Bridge Health Research Articles

Long-term missing wind data recovery using free access databases and deep learning for bridge health monitoring

Wind environment monitoring at the bridge site is essential in structural health monitoring (SHM). However, as the performance of the electronic equipment for bridge SHM system deteriorates, wind monitoring data often suffer from long-term continuous data missing. The duration of data missing may be as long as several months, creating barriers to safety monitoring and monitoring data mining of the bridge structures. The conventional interpolation techniques hardly achieve the desired data recovery. Therefore, a framework was proposed for long-term missing wind data recovery based on a deep neural network (DNN) utilizing a free access database (European Center for Medium-Range Weather Forecasts, ECMWF). This framework consisted of one regression task (Task 1) and one temporal super-resolution task (Task 2). In Task 1, the hourly wind data provided by ECMWF were first learned to the hourly ones of the bridge SHM system. In Task 2, the low-resolution wind data (hourly averages) were upsampled to high-resolution ones (10-min averages). The U-net architecture provided the basis for the DNNs in both tasks. Unlike the conventional time-domain loss function used in Task 1, Task 2 adopted a time-frequency cross-domain loss function for training, which innovatively employed a spectrum magnitude balance strategy to enhance the reconstruction of the high-frequency components of wind speed signals. The proposed framework's feasibility and effectiveness were verified through a case study of recovering long-term continuous missing wind data in the SHM system of Sutong Bridge, China. The proposed methodology provides a new perspective for recovering long-term continuous missing SHM data.

Performance evaluation of GNSS kinematic PPP and PPP-IAR in structural health monitoring of bridge: Case studies

Monitoring the bridge structural health and finding bridge damages in time are of great significance to ensure the safe operation of bridges. In this paper, we aim to evaluate the availability of Global Navigation Satellite System (GNSS) precise point positioning (PPP) technology in bridge structural health monitoring with float ambiguity solution and with inter ambiguity resolution (PPP-IAR). Based on 10 stations evenly distributed in China, we find that PPP-IAR solutions have a faster convergence speed than PPP solutions, and the accuracy of better than 1 cm in horizontal and 2 cm in vertical direction can be achieved by GPS/BDS/Galileo (GCE) combined PPP-IAR. From a simulated displacement experiment, PPP-IAR has a better accuracy and faster convergence speed than PPP for the identification of displacements at different levels. Finally, the fieldwork experiment of three bridges with different spans and states are conducted. Results show that both PPP and PPP-IAR method can effectively identify the bridge vertical displacements resulting of the vehicle loading at centimeter accuracy. Meanwhile, PPP and PPP-IAR method are capable of extracting low-order vibration signals of bridge, which are comparable to the double-difference (DD) solution. It demonstrates that PPP and PPP-IAR technology could be an alternative method to DD for bridge health monitoring, and PPP-IAR is expected to have a faster convergence speed and higher accuracy in the real-time monitoring of bridges.

Comparative study on sensitivity of acceleration and strain responses for bridge health monitoring

ABSTRACT Bridge health monitoring has been attempted to ensure the safety of the bridges in their operations, employing various measurement options like acceleration, strain, displacement, etc. The relative efficacy of these measurements as a damage-sensitive response has remained a topic of research. While acceleration has traditionally been used in abundance, dynamic strain, being relatively cheaper to record, also holds the potential to replace acceleration. This study undertakes a comparative investigation weighing the relative benefits of both the measurement options for prompt and reliable damage detection in both the time and frequency domain. The comparison is drawn in the light of damage sensitivity, intensity and consistency of the damage signature of the adopted measurement type while keeping the damage and loading specifications unaltered. A multi-span concrete box girder has been replicated with a high-fidelity numerical model as a proxy for the real structure followed by an experimental validation on a propped cantilever beam. Acceleration and strain responses are measured and analyzed for different damage conditions. A rigorous sensitivity analysis is undertaken to compare explicitly the performance of both the measurement options. The results demonstrated superior performance with the strain response in time and frequency domains from consistency and intensity perspectives.

A novel approach to investigate the mechanical properties of the material for bridge health monitoring using convolutional neural network

In the bridge structure, the main bearing element is the spans, which are directly subject to random and continuous loads, environmental influences, ageing, etc. These factors are easy to lead to material deterioration. Therefore, this study proposes a novel approach to investigate the mechanical properties of the material. The span of the bridge is the selected structure for investigation. This study focuses on the general evaluation of mechanical factors consistent with reality, so the following main factors are to be carried out. First, the viscoelastic model of material will be applied to set up and solve the governing differential equation of the beams with material characteristics involving the elastic modulus (E) and the viscous coefficient (C). The viscoelastic model is different from the traditional elastic model due to its non-linearity, reflecting the actual state of the structure. Second, the random vibration signal-based Loss Factor function (LF) calculation using the Power Spectral Density (PSD) to detect changes in structures. LF is a feature representing changes in material properties, including elasticity and viscosity, and is suitable for many types of bridge structures. Also, the paper uses Cubic Interpolation (CI) to generate a surface representing the LF distribution. This interpolation results in surfaces with respect to the LF values distributed by frequencies and spectral amplitudes. Finally, the LF distribution-based material investigation using Convolutional Neural Network (CNN) is proposed with high performance and accuracy. This study applies the proposed method to several bridges in Ho Chi Minh City, Vietnam. It demonstrates that LF is highly suitable for bridge health monitoring.

Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions. The current cloud-based solutions cannot scale if the raw data has to be collected from thousands of buildings. This paper presents a full-stack deployment of an efficient and scalable anomaly detection pipeline for SHM systems which does not require sending raw data to the cloud but relies on edge computation. First, we benchmark three algorithmic approaches of anomaly detection, i.e., Principal Component Analysis (PCA), Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4, with limited computing capabilities. Our approach decreases network traffic by $\approx8\cdot10^5\times$ , from 780KB/hour to less than 10 Bytes/hour for a single installation and minimize network and cloud resource utilization, enabling the scaling of the monitoring infrastructure. A real-life case study, a highway bridge in Italy, demonstrates that combining near-sensor computation of anomaly detection algorithms, smart pre-processing, and low-power wide-area network protocols (LPWAN) we can greatly reduce data communication and cloud computing costs, while anomaly detection accuracy is not adversely affected.

A multiscale local pattern filtering method for noise mode identification of dynamic signals in bridges

The problem of effective information extraction of dynamic signals is a key challenge of vibration-based structural health monitoring. Traditional ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) are effective filtering methods but have the shortcomings of mode mixing, completeness and the endpoint effect in adaptive decomposition stage. To address the above problems, this paper proposes a noise mode identification method based on multiscale local pattern filtering with a machine learning algorithm. In addition, this method improves the filtering performance of mode decomposition and wavelet threshold denoising methods effectively. To validate the performance of this method, simulation experiment and real signal analysis were used for validation: their results showed that the proposed method has better adaptive decomposition performance than EEMD and CEEMD. Compared with the EEMD, CEEMD and wavelet threshold denoising methods, the proposed method can better balance smoothness and the power reserve, has optimized time- and frequency-domain characteristics, filters with a higher signal-to-noise ratio and reliability, and has more optimized filtering performance. The method proposed in this paper can be applied to bridge health monitoring technology, and has important scientific and engineering significance.

Genetic Algorithm Supported by Influence Lines and a Neural Network for Bridge Health Monitoring

This paper proposes a hybrid technique to solve the inverse problem of damage localization and severity estimation in beam structures. The first phase of the method involves the use of influence lines (ILs) to extract information about the damage location. Then, a genetic algorithm (GA), representing the core of the whole procedure, uses static parameters as displacements and rotations at a few points to evaluate the bending stiffness along the structure by updating a finite-element model. The information obtained in the first phase is used in the second phase for (1) reducing the number of design variables of the GA and the consequent computational time; and (2) improving the accuracy of GA solutions because it allows a suitably trained neural network to select proper values for the coefficients of the proposed cost function in the genetic algorithm. The procedure is applied to a test problem, a simply supported, prestressed concrete railway bridge located in northern Italy. Numerical experiments are also conducted to test the procedure when the beam length and geometric properties vary.

Online Sifting Technique for Structural Health Monitoring Data Based on Recursive EMD Processing Framework

Real-time and online screening techniques for single load effect signal monitoring are one of the key issues in smart structure monitoring. In this paper, an online signal sifting framework called online recursive empirical mode decomposition (EMD) is proposed. The framework is based on an improved EMD that optimizes the boundary effect by using extreme value recursion and eigensystem realization algorithm (ERA) extension, and combines the intrinsic mode functions (IMFs) correlation coefficient and adaptive filtering to select IMFs for signal reconstruction to achieve the sifting purpose. When applied to simulated signals, the method satisfies the requirements of signal sifting in an online environment with high adaptivity, low parameter sensitivity and good robustness. The method was applied to the dynamic strain data collected by the health monitoring system of Daishan Second Bridge to achieve real-time online sifting of strain signals caused by traffic loads, which provided the basis for subsequent data analysis applications and confirmed the value of the application in a real bridge health monitoring system.

Fatigue Durability Analysis for Suspenders of Arch Bridge Subjected to Moving Vehicles in Southwest China

An analysis of the effect of vehicle loads on the fatigue life of local arch bridge suspenders in Southwest China was conducted by generating vehicle statistics, creating a fatigue vehicle model, and developing a fatigue life prediction method: First, the Midas model of an arch bridge was established, a random vehicle flow simulation was conducted in the Monte Carlo method, and the stress spectrum of the suspenders was calculated. Secondly, ANSYS software was used to estimate the fatigue life of the suspenders, and the accuracy of the proposed method was verified. Finally, the method was applied to the practical project of the Dafeng River Bridge in Southwest China. The results show that this method can predict the fatigue life of suspenders. It can be applied in the bridge health monitoring software system to realize real-time fatigue damage monitoring of the fatigue damage of suspenders, which could be used to predict the fatigue lives of bridges in Southwest China and provide references for the replacement of suspenders.

Research on optimized layout of bridge sensors based on MAC stepwise addition and subtraction algorithm

The difference in the location and number of sensors induces the acquisition effect of structural state information in the bridge health monitoring system to a certain extent, which is also one of the most difficult problems restricting the development of bridge health monitoring technology. Based on the Modal Assurance Criterion, this paper took a cantilever simply supported beam as an example and adopted the Effective Independence method, Kinetic Energy method and QR decomposition method to optimize a sensor layout. Taking the number of sensors as 5, the layout and MAC matrix factors were analyzed by comparing the MAC matrix and its distribution diagrams obtained by different methods. The results showed that the stepwise subtraction method based on QR decomposition was the best. And by changing the number of sensor, it was found that the optimal number of sensor was 9. It was also found that the existing methods had the shortcomings of over-reliance on the selection of initial measuring points. In order to improve this defect, cyclic iteration of stepwise addition and subtraction was introduced, and an optimal layout method based on the stepwise addition and subtraction of the number of sensors was proposed. Through screening and optimization of this method, the average off-diagonal element in the calculation example was reduced from 0.118 to 0.006 to achieve the satisfactory optimization effect. At the same time, this method was employed to optimize the layout of a concrete continuous rigid-frame bridge with 9 and 19 sensors, and the results showed that the sensor layout was more uniform. Therefore, the proposed method provides the opportunity to obtain fair optimization results and to have a considerable engineering application value.

High-efficiency piezoelectric energy harvester for vehicle-induced bridge vibrations: Theory and experiment

Massive dissipated vibration energy of railway bridges is a sustainable energy source that can be harvested by piezoelectric energy harvesting technology. It provides a promising solution for powering bridge health monitoring systems. However, it remains challenging to achieve high energy output for the harvested energy to be of utility value. An efficient dynamic-magnified piezoelectric energy harvester (D-M PEH) is proposed, which reaches an energy output as high as 0.55 W under a harmonic acceleration excitation of unit amplitude and low frequency. The high energy output is achieved by incorporating a dynamic amplifier as well as several composite piezoelectric transducer cells (PT-cells) with d33-mode. The lumped parameters of the PT-cell are theoretically derived by considering the stacking deformation caused by the inverse piezoelectric effect. The energy harvesting performances of the D-M PEH are then investigated both theoretically and experimentally. When a train passes through the bridge, the maximum output voltage and power of the proposed D-M PEH can reach 603 V and 0.728 W, respectively. The output power is much higher than that of reported piezoelectric energy harvesters installed on railway bridges. The prototype system holds great potential to be part of the smart railway.

Systemic reliability of bridge networks with mobile sensing‐based model updating for postevent transportation decisions

AbstractThis paper proposes the upscaling of conventional individual bridge health monitoring problems into urban regions and transportation networks via mobile and smart sensing techniques together with an innovative reconnaissance procedure. The paper associates structural failure probabilities with systemic features and proposes decision criteria to optimize postdisaster actions. Twenty bridges constituting transportation network infrastructure compose the testbed region and utilize smartphone accelerometers for dynamics characterization in a vibration‐based framework. In this framework, reconnaissance output serves for model development, and mobile sensor data enable finite element model updating. Structural reliability analyses merged in a chain setting generate the systemic behavior of cascaded bridge performance. Combining systemic reliability with transportation and health services demand, one can optimize the response strategies of the bridge population and strategize disaster‐related decisions in a postevent assessment setting. Based on a testbed region with remote access to nearby vicinities, 18 earthquake scenarios are conducted to visualize the optimal evacuation strategies on the network, taking systemic bridge performance into consideration. Cost‐free mobile sensing support adds one more fundamental information source for reducing the uncertainty of the models and, therefore, improves associated mitigation actions.

Application of Wireless Sensor Network in Bridge Health Monitoring

Application of Wireless Sensor Network in Bridge Health Monitoring

Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis.

This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios.

A hybrid time-frequency method for robust drive-by modal identification of bridges

Indirect bridge health monitoring (iBHM) has gained significant attention in recent years as an alternative to direct bridge monitoring techniques. iBHM leverages the use of a vehicle traveling over a bridge as a sensing device as well as an actuator. The resulting data acquired from the vehicle contains dynamic information about the bridge, vehicle suspension, engine noise, ambient traffic noise, road roughness, etc. Due to the presence of sensor-vehicle-bridge interactions in vehicle measurement, a powerful decoupling framework of system identification is required to extract useful information about the bridge and delineate the effect of vehicle and driving frequencies. This paper proposes a hybrid time-frequency method capable of decoupling vehicle-bridge interactions of vehicle measurement and performing a robust bridge modal identification under various operational challenges. In the proposed research, wavelet packet transformation (WPT) is first used to decompose the vehicle measurement into various WPT coefficients. Then, the modal frequencies of the bridge are extracted from the resulting WPT coefficients using Synchro-Extracting Transform (SET) and are separated from the vehicle and driving frequencies. The proposed method is validated using numerical and laboratory studies as well as a full-scale study comprising of a 220 m long box-girder bridge. The results provide strong evidence that the proposed hybrid time-frequency method can serve as a promising iBHM method.

Fatigue Resistance of a BFRP-Encapsulated Long-Gauge FBG Strain Sensor under Cyclic Train Loads

To verify the performance of a basalt fiber reinforced polymer (BFRP) fiber-encapsulated long-gauge strain sensor in railway bridge health monitoring, this paper studies the fatigue resistance and durability of the BFRP fiber-encapsulated FBG sensor under train loads. First, the influences of the length of the anchorage section and the length ratio of the sensing section on the accuracy of the sensor were studied. Then, the BFRP sensor was applied to a sleeper for 2 million cycles of tension fatigue testing. The strain-time history of the whole fatigue test was monitored and compared. After the test, a calibration test was carried out to verify the accuracy and repeatability of the sensor. Finally, the slip and fatigue cracking of the fiber in the anchorage section of the sensor were observed by electron microscopy. The results show that the gap between the anchoring section and the bare optical fiber was filled with epoxy resin, and there was no slip behavior. No fatigue cracking occurred in the fiber, and the strain coefficient and linearity of the sensor showed no obvious changes after 2 million cycles of loading. The long-gauge strain sensor encapsulated by BFRP fibers exhibited good fatigue resistance and can meet long-term monitoring requirements under train loads.

Paradox of telemedicine: building or neglecting trust and equity

Paradox of telemedicine: building or neglecting trust and equity

Estimations of the elastic moduli of concrete at different temperatures and humidities by mesomechanics methods

Changes of environmental temperature and humidity affect the elastic modulus of concrete, which makes the vibration-based bridge health monitoring method unreliable. This research studied the effects of environmental temperature and moisture content on the effective elastic modulus of concrete by the mesomechanics methods. In meso level, for the two components of concrete, pores and micro cracks, effects of water in them on the effective elastic modulus at different temperatures were studied separately. Then estimations of the effective elastic moduli at different temperatures and moisture contents were proposed. To verify this, the calculated effective elastic moduli were compared with the experimental values. Finally, the mechanism of the variations of the effective elastic moduli with temperature was revealed. The proposed estimations of the effective elastic modulus can offer a simple and reliable approach to obtain the accurate elastic modulus of concrete based on the environmental temperature and concrete moisture content, which can eliminate their effects in bridge health monitoring.

Probabilistic Statistics-Based Endurance Life Prediction of Bridge Structures.

With the massive construction of bridge infrastructures, bridge health monitoring systems have gradually matured in application and research, but previous research has primarily focused on structural damage detection and bridge safety warnings based on valid data. The structural details of steel bridge panels and structural systems are determined by the coupling effects of many intrinsic and extrinsic uncertainties, such as material properties, structural characteristics, manufacturing processes, and random traffic loads. The evaluation of fatigue is a difficult task. This article first builds a big data platform, utilizing its high-efficiency parallel computing capability and highly fault-tolerant distributed file system to achieve second-level monitoring data processing; ensuring real-time data cleaning, data analysis, and safety warning; and building a big data analysis and processing platform with high reliability, high availability, high storage efficiency, and high scalability of bridge health monitoring. The big data platform chooses HDFS for offline data storage and Spark for data analysis and modelling after comparing and analysing the benefits and drawbacks of various big data technologies. Kafka is used for caching real-time data, and Spark-streaming is used for reading data and real-time processing. Finally, the platform's superiority and reliability in terms of offline computing performance, real-time online performance, scalability, and fault tolerance are confirmed through experimental analysis; the optimal data cleaning method is derived by comparing and analysing monitoring data noise, jump point, and drift phenomena. This part of the research is based on bridge temperature data with stable signals and bridge strain data with fluctuating signals, taking into account the influence of different data types; the corresponding data missing repair algorithms are proposed for different types of data to form a complete and general data patching method process. The probabilistic fracture mechanics theory, in comparison to the traditional deterministic fatigue assessment method, can better reflect the essential uncertainty of fatigue problems and is an effective way to assess the fatigue performance of orthotropic steel bridge decks. The goal of data patching is to ensure data recovery accuracy of over 90%, with no patching repair required for monitoring data with too much missing data. The endurance life of bridge structures is predicted using a big data probabilistic statistics approach based on a variety of factors such as material properties, construction characteristics, manufacturing processes, and random traffic loads.

A drive-by bridge inspection framework using non-parametric clusters over projected data manifolds

Developing robust bridge health monitoring (BHM) frameworks based on the vehicle-mounted sensing, or so-called indirect structural health monitoring (SHM) or Drive-by Bridge Inspection is currently a rapidly growing research area. The goal is to monitor any change in the structural integrity of a bridge using the vibration responses measured from sensors installed on a vehicle passing over the bridge. To this aim, we present a novel data-driven approach by integrating a nonlinear dimensionality-reduction technique using Uniform Manifold Approximation and Projection (UMAP) together with a non-parametric clustering technique using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Ultimately, through the combined use of UMAP and HDBSCAN, it is demonstrated that such methods align perfectly well with the problems and restrictions present when analyzing drive-by bridge inspection data. That is, in the drive-by bridge inspection context data sets may have low cardinality, and the individual clusters may have different sizes and densities and do not need to have a spherical shape. Furthermore, new clusters may emerge over time as new damage cases evolve. The pairing of UMAP and HDBSCAN will be shown to assist in these issues in a principled manner. For the validation, we first make use of the theoretical formulation of the vehicle-bridge interaction system and conduct a finite element simulation of this system to obtain the moving vehicle response once traveling over five different bridge states. Further, a simply supported bridge model in the laboratory is considered for the experimental validation. The structure of the bridge model is gradually changed by adding multiple stiffener plates, and by gradually increasing a concentrated mass at mid-span. The proposed method is then applied to identify changes that occur in the laboratory bridge model. The experimental results for damage characterization demonstrate that various bridge states can successfully be separated from one another. The contributions of the work are three-fold; (1) First, a novel feature extraction and classification framework based on the combined use of UMAP and HDBSCAN is developed and validated. (2) Second, this paper demonstrates one of the early successful attempts of drive-by bridge inspection for monitoring the progressive change in the structure of a bridge based on the extensive numerical and experimental investigations. (3) Finally, the research presented in this work can open up new opportunities for condition monitoring of bridge network.