Bridge Condition Assessment Research Articles

Effect of Non-Uniform Temperature Distribution on the Natural Frequency of Concrete Girder Bridges

Structural dynamic properties like frequencies and mode shapes have been widely adopted for bridge condition assessment and damage identification. One of the main challenges lies in environmental factors, particularly the varying temperature, which significantly influences the bridge’s natural frequencies. In some cases, the effect of temperature changes can be comparable to, or even exceed, the effect caused by damage, rendering the damage identification methods ineffective. This study investigates the influence of the cross-sectional non-uniform temperature distribution, as a result of surrounding environmental factors, on the frequency of concrete girder bridges. A theoretical analysis is conducted to derive the bridge’s frequencies considering the cross-sectional non-uniform temperature distribution. The upper and lower bounds of the frequencies are developed by using only the environmental temperature and the largest temperature gradient defined by codes. More importantly, the damage to the bridge can be detected if the frequencies exceed the bounds for a certain period. This approach is convenient and practical in real applications without embedding thermocouples in the main girder. Numerical simulations, laboratory experimental tests, and field measurements are carried out to validate the derived frequency considering the cross-sectional non-uniform temperature distribution as well as the upper and lower bounds of the frequency. In particular, the results of the Z24 bridge show that when the bridge is damaged, the measured frequency exceeds the bound continuously, verifying the capability of the developed upper and lower bounds to evaluate bridge conditions.

Advanced TOPSIS Approach to Variable Index Weighting in Bridge Condition Assessment

Advanced TOPSIS Approach to Variable Index Weighting in Bridge Condition Assessment

Technologies and Platforms for Remote and Autonomous Bridge Inspection – Review

Recent scientific and technological advancements have enabled a more efficient structural condition assessment of bridges, mainly through the implementation of intelligent inspection strategies. These intelligent strategies can provide early identification of critically damaged components before failure, and will therefore play a key role in extending the life of infrastructure. The latest inspection technologies can provide inspection plans with damage conditions, create a damage report as well as provide statistics and comparisons to the previous inspection findings. The new and existing inspection technologies are directed to help with the digitalization of the Bridge Management System (BMS). The complexity of maintenance/inspection requires organized, automated, open and transparent digital processes, which should consider both—structure and asset management data. Further, the inspection/monitoring findings serve as a source for decision-making models. The digitalized aspects of autonomous inspection provide better performance prediction models and guarantee safety for the users. This paper presents the latest findings in the field of remote inspection of bridges. In particular, the main technologies for inspection and geometrical assessment are depicted, especially those based on computer vision systems installed in UAVs and robots, LiDAR, radar, satellites and other non-contact systems including on-board monitoring. The accompanying article entitled: “Methodologies for remote bridge inspection” deals with the methodologies used for data processing based on Artificial Intelligence (AI).

A computer vision–aided methodology for bridge flexibility identification from ambient vibrations

AbstractThis paper presents the implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to identify the structural flexibility from the ambient vibrations. The magnitude ratio between the flexibility estimated from known/unknown input force are theoretically derived and decomposed into two parts: and . The first scale factor related to basic modal parameters can be acquired using the general modal identification methods. Aiming to tackle the difficulty in identifying the second scale factor related to the force intensity, a video stream of traffic is processed to detect and classify vehicles to determine the vehicle's location while displacement measurements are simultaneously collected. By integrating the toll station data, the vehicle loads are assigned to the vehicle on the bridge deck through the uniqueness of the license plate number. Thus, a structural input–output relationship is established to solve the second scale factor . Finally, the flexibility estimated from the ambient vibration are scaled by and , respectively to obtain the exact flexibility , which are same as the analytical ones . Both numerical example and a laboratory test are performed to demonstrate the accuracy of the proposed methodology. The algorithms, approaches, and results given in the paper demonstrate its effectiveness and shows great potential for its application on a real‐life bridge's condition assessment.

A Generative adversarial network model for estimating temporal frequency variation of vehicle-bridge interaction using modified Stockwell transform

The presence of massive and directional vehicles moving on a railroad bridge causes temporal fluctuations in the fundamental frequencies of both bridge and vehicle systems, making traditional structural inspections difficult to employ effectively. Thus, this paper proposes an image-to-image Generative Adversarial Network (GAN) that estimates temporal frequency variation for the vehicle-bridge interaction system. To address the challenges associated with conventional approaches, eigenvalue analysis, numerical substitution, and Fourier series approximations are examined using a simple degree of freedom and a more complex model. Thus, the GAN model is proposed to overcome those limitations. In the framework, based on the properties of a real bridge and vehicle model, the modified Stockwell transform of bridge acceleration from the dynamic simulation is used as input data with the paired data from the numerical substitution approach. Then, the model is validated through quantitative measures and laboratory scale experiments. Results showed that the model has strong performances across the various scenarios, demonstrating the potential for bridge condition assessment under operational conditions.

Index of Attention for a Simplified Condition Assessment and Classification of Bridges

Index of Attention for a Simplified Condition Assessment and Classification of Bridges

Linear and nonlinear time-series methodologies for bridge condition assessment: A literature review

Railway bridges are essential components of any transportation system and are typically subjected to several environmental and operational actions that can cause damage. Furthermore, they are not easily replaced, and their failure can have catastrophic consequences. Considering the expected lifespan of bridges, it is essential to guarantee their adequate serviceability and safety. In this scenario, emerges the Structural Health Monitoring (SHM), which allows the early identification of damage before it becomes critical. Damage identification is usually performed by the comparison between the damaged and undamaged responses obtained from monitoring data. Among the several features extracted from the responses, the time-series models exhibit a better performance, capability of early damage detection, and may also be applied within online damage detection strategies using unsupervised machine learning frameworks. In this paper, a review of advanced time-series methodologies for damage detection is presented. Initially, several time-series models often used in SHM are described, such as Autoregressive Models (AR), Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). Later, the framework where these models are usually applied is also detailed, including the latest upgrades and most relevant results. Finally, the conclusions summarize and elucidate the current perspectives and research gaps on the time-series models.

Development of a model for assessing the technical condition of Baghdad bridges

The research aims to develop a significant model for assessing the condition of bridges. This model may serve as a supplementary source of information in determining bridge condition ratings not only in Baghdad, but also in other regions with similar operating conditions. The authors also used the methods of mathematical statistics, probability theory, and mathematical theory of planning to conduct an experiment using the results of bridge survey. Currently, the Commission on Audit uses financial depreciation models to determine the service life of structures, with no alternative formulas available for predicting this parameter in the country. Therefore, the authors consider a new approach on the basis of available research on depreciation models. This approach is based on assessment of the condition of 30 structural elements of a bridge, as well as the results of inspections conducted by the Department of Public Works and Highway Bridges. The purpose was to determine the applicability of available data as meaningful variables in identifying a regression model to predict the condition of such bridges in Baghdad. Statistical data for determining the coefficients and dependent variables provided a relevant model for bridge condition assessment, which can be applied to identify the technical state of bridge structures.

Comparative Study on PSD and HHT Techniques for Condition Assessment of Steel Truss Bridge Using Acceleration Data

Bridge structures deteriorate over time due to factors like corrosion, fatigue, aging of materials, and unexpected natural or accidental events. Consequently, the vibrations measured in these structures exhibit time-varying and non-stationary characteristics. Additionally, non-linearities are inherent in these structures due to material behavior, boundary condition changes, and joints or connections. Analysis of non-linear and non-stationary structural vibration data is necessary to extract information on the condition of the bridge. Most methods assessing bridge conditions from acceleration responses rely on the Power Spectral Density (PSD) which is based on the Fourier Transform. However, the accuracy of the Fourier transforms for non-linear and non-stationary signals limits its application potential. The Hilbert–Huang Transform (HHT) has emerged as an alternative for handling these non-stationary signals due to its time-frequency-energy representation. This study provides a comparative study of both techniques by utilizing the data obtained from continuously monitored Pamban Bridge. This steel truss bridge was fitted with accelerometers to record bidirectional acceleration time histories at twenty bottom node points on the bridge. This study examines the continuous variations in the frequency component by both methods for 136 days. A linear best-fit trendline is obtained for the frequency parameters at different sensor positions. The slope and intercept values of this linear best fit trendline is further studied. The HHT showcases various evolving dominant frequency components over time in contrast to the PSD representation. The frequency spectrum from the HHT displays less variation over time at each sensor position compared to the predominant peak value derived from the PSD of the acceleration signal. The spatial variability in the predominant frequency obtained from HHT is also less than the PSD. Therefore, the ability of HHT to discriminate damages needs further investigations.

Crowdsensing-based automatic bridge health condition assessment using drive-by measurements and deep learning

In recent decades, assessing the structural health conditions of aging bridges has emerged as a significant concern. A recent drive-by measurement method has attracted substantial attention, in which only several sensors are installed on crowdsensing vehicles rather than bridges, providing a more economical and convenient solution. This paper proposes an automatic bridge condition assessment framework incorporating drive-by measurements and deep learning techniques. The methodology involves collecting and segmenting accelerations from a vehicle passing a healthy bridge into short-time overlapped frames. Over multiple vehicular passes, all frames are then transformed into frequency-domain responses, forming the input for training an unsupervised deep learning model. The model is then trained to reconstruct the input using these frequency-domain responses. In assessing the bridge with an unknown health state, the trained model is employed to reconstruct the passing vehicle’s new short-time frames, and the response construction error automatically determines the bridge’s health condition. Experimental validation utilizing a laboratory bridge and scaled truck demonstrated that the trained model could consistently identify a healthy bridge during passages, with larger reconstruction errors indicating that the bridge was damaged. The innovative framework showcased promise for efficient and reliable bridge health condition assessment.

Improved bridge modal identification from vibration measurements using a hybrid empirical Fourier decomposition

Bridge health monitoring has been a prominent focus within the global engineering community. Bridge owners, stakeholders, and engineers face the formidable tasks of ensuring efficient monitoring, conducting reliable data analysis, interpreting data logically, and making timely decisions. With the increasing global infrastructure deficit, there is an ever-increasing need to develop reliable and economical bridge monitoring solutions. In this paper, a bridge condition assessment technique is proposed that can utilize the vibration data collected from the instrumented sensors and provide reliable system identification results. The proposed method develops a hybrid approach by integrating the Natural Excitation Technique (NExT) and Empirical Fourier Decomposition (EFD) to analyze ambient bridge vibration data and determine the modal parameters of the bridge. First, NExT is formulated to determine the cross-correlation functions of the bridge measurements, and then EFD is explored to decompose the signals into their monocomponents to identify the bridge modal parameters. The proposed methodology can overcome mode mixing and perform modal identification of a system with closely spaced frequencies and low energy modes. The estimated modal parameters such as bridge frequencies, mode shapes, and damping ratio are used for condition assessment of numerical, experimental and full-scale structures, including a short-span steel bridge located in Ontario, Canada. The results demonstrate that the proposed methodology can provide accurate and robust estimates of bridge modal parameters. Future research is reserved for real-time implementation of the proposed methodology for a wide range of civil structures.

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.

Intelligent identification of moving forces based on visual perception

The task of identifying moving forces presents a challenging inverse problem for bridge health monitoring and condition assessment. Existing methods for identifying moving loads rely on complex structural response measurement equipment, resulting in inefficiency and elevated costs. To address these challenges, this study introduces an innovative intelligent identification method for moving forces on bridges using visual perception techniques. The proposed method utilizes cameras to capture displacement changes at specific points on the bridge surface to reconstruct moving forces in the time domain. Initially, numerical simulations were conducted to ascertain the feasibility and robustness of identifying moving forces based solely on the structural displacement response. This phase also investigates the influence of different measurement point combinations on identification accuracy. Subsequently, the accuracy of the proposed method in measuring the dynamic displacement response of the structure was carefully evaluated by an outdoor shaking table test. Furthermore, a meticulously designed simply supported beam model was fabricated, followed by the execution of precise vehicle-bridge coupling dynamic experiments. This phase rigorously validates the effectiveness and accuracy of the proposed moving force identification method at varying vehicle speeds. The obtained results substantiate the proposed approach's capability not only to comprehensively capture bridge response data for full-area monitoring but also to consistently and reliably identify information on moving vehicle forces. This study underscores a pioneering application of intelligent visual perception technology in the field of identifying moving forces. The introduced noncontact intelligent identification method is an effective solution for monitoring moving forces on bridges, which has a wide range of applications in the future.

Online Bridge Structural Condition Assessment Based on the Gaussian Process: A Representative Data Selection and Performance Warning Strategy

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations.

State-Based Technical Condition Assessment and Prediction of Concrete Box Girder Bridges

The technical condition of bridges has become a crucial issue for organizing the maintenance and repairs in bridge management systems. It is of great practical engineering significance to construct an effective model for predicting the technical condition degradation of the bridge through the use of the historical inspection data. Based on the semi-Markov random process, this paper proposes a useful deterioration prediction model for bridges in the highway network. From the historical inspection data of the prefabricated concrete box girder bridges, the degradation curves of technical condition rating are obtained. The effect of bridge length on degradation rate of the prefabricated concrete box girder bridges is analyzed. According to the Weibull distribution parameters of different condition grades, the technical state degradation models for a bridge group and an individual bridge are proposed to predict the performance of the overall bridge and superstructure of the bridge. The results show that with the increase in bridge length, the degradation rate of bridge technical condition increases. The degradation rate of the technical condition of the superstructure is faster than that of the overall bridge. The proposed semi-Markov stochastic degradation model for the bridge group can not only predict the different condition ratings of the bridges at any time, but also predict the future deterioration trend of an individual bridge under any ratings.

Characterizing Bridge Thermal Response for Bridge Load Rating and Condition Assessment: A Parametric Study

Temperature is the main driver of bridge response. It is continuously applied and may have complex distributions across the bridge. Daily temperature loads force bridges to undergo deformations that are larger than or equal to peak-to-peak traffic loads. Bridge thermal response must therefore be accounted for when performing load rating and condition assessment. This study assesses the importance of characterizing bridge thermal response and separating it from traffic-induced response. Numerical replicas (i.e., fine element models) of a steel girder bridge are generated to validate the proposed methodology. Firstly, a variety of temperature distribution scenarios, such as those resulting from extreme weather conditions due to climate change, are modelled. Then, nominal traffic load scenarios are simulated, and bridge response is characterized. Finally, damage is modelled as a reduction in material stiffness due to corrosion. Bridge response to applied traffic load is different before and after the introduction of damage; however, it can only be correctly quantified when the bridge thermal response is accurately accounted for. The study emphasizes the importance of accounting for distributed temperature loads and characterizing bridge thermal response, which are important factors to consider both in bridge design and condition assessment.

Moving-Principal-Component-Analysis-Based Structural Damage Detection for Highway Bridges in Operational Environments.

With the deterioration of bridge performance and ever-increasing amounts of traffic, bridge safety is becoming a concern for the engineering community. A method that can assess a bridge's condition in real time is urgently needed. The main factors that hinder bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA)-based method is developed for structural damage detection in bridges in operational environments in this paper. Two main operational environmental factors, the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate in assessing the bridge's condition in operational environments.

Condition and structural assessment of a historic steel bridge according to the Italian guidelines for the evaluation of safety of existing structures

“Ponte dei Bersaglieri” is a historic bridge built in the early 1900s and rebuilt after the Second World War in San Donà di Piave, Venice, Italy. The bridge consists of four 53 m spans for a total length of 200 m. The spans are made of a steel lattice structure resting on concrete piles and abutments. At the request of the Managing Authority, a "Level 4" check and a serviceability check were planned in accordance with the Guidelines for Existing Bridges. The verification of such a bridge presents several difficulties related to the lack of basic information, as the documentation of the original project and the many interventions carried out over time were not available. Based on the scattered information collected, several surveys were organized to obtain certain information about the structure. Firstly, the geometry was measured globally by means of a point cloud produced by a drone survey, together with manual measurement of the thickness of individual members. A preliminary FEM model was then created, based on mechanical tests and inspections of existing materials. The aim of this phase was to determine the mechanical characteristics of both superstructures and substructures using destructive and non-destructive testing. An accelerometer data acquisition campaign was then planned to obtain the natural frequencies and mode shapes of the structure using the OMA method and to verify the compliance of the FEM model. At the end of the investigation campaigns, the FEM model can be finalized and it is possible to carry out level 4 verifications and serviceability checks.The inspection of the foundations in the riverbed will be conducted in the specific case with “multibeam technology to evaluate the erosion of the bottom of the riverbed and consequently the possible erosion of piers and abutments.This experience is representative as it describes some of the typical problems encountered during the verification of a historic steel bridge. The paper can be useful to other designers who are faced with similar situations, considering the large number of historic bridges currently present on Italian territory and the need to preserve them while ensuring their safety and functionality. What presented in the following has been developed within the scope of Level 4 assessments as outlined in the Guidelines for the classification and management of risk, safety assessment, and monitoring of existing bridges.

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.

Computer vision-based displacement identification and its application to bridge condition assessment under operational conditions

Computer vision-based displacement identification and its application to bridge condition assessment under operational conditions