Bridge Condition Research Articles

High‐speed drive‐by monitoring: field testing with an intercity express train (ICE)

AbstractThe structural condition of bridges in Germany is often affected by aging and increased traffic loads. However, retrofitting or renewing all bridges is not always feasible due to limited resources. Indirect or drive‐by monitoring of bridges with sensors on passing vehicles has been explored as a cost‐effective and scalable alternative to direct monitoring. In this study, we propose a novel High‐Speed Monitoring (HSM) method for determining natural frequencies, based on accelerometers mounted on train axles, developed for speeds under normal operating conditions. In combination with Bayesian optimisation, a resonance curve can be generated using only a few bridge passages, from which the dominant bending natural frequency of a bridge can be determined with an accuracy comparable to direct monitoring. Measurements on an ICE‐TD passaging bridges travelling at high speeds of up to 200 km/h have shown that the natural frequencies can be determined with the same accuracy as direct measurements. Thus, in this study we were able to show that the method is not only accurate, cost‐effective and scalable, but can also be applied at high speeds without affecting operations.

Sensory monitoring system of the post‐tensioned concrete bridge over the Odra River in Poland

AbstractThe paper presents the Structural Health Monitoring system installed on the posttensioned concrete road bridge over the Odra River in Poland. The bridge superstructure has a form of the 5‐span box girder with skewed supports, built using the cantilever method. During operation of the bridge (since 2010) many cracks arrived in the superstructure, mainly within the main bridge span with a length of 140 m. Therefore, as a part of a complex diagnostic process, the monitoring system were incorporated for assessment of the bridge performance and for observing further propagation of damage. The implemented monitoring system utilizes Fiber Bragg Grating (FBG) technique for measuring of strains, changes of cracks width and angular displacements of concrete box girder for various operating conditions of the structure including effects of regular road traffic, environmental influences and rheological phenomena. The paper presents applied testing and measuring procedures as well as results of the monitoring. Conclusions regarding effectiveness of the applied testing method in identification of the bridge condition are formulated and discussed.

Concept for a digital twin of railway bridges on the example of the new Filstal bridges

AbstractThe preservation of infrastructure is becoming increasingly important. The current reactive maintenance management only initiates maintenance measures after damage has occurred. Short‐term measures can lead to unforeseen restrictions in infrastructure availability and thus cause high social and economic damage.High‐quality measurement technology, automated data analysis, structured data management and collaborative work are just a few technological developments that form the cornerstones for a future predictive maintenance strategy. The digital twin, a digital representation of the real object, combines these developments and is a promising method for a sustainable preservation of infrastructure.Using the example of the Filstal bridges, a concept for a digital bridge twin is introduced. The digital twin combines measurement data, bridge information, inspection results as well as structural safety verifications and aggregates them to continuously display the current bridge condition and thus supports the maintenance of bridges. For intuitive use, all information is located and presented in a way that is consumable by the user.

Structural Condition Classification of Railway Bridge KW51 Before, During, and After Retrofitting

AbstractA linear classification algorithm for various structural states (before, during, and after retrofitting) of railway steel arch bridge KW51 based on Linear Discriminant Analysis (LDA) of principal components is proposed. The technique is typically employed in other fields, such as genetics, but its use in civil engineering is still limited. The method is applied in two steps, first, the original vibration measurements are projected to a lower dimensional subspace using Principal Component Analysis (PCA). The significant principal components are provided as inputs to LDA to constitute the newly transformed subspace. Joining PCA and LDA improves the performance aptitude of LDA when only first major principal components hold the main signature of the features. The acceleration datasets that represent bridge conditions under train passage on the steel arch railway bridge in Belgium, mentioned as bridge KW51 have been utilized for validation of the algorithm. The outcome of the visualization process is further analyzed using three clustering methods (kmeans, Fuzzy C means, and Gaussian Mixture Modelling). The confusion matrix is used to examine the performance of the clustering methods. The proposed algorithm provides promising results that outperformed other classification methods.

Numerical comparative study on seismic response of half-span and full-span scaled models of a floating type cable-stayed bridge

This study compares the results of numerical simulations of half-span and full-span cable-stayed bridge models based on the seismic response of the pylons and main girders subjected to earthquake loads. The half-span model represents the most critical condition of the bridge during the construction phase, while the full-span model represents the bridge in operational phase. The three-dimensional finite-element modeling of the two bridge models uses data from the longest existing cable-stayed bridge in Indonesia which was scaled down geometrically to 1/160, using Midas CIVIL software. The Kobe earthquake record from the PEER Strong Motion Database was used in this study by applying the spectral matching method to modify its acceleration according to the location of the existing bridge being analyzed. This study also examines the influence of the directional effects of earthquake load relative to the longitudinal axis of the bridge, namely 0, 45, and 90 degrees. The results of the analysis show that for the half-span bridge model, the maximum acceleration occurs at the top of the pylon due to earthquake excitation at 90 degrees direction, with an amplification factor of 4.658, while the amplification factor in full-span bridge conditions is only 2.44. In addition, in both the half-span and full-span bridge models, the largest acceleration amplification factor occurs in the main girder that is caused by earthquake excitation at 90 degrees direction. The largest transverse displacement occurs at the free ends of the main girder, but not in the full-span model. The results of the study indicate that during the detailed design process the designer has to consider the effect of earthquake on the strength and stability of the bridge during construction especially if the balanced cantilever method applies.

Penilaian Kondisi Jembatan di Kabupaten Sijunjung

A bridge is a construction structure whose role is to connect one place to another by crossing a natural or artificial obstacle. Bridge conditions can be caused by many factors such as the influence of weather, traffic loads, the environment, material durability, and so on. Good bridge conditions can be achieved if maintenance is carried out regularly. Therefore, assessing or checking the condition of the bridge is very necessary so that disturbances caused by the factors above can be avoided before an accident occurs. From the results of the field survey, it was found that 52.11% of bridges were in Good condition, 27.46% in Lightly Damaged Condition, 16.90% in Damaged Condition, 2.82% in Severely Damaged Condition and 0.70% in Critical Condition. . The bridge condition assessment was carried out for 2 weeks by carrying out inventory checks, detailed surveys and routine surveys on each bridge.

Improving visual question answering for bridge inspection by pre‐training with external data of image–text pairs

AbstractThis paper explores the application of visual question answering (VQA) in bridge inspection using recent advancements in multimodal artificial intelligence (AI) systems. VQA involves an AI model providing natural language answers to questions about the content of an input image. However, applying VQA to bridge inspection poses challenges due to the high cost of creating training data that requires expert knowledge. To address this, we propose leveraging existing bridge inspection reports, which already include image–text pairs, as external knowledge to enhance VQA performance. Our approach involves training the model on a large collection of image–text pairs, followed by fine‐tuning it on a limited amount of training data specifically designed for the VQA task. The results demonstrate a significant improvement in VQA accuracy using this approach. These findings highlight the potential of AI models for VQA as valuable tools for assessing the condition of bridges.

Development of Data-based Hierarchical Learning Model for Predicting Condition Rating of Bridge Members over Time

Bridge maintenance strategy is implemented to ensure that effective decisions are made based on the evaluation of the current performance and predicted future conditions of a bridge. The current bridge maintenance system relies on traditional manpower-based methods to determine bridge performance. However, the present techniques employ a material deterioration model that has uncertainty in predicting bridge performance. Moreover, the utilization of collected bridge inspection-related data is insignificant, and related research is lacking. Accordingly, a new type of bridge deterioration model is proposed using state performance data based on bridge inspection. To formulate the deterioration model, bridge condition inspection data accumulated over a long period were utilized. The data have been continuously updated since 1992 by the US Federal Highway Administration and consist of basic information and various influencing factors of bridges. The developed model uses the long short-term memory (LSTM) algorithm (a type of recurrent neural network) as well as layer normalization and label smoothing to improve the applicability of fundamental data. For the stable application of data, predicting the model performance for up to 30 years every 2 years is possible. By implementing the many-to-many type of the LSTM algorithm, the predicted probability of each grade derived for each sequence was weighted and averaged with the grade value to derive a continuous state grade result. Thus, the proposed model can express discrete historical degradation indices in continuous form according to the service life. In addition, it enables the prediction of bridge performance using only the fundamental information regarding new and existing bridges. For the effective use of basic data, an optimization model was derived using preprocessing and various regularization techniques. Additionally, a feed-forward deep learning model and stochastic model were developed using the same data. For performance assessment, a regression analysis evaluation method was applied, and comparative analysis was performed using the inspection data of an actual bridge. The use of a time-series model enabled the continuous and stable prediction of bridge performance.

Study on damage identification of High-Speed railway truss bridge based on statistical steady-state strain characteristic function

The localization and quantification of damage in bridge structures have always been a challenging problem, especially for high-speed railway truss bridges with intricate structural forms and complex load conditions. To address this issue, this paper proposes a novel damage indicator based on strain characteristic functions, and integrated it into an automated framework for real-time damage detection which is particularly suitable for in-service structural health monitoring applications. To evaluate the effectiveness of this approach, a study is conducted using a finite element digital twin model to simulate the structural responses at critical areas of high-speed railway truss bridges under train-induced loads. The proposed damage indicator of the model was used to investigate and evaluate different types of bridge damage, reflecting both the location and severity of damage in high-speed railway truss bridges. The results suggest that the proposed damage indicator, when used with a distributed sensor system, shows promise in accurately locating and measuring damage in high-speed railway truss bridges. In addition, for the bridge health monitoring system with sparse sensor distribution, this indicator can provide a probability assessment of various damage conditions of a high-speed railway truss bridge. This information could potentially assist in the process of conducting in-service maintenance of a high-speed railway truss bridge.

Eye in the Sky: 360° Inspection of Bridge Infrastructure Using Uncrewed Aerial Vehicles (UAVs)

Bridges play a key role in supporting the transportation network in the United States. The 2021 infrastructure report card prepared by ASCE highlighted that more than 40% of bridges in the U.S. are over 50 years old. Some of these bridges are classified as structurally deficient, even though they are safe to travel. To address these challenges, highway agencies are exploring innovative technologies to conduct inspections and realize benefits in relation to access, cost, and safety. Federal and state DOTs have conducted several studies on the application of uncrewed aerial vehicles (UAVs) for bridge health monitoring. This study identified the existing knowledge gap in performing 360° inspection of bridges. In this current research, UAVs were demonstrated for conducting 360° inspections of three different bridges in Alaska. The locations of the aerial images during the inspections were also pictographically represented to provide a holistic idea for the highway agencies and practitioners. Three-dimensional models representing the actual conditions of the bridge were generated and used for comparing the bridge condition assessments with traditional inspection reports. Infrared imagery was also collected to identify the effect of thermal loading in assessing the conditions of the bridge elements. The applicability and recommendation scale for the use of UAVs for different bridge inspections was provided. The approach demonstrated in this study is expected to result in more than 90% savings in storage requirements and contribute to an increase in the applications of UAVs for conducting 360° bridge inspections across the U.S.

EXPLORATORY STUDY OF CLIMATE CONDITIONS ON BRIDGE USEFUL LIFE

An accurate and reliable assessment of bridge conditions can help public and private entities better plan for bridge maintenance, repair, and reconstruction. Because the performance of bridges over their useful lives depends on multiple aspects, it is essential to investigate the factors affecting their conditions. Literature has shown that aging, climatic conditions, increased traffic demand, and delayed maintenance lead to deterioration that eventually affects the usability of the bridge. Among those factors, climatic conditions such as temperature, humidity, and rainfall negatively affect the durability and service life of the bridge. The objective of this paper is to explore the influence of climatic conditions on the performance ratings of the bridge deck, substructure and superstructures. National Bridge Inventory (NBI) data from states of Illinois and Florida in 2022, representing two different climate zones in the United States, will be used to analyze the effects of climate conditions. This study will show the quantitative impact of climate on the bridge conditions and contribute to our understanding of the relationship between environmental factors and bridge deterioration.

ASSESSMENT OF THE INTEGRITY OF STEEL RAILROAD BRIDGES RENEWED WITH POLYMER CEMENT AFTER 10 YEARS

Renewal of a steel railroad bridge using polymer cement and fast-strength lightweight concrete has been proposed. This is referred to as the renewal of an existing steel railroad bridge with composite construction. This enabled a reduction in stress amplitude and a longer service life of more than 50 years. At the same time, a reduction of about 10 decibels in noise and vibration was achieved, improving environmental compatibility. This renewed structure was actually applied to a steel railroad bridge in Japan 10 years ago. This steel railroad bridge is the Kawashima overpass of Sagami Railway in Yokohama. After the renewal construction, the bridge has continued to be used under heavy traffic in Japan and is still in service 10 years later. This report describes the condition of this steel railroad bridge after 10 years and investigates the durability of this renewed structure. This report proves that this renewal structure is effective.

Data-Driven Bridge Weigh-in-Motion

Heavy vehicle detection in the road system is now an urgent issue from the perspectives of law enforcement and road health monitoring. Weigh-in-motion (WIM) is a technology which estimates vehicle weights without stopping the vehicles. Paved WIM (PWIM) is expensive and has limited installation locations. Bridge WIM (BWIM), which utilizes bridge components as weight scales, is quite inexpensive and easier to install. BWIM requires the dynamic characteristics of the bridge and traffic conditions for accurate weight estimation. In general, such characteristics are measured by several experimental runs using a vehicle with known axle weights. The weighing accuracy may be greatly degraded owing to the influence of the other traveling vehicles. In this paper, we propose a data-driven BWIM using a neural network. The model parameters are optimized automatically by video analysis and vehicle identification between WIMs. The model can estimate vehicle weights accurately considering various traffic conditions that may degrade the weighing accuracy.

A Cross-Diagnosis Method for Determining the Structural Condition of a Long-Span Suspension Bridge Based on the Spatial Windows of Distributed Strain Data

Using structural health monitoring (SHM) techniques, Brillouin optical time-domain analysis (BOTDA) sensors can be mounted along the main box girder entire length of a long-span suspension bridge, and the high-density measured points strain monitoring data can be obtained. However, insufficient research has been conducted on accurately diagnosing the structural condition of a long-span suspension bridge by using the abovementioned strain monitoring data. To address this issue, a cross-diagnosis method that determines the structural condition of long-span suspension bridges based on the distributed strain data spatial window is proposed in this study. First, the distributed strain data spatial window based on a long-span suspension bridge structural symmetry is defined. Then, a method that divides the distributed strain data of the bridge main box girder into different spatial windows using mutual information between the strain data from BOTDA sensors is presented. The special symmetry of the environmental temperature effect on the spatial window structural performance is carried out to separate the temperature effect from the strain monitoring data; this process can effectively reduce the interference of ambient temperature on the results of the structural condition diagnosis. Second, using a convolutional neural network, a diagnosis index of the structural condition is generated by using the correlation model between the high-density measured points and the distributed strain data belonging to one whole spatial window. Regarding one spatial window, the proposed diagnosis index can effectively reflect the variation in the distributed strain correlation model caused by the damaged condition of the long-span suspension bridge to achieve cross-diagnosis of the structural condition of the bridge. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation using strain monitoring data obtained from a real bridge.

Atmospheric Corrosion of Steel Infrastructure in Canada Under Climate Change

Protection against atmospheric corrosion of steel infrastructure and its components requires assessment and prediction of corrosion rates using quantitative and reliable modeling of the environmental parameters that impact the corrosion process. This has become even more critical in the context of climate change. This study demonstrates the feasibility of using ISO dose-response functions (DRFs) to classify atmospheric corrosiveness in Canada to improve the current qualitative approach to the corrosion design of carbon steel infrastructure, especially bridges. The DRF predictions for first-year corrosion rates were first validated using a set of historical field data collected from different locations across Canada. Further examination has revealed a dramatic change in atmospheric corrosiveness at many locations in Canada since the last systematic effort in field data collection in the 1950s to 1960s. It is believed that this was caused by a significant drop in in sulfur dioxide levels in the atmosphere. A projection of future corrosiveness in various Canadian cities with differing climates was also presented by considering the projected impacts of climate change on the temperature and relative humidity. This study has shown a promising precursor for the development of a corrosiveness map in Canada to better guide the corrosion design and protection of steel infrastructure. Furthermore, this study has demonstrated an acute need to understand the current chloride deposition conditions of steel bridges, including the increased local exposure of roadways to corrosiveness by the use of de-icing salts.

Research on dynamic detection method of bridge vehicle load based on optimization algorithm

Abstract In recent years, accidents causing casualties and losses to people and property have occurred frequently throughout the country due to bridge safety problems, causing huge losses to the country and people’s property. To prevent the recurrence of such incidents and effectively maintain the safety and interests of the people, this paper intends to use the optimization algorithm as the technical support to study the optimal method for dynamic detection of vehicle loads on bridges. The gradient descent method is used to obtain the deflection influence line of the bridge dynamic test structure and the overall vibration frequency of the structure. The bridge test area is then selected by the stochastic gradient descent method, and the measured strain and deflection influence lines are used to modify the initial finite element model of the bridge so that the finite element model can truly reflect the actual mechanical behavior of the bridge structure. According to the survey, as of the end of 2019, there were 878,300 highway bridges with a total mileage of 6063.46 m, which was 26,800 more than in 2018, and the mileage increased by 4,948,600 m. Among them, mega bridges accounted for 5716 bridges with a mileage of 10,323,300 m, and bridges accounted for 108,344 bridges with a mileage of 29,237,500 m. Many bridges in service provide convenience for people’s life and travel but also bring great challenges to bridge management and maintenance, bridge appraisal, and assessment. Monitoring the bridge load condition helps to grasp the actual working condition of bridges in a timely and accurate manner and to warn and repair the safety hazards that appear on bridges in time to prevent accidents.

Improved Method for Estimating Construction Year of Road Bridges by Analyzing Landsat Normalized Difference Water Index 2

The construction year of road bridges plays an important role in bridge management systems. Based on the age of road bridges and other factors, deterministic and probabilistic deterioration models can be used to calculate deterioration rates and predict the future physical condition of road bridges. Two new techniques are proposed in this manuscript for estimating the construction year of road bridges by analyzing the normalized difference water index 2 (NDWI_2). Technique 1 uses both the target bridge point (TBP) and a selected optimal reference control point, while Technique 2 uses only the TBP. Landsat 5 Thematic Mapper NDWI_2 data were analyzed at all 44 road bridges in Nago City, Japan of the bridges’ overall length ≤ 100 m and construction year between 1990 and 2006. The sequential t-test analysis of the regime shift method, at a significance level α = 0.05 and cutoff length l = 2 to l = 27, was used to interpret the estimated construction year from the NDWI_2 for both techniques. Both techniques successfully determined the estimated construction year, which was statistically significant with p-values < 0.05, except for seven road bridges in Technique 1 and one road bridge in Technique 2. The correlation and comparative analysis of the actual and estimated construction years yielded R2 = 0.24 and R2 = 0.33, as well as an average deviation of S = 5.81 years and S = 4.08 years for Technique 1 and Technique 2, respectively. The findings suggest that Technique 2 is more accurate and provides a better estimate than Technique 1. It was observed that, as the cutoff length l increased, the absolute error between the actual and estimated construction year increased. Therefore, as a measure of accuracy, the upper limit of cutoff length l was set to l≤ 12. It was also observed that the increase in the bridge’s overall length and forested area contributed to the accuracy of the results. By using the construction year as one of the inputs into bridge management systems, bridge managers can make more informed decisions about how best to maintain and improve road bridges to ensure user safety and road bridge preservation for the future.

Using Conceptual Graph modeling and inference to support the assessment and monitoring of bridge structural health

Effective bridge maintenance requires sufficient and accurate knowledge on structural health. However, despite the development of structural health monitoring (SHM) and inspection aids, bridge structural health monitoring remains challenging. This work proposes a comprehensive predictive description based on Conceptual Graphs (CG), of the bridge condition deterioration mechanism, with the formalization of the inspection history. The model combines formalized​ assumptions with several deterioration factors and expert knowledge to assess the evolution of the structural condition. The model is based on logical and graphical descriptions of the deterioration of the structural condition thanks to the use of the CG modeling. Through the graphical inference, the evolution of the apparent condition and the deterioration time can be assessed. The application to two bridge cases highlights the interest of this approach to help understanding the sequence of deterioration. The actual condition obtained and the formalized inspection history allow a better decision.

A temperature-driven approach for quantitative assessment of strengthening effect of continuous bridges using structural health monitoring data

The stiffness degeneration of small to medium span bridges has been increasingly observed in recent years, and it has become a major concern of government and bridge owners. A quantitative evaluation method for the bridge performance with strengthening measures is highly desired. Due to the advantages of uninterrupted traffic and long-term tracking capability, a temperature-driven approach for characterization of the correlation pattern between bridge temperature-induced strains and bridge status was proposed in the present study by using structural health monitoring data. First, a theoretical solution of the simplified bridge model was derived to establish the correlation between the stress and deterioration extent under temperature gradient load. After a numerical simulation that combines the thermal–structural interaction analysis and the vehicle–bridge interaction analysis, the strain range was proposed as an assessment index to ensure the stability and effectiveness of the evaluation results. Next, the Generalized Extreme Studentized Deviate method was used for detecting the outliers. The statistical results of the assessment index for different strengthening methods were compared to evaluate the associated strengthening efficiency, and the associated equivalent section height was calculated for visualizing the bridge condition after strengthening measures were taken. The results demonstrated that the proposed temperature-driven method was able to quantitatively evaluate the bridge strengthening effects with a high efficiency.

Clinical signs of the quality level of fixed dentures in the process of use and rationale for feasibility of their replacement

The growing requirements for the quality of dental care necessitates expert evaluation of dental prostheses. Therefore, on the way to improve the structure and process of providing dental care, it is important to analyze the primary data of its results in quantitative and qualitative terms. The purpose of this work is to clarify the content and results of consulting care by analyzing clinically important indicators of the quality of fixed dentures that determine their applicability for further use. Among the 410 patients aged 35-81 examined for damage to abutment teeth and periodontal tissues, unsatisfactory condition of crowns and bridges, the 1st subgroup included 76 (18.5%) persons who sought counseling on their own initiative and the 2nd subgroup – 334 (81.5%) persons with referrals from other medical institutions. Quality indicators of 759 artificial crowns and 803 bridges were determined (K08.5 – unsatisfactory restoration of teeth) in the early (up to 24 months) and remote (more than 24 months of use) periods of observation using a modified index of clinical and technological quality. In patients of both groups in the early observation period 67 crowns and 101 bridges (all signs – 253), and in the remote period – 692 crowns and 702 bridges (3804 signs) of low quality were revealed. Quality index of these structures in the early and remote periods of observation was 0,71 versus 0,36 (p<0.05) and 0.64 versus 0.37 respectively (p<0.05). In 71 (93.4%) patients of the 1st group grounds for replacement of 292 out of 327 (89.3%), and in 238 (71.3%) patients of the 2nd group – 827 (67.0%) out of 1235 fixed partial dentures were found. Thus, expert assessment of the quality of dental care is an integral part of ensuring the predicted results of prosthetic treatment. The proposed method of assessing the clinical and technological quality of fixed dentures makes it possible to determine their suitability or unsuitability for further use.