damage are pinpointed. Leveraging prior knowledge of the bridge modal frequency range, the frequency-domain sensitive boundaries bogie acceleration to bridge frequency are determined. On this basis, the sensitive region of bogie acceleration to bridge frequency can be precisely localized in the Hilbert Huang transform spectrum, ultimately identifying bridge frequency through searching local maximum instantaneous energy points within this region. Finally, comprehensive experiments, considering different vehicle speeds and track spectra scenarios, are carried out to investigate the effectiveness and robustness of the proposed method. The results affirm the performance of the method to accurately identify the first-order modal frequency of the damaged bridge, further underscoring the promising prospect of on-board BSHM.

Measurement Refinements of Ground-Based Radar Interferometry in Bridge Load Test Monitoring: Comprehensive Analysis on a Multi-Span Cable-Stayed Bridge

This paper presents three refinements in ground-based radar interferometer (GB-radar) measurement for bridge load testing: (1) GB-radar phase jumps were detected for the first time on bridge tower displacement monitoring, and a recovery method is presented to obtain the correct unwrapped value; (2) a precise displacement projection method considering target deformation was exploited, and a case study of the Fifth Nanjing Yangtze River Bridge (FNYRB) GB-radar campaign shows that a centimeter-level compensation can be achieved; (3) a post-construction settlement phenomenon was found during the FNYRB static load tests, characterized by 0.31 mm/min, which accumulated up to 25 mm. In addition, the dynamic monitoring capabilities of GB-radar for the bridge tower and girder were verified, highlighting its potential for bridge structural health monitoring (SHM). The insights gained from this study offer valuable recommendations for future GB-radar bridge displacement monitoring.

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review.

Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes model-based and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility.

Train-induced vibration response reconstruction for bridge damage detection with a deep learning methodology

Sensor malfunctions in bridge structural health monitoring system (BSHMS) could cause the missing of measured data, reducing the accuracy of damage detection. It is of great significance to reconstruct the data of faulty sensors with measured data of well-functioning sensors for bridge structural state assessment. In this paper, a train-induced vibration response reconstruction approach for bridge damage detection is proposed. An enhanced deep densely connected neural network (ED-DCNN) introducing a multi-head self-attention mechanism and a skip connection is designed to reconstruct the missing bridge responses, aiming to improve the vibration correlation between the faulty sensors and good sensors and benefit the learning of temporal and spatial features of responses. Then to further validate the accuracy and reliability of the ED-DCNN, an alternate down-sampling residual network (AD-ResNet) is built to identify bridge damage with the complete responses, including the reconstructed ones and the measured, which extracts the structural damage features at different scales utilizing different types of residual blocks in an alternating manner. The efficiency and robustness of the two networks are comprehensively studied using the collected acceleration data from a heavy-haul railway bridge, considering different noise levels. The results demonstrate that under various conditions, the reconstructed responses match the real responses well in time domain, with a low RMSE below 0.4% and a high correlation coefficient of 0.9744, which indicates that the incorporation of a multi-head self-attention module effectively improves the construction of correlations between responses. Additionally, the natural frequencies of bridge are accurately identified from the reconstructed responses. In terms of bridge damage detection, taking both the measured and reconstructed responses as inputs, the identification accuracy obviously improves by 33.57% compared to cases without the reconstructed responses, further emphasizing an excellent reconstruction capability of the ED-DCNN.

Contact-point response reconstruction for indirect bridge monitoring via Bayesian expectation-maximization based augmented Kalman filter

The drive-by bridge dynamic measurement using an instrumented vehicle during its passage over the bridge can be an indirect bridge structural health monitoring system. The identification of the contact-point (CP) responses between vehicle wheels and bridge structure from vehicle responses offers an efficient and economical way for the modal identification and condition assessment of short- to mid-span road bridges. This paper proposes a novel method for the contact-point displacement response reconstruction of the vehicle-bridge interaction (VBI) system in a joint input-state estimation manner based on Bayesian Expectation-Maximization (BEM). An analytical model of VBI including the road approach in front of a simply-supported bridge is presented firstly. Then the augmented state-space model of the vehicle with the contact-point responses included as variables along with vehicle states is established. A new method by integrating the augmented Kalman filter (AKF) with a BEM strategy is proposed to solve the state estimation problem without knowing the vehicle axle responses. The vehicle states and CP displacement responses are identified simultaneously. The effects of the measurement noise, road surface roughness and vehicle speed on the identified results are investigated using numerical simulations. The experimental tests are used to further verify the feasibility and accuracy of the proposed method. The results show that the proposed method has potential for drive-by bridge condition assessment using on-board vehicle response measurements for a large population of short- to mid-span bridges.