Structural Condition Of Bridges Research Articles

К оценке техникo-эксплуaтaциoннoгo сoстoяния гoрoдских железoбетoнных мoстoв и путепрoвoдoв

To consider the issue of assessing the technical and operational condition of urban reinforced concrete bridges and overpasses. Study technical passports of about 300 reinforced concrete bridge structures in Tashkent. Analyze in detail and formulate the main technical and operational indicators (TEI) as criteria for assessing the technical condition of bridge structures in Tashkent. Methods: methods for assessing the technical condition of the city’s bridge park objects have been determined — absolute and relative. For this purpose their purposes are indicated. Results: the statistics were compiled on the basis of the bridge park of the city of Tashkent, carried out by the authors of a survey of 30 city bridge structures. Based on the results of the examination of selected bridge structures, it was established that the main damage to the spans and supports of reinforced concrete bridges and overpasses is caused, first of all, by the poor condition of waterproofing, expansion joints and lack of water water pipes. The main technical and operational indicators (TEI) are formulated as criteria for assessing the technical condition of bridge structures in Tashkent, such as “safety and traffic comfort”, “durability”, “load capacity”, “throughput”, and “maintainability”. Practical importance: taking into account the specifics of a large metropolis, managing the technical condition of bridge structures in Tashkent is an extremely responsible and complex task. For use in practical operation of reinforced concrete bridge structures as the most widespread objects of the city’s road transport infrastructure, a statistical analysis was performed. From a practical point of view, this information will make it possible with a sufficient degree of probability to ensure and maintain the specified regulatory (design) levels of reliability, safety and durability of bridge structures.

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.

Methodology for Assessing the Technical Condition and Durability of Bridge Structures

The proposed methodology aims to determine and forecast the technical condition of bridge elements, which could serve as an advanced engineering tool for assessing reliability and durability. It is developed based on fundamental studies that synthesize the experience of studying the physical–mechanical and physical–chemical properties of materials in bridge structures operating under real conditions. The theoretical foundation of the methodology is a reliability model and residual lifetime prediction of bridge elements based on Markov’s theory. The developed methodology is designed for assessing the technical condition of individual bridge elements, followed by a comprehensive evaluation of the entire structure. Reliability during operation is adopted as the indicator of technical condition. This quantitative reliability indicator in the model serves as a criterion for evaluating the safety level of bridge elements; ranking of bridge elements as necessary for specific types of repair, reconstruction, or replacement; strategic planning of expenditures for repair or reconstruction under limited funding; and forecasting the remaining resource of elements. An evaluation and prediction algorithm for the technical condition of bridges is proposed for the application of the developed methodology. A mathematical experiment of the developed methodology was conducted, which confirmed the adequacy of the proposed hypothesis, i.e., the use of the reliability model and the prediction of residual lifetime of bridge elements. First, a three-step mechanism for refining the technical condition of the bridge is proposed, significantly enhancing the accuracy of the calculations. Therefore, the developed methodology holds practical value and can serve as a basis for information-analytical systems for managing the condition of bridges.

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges.

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.

Drive‐by frequencies extraction by means of synchrosqueezed wavelet transform

AbstractBridges and viaducts are facing growing traffic, increasing loads as well as ageing and deterioration. Nowadays, vibration‐based Structural Health Monitoring (SHM) represents an effective solution to extract quantitative information regarding bridge structural condition. Bridge frequencies are fundamental parameters capable to reflect and summarize bridge dynamics and health condition. Typically, their identification is performed by direct approaches, which rely on the use of sensors installed on the structure under analysis. A cost‐efficient, movable, and flexible alternative is offered by drive‐by methods, consisting in the use of instrumented vehicles to extract bridge frequencies. This paper presents an experimental campaign carried out on a multiple‐span mixed railroad bridge, with the aim to identify its frequencies through onboard measurements. A set of MEMS wireless accelerometers is deployed on the vehicle, firstly allowing for vehicle pitch component mitigation. Vehicle accelerations were then used to extract bridge frequencies, by means of a Most Probable Modal Frequencies approach, relying on synchrosqueezed wavelet transform. At a cruise speed of 30 km/h promising results in terms of indirect frequency estimation were found, with no traffic disruption.

Validation of the digital inspection of bridges

AbstractThe paper presents the main results of the development project “Digitalization of monitoring the condition of bridge structures on highways and expressways in the Republic of Slovenia”. Within the project, test cases of the digitalized inspection method were carried out on three different concrete bridges. To enable validation of digitalization, a traditional and digital inspections for all three bridges have been conducted, focusing on the three important types of reinforced concrete bridges damages: delamination, cracks and reinforcement corrosion. The comparison of the results has shown that digital inspection can already strongly support bridge inspections, especially during detailed inspections, as it can, to some extent, completely replace the time‐consuming damage mapping. In addition, the analyses show that, already today, digital inspection is often economically justifiable.

Decision Support Model for Allocating Maintenance Budgets for Bridges

This paper introduces a newly developed decision support model for allocating a budget for bridge maintenance. The model is built based on capturing the experts’ knowledge for the prioritization of criteria for selecting a bridge maintenance plan. A literature review and interviews with a group of nine local experts were carried out to identify the criteria for bridge prioritization for maintenance. A questionnaire survey was then designed and administered, utilizing multi-criteria decision-making techniques through face-to-face meetings with local bridge maintenance managers to determine the weights of the identified criteria and develop the utility curves for each criterion. The developed model consists of four major modules: (1) bridge ranking module; (2) prioritization module of bridge structural elements; (3) optimization-based simulation module; and (4) the outputs module. The model was validated in a hypothetical network of four bridges. The validation reveals that the model can assist bridge maintenance managers in setting a bridge maintenance plan, with a high level of confidence using efficient frontier analysis technique. Utilizing frontier analysis enables comparisons of mean costs of different bridge maintenance plans against different levels of risk to enable decision-makers to make informed decisions. The result of this study indicated that bridge structural condition was the most important criterion amongst all the criteria influencing the decision of bridge prioritization for maintenance, followed by traffic and bridge location. Moreover, bridge deck and parapet, followed by bearing pads, were found to be the most important of the bridge’s structural elements.

Information fusion between bridge management system and structure health monitoring system

Through the application status investigation of the bridge management system and the structural healthmonitoring system, it was found that the two systems were not fused well, which resulted in resources waste and inaccurate outcomes. Based on the research of status and application of the two systems, the paper analyzed the necessity and feasibility of the information fusion of the two systems. Finally, the specifie information fusion of the two systems is presented through the exact information fusion of the systems in the bridge structural condition assessment, one of the most important modules of the bridge management system. More accurate results of the condition assessment and remaining life prediction of bridges can be obtained by the bridge management system after the fusion, which will offer some evidence for the bridge maintenance strategy made by bridge management decision- -makers.

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.

Techniques for Processing Experimental Data for Structural Health Monitoring of Bridges

Reliability of transport artificial structures and transit of trains at sanctioned speed should be provided with the necessary and sufficient load-bearing capacity, strength, rigidity, and stability of engineering structures.The objective of this work was to substantiate the possibility of using well-known methods for controlling the stress-strain state of structures using automated systems of structural health monitoring of bridge spans.It is extremely important regarding operation of transport artificial structures designed according to the standards of the first half of the 20th century.Under these conditions, the experimental determination of the stress-strain state of bearing structures of bridges becomes the most important component of the task of a comprehensive assessment of physical wear and tear as well as of operational reliability of the structures. Monitoring the structural health and technical condition of bridges and planning of timely measures aimed at the repair, strengthening or reconstruction of spans will extend their service life and ensure safety during operation.Maximum permissible deflections of spans under a movable temporary vertical load have been revealed since to ensure smooth movement of vehicles it is necessary to control horizontal longitudinal and transverse displacements of the top of the bridge piers, as well as vertical settlements.The paper suggests methods of interpreting data measured by inclinometers and electric strain gauges, tensiometers to use them in an automated system for monitoring the structural health of railway bridges. The method of strain measurement is described in detail in the proposed options for installing resistance strain gauges on structures to measure tensile-compression stresses and longitudinal forces due to a temporary vertical load.Monitoring the technical condition of bridge structures, using the methods for measuring deflections and deformations proposed by the authors in this article, will make it possible to assess the change in bearing capacity of the structure over the entire period of operation. The study used regulations and experience of the Russian Federation and the Republic of Kazakhstan.

Problems of the system for bridge structures management

Problem. During operation, the bridge structure, made of reinforced concrete structures, is exposed to natural and climatic impacts, permanent and temporary load, chemicals and heavy traffic without virtually supervision of the structures. As a result, damage and defects appear in the elements of the bridge structure, the material of the structures undergoes structural changes, the damage spreads with different intensity and their degradation can cause destruction. In the process of operation, the building ages, loses its original capabilities, quality and consumer properties, so more and more costs are needed to restore and maintain its physical condition. Thus, the process of preservation comes to the fore, and the durability and reliability of structures depend on the effectiveness of actions for the preservation of bridge structures and the effectiveness of management for their operation and repair. Therefore, the relevance is not in doubt. Goal. The purpose of the work is to conduct a brief analysis of existing control systems for bridge structures. It is shown that the system of management and operation of bridge structures, implemented in Ukraine, has shortcomings, one of which is the unfinished issue of organizing the management process with the priority of current repairs, which allows timely recording of dangerous damage and prevents further deterioration of operational condition and the lack of specialized organizations.At the same time, any system for managing the condition of bridge structures is aimed at extending the service life of the structure (declared before reconstruction) and increasing the functionality of structures with prolonging the period of operation before overhaul. This is due to the preservation of individual structural elements or the current repair of pavement at the end of the second and during the third operating conditions with a rational use of resources.Results. It is proposed to improve the management system of bridge structures on the basis of prolongation of defect-free professional operation and preservation of structural solutions of the structure. Practical value. In the process of repairing bridge structures, technical or scientific support is provided to ensure the quality of work and the creation of specialized organizations during operation.

Numerical Investigation of the Characteristics of the Dynamic Load Allowance in a Concrete-Filled Steel Tube Arch Bridge Subjected to Moving Vehicles

Dynamic load allowance (DLA) is a key factor for evaluating the structural condition of bridges; however, insufficient research has been performed regarding the characteristics of DLA in concrete-filled steel tube (CFST) arch bridges. To address this issue, based on an actual CFST arch bridge, the DLA characteristics of bridges are investigated numerically in this study. First, aiming at different structural components, such as the arch rib, main girder, and suspenders, the DLA values obtained at various locations of different structural components are compared in detail, and then the changing regulations of the DLA, considering the influence of different vehicle speeds and various extents of pavement roughness, are summarized and analyzed. Additionally, the relationship between the different DLAs obtained by using the different response indices, that is, displacement, bending moment, and axis force of structure, is investigated. Finally, some conclusions that are significantly beneficial for evaluating or detecting the condition of CFST arch bridges are drawn.

Automated wireless monitoring system for cable tension forces using deep learning

As demand for long-span bridges is increasing worldwide, effective maintenance has become a critical issue to maintain their structural integrity and prolong their lifetime. Given that a stay-cable is the principal load-carrying component in cable-stayed bridges, monitoring tension forces in stay-cables provides critical data regarding the structural condition of bridges. Indeed, various methodologies have been proposed to measure cable tension forces, including the magneto-elastic effect-based sensor technique, direct measurement using load cells, and indirect tension estimation based on cable vibration. In particular, vibration-based tension estimation has been widely applied to systems for tension monitoring and is known as a cost-effective approach. However, full automation under different cable tension forces has not been reported in the literature thus far. This study proposes an automated cable tension monitoring system using deep learning and wireless smart sensors that enables tension forces to be estimated. A fully automated peak-picking algorithm tailored to cable vibration is developed using a region-based convolution neural network to apply the vibration-based tension estimation method to automated cable tension monitoring. The developed system features embedded processing on wireless smart sensors, which includes data acquisition, power spectral density calculation, peak-picking, post-processing for peak-selection, and tension estimation. A series of laboratory and field tests are conducted on a cable to validate the performance of the proposed automated monitoring system.

Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

Results of the checking calculation of the structures of pedestrian bridge over the military descent in Odessa

Problem. The highest pedestrian bridge in Odessa is stretched over the Military Descent between Primorsky Boulevard and M. Zhvanetsky Boulevard in Odessa. During the whole life of pedestrian bridge, the research and testing [3] of bridge structure in 1969, and inspection [6] in 2000 and 2005 were carried out. Unfortunately, not all recommendations of the work [6] have been implemented. This fact doesn’t contribute to the satisfactory operation of pedestrian bridge structure, especially near the shore anchor No. 0. The above-mentioned recommendations included: vertical planning of the slope and the area under the bridge deflection near the shore anchors; fixing a block-divided limestone-shell rock massif; fixing the retaining walls, which support the slope, in the area of inclined supports foundations and near the shore anchors No. 0 and No. 3. The technical condition of the bridge is generally classified as partially serviceable (state 4) according to standard [9]. According to inspections [3], the operating conditions of the bridge structure and the surrounding area are extremely unsatisfactory. Goal. The goal of the work is estimating the technical condition of pedestrian bridge structure over the Military Descent between Primorsky Boulevard and M. Zhvanetsky Boulevard in Odessa, to ensure the further reliable operation of the pedestrian bridge and development of the project of high-priority measures for bridge major overhaul. Methodology. On the basis of provided materials, a three-dimensional computer model of pedestrian bridge was developed. Computer model calculations were performed using the LIRA software package. The design scheme of the bridge is adopted in the form of a spatial system consisting of shell elements that simulate the behavior of steel sheet structures, as well as bar elements that simulate the behavior of struts on shore anchors, anchor bars and auxiliary elements in modeling the hinged support of inclined racks. Results. Analysis of the results of steel structures calculation showed that: the maximum load on foundation pier 1a from the design load combination (DLC) No. 6 is 289.1 t; the first and third oscillation modes are translational vertical oscillations, the second oscillation mode is horizontal oscillations; the maximum vertical deflection in the central span from DLC No. 16 is 193 mm, which exceeds the limit deflection of 130 mm; the vertical deflection in the end spans doesn’t exceed the limit value, which is 105 mm; the maximum horizontal deformation on the shore anchor No. 0 is 51.8 mm, and on the anchor No. 3 it is 23.6 mm; the natural oscillation periods of unloaded bridge are: for the 1st mode (vertical) – 0.892 s, for the 2nd mode (horizontal) – 0.576 s. They don’t fall into the forbidden range [10] – from 0.45 to 0.60 s in vertical plane and from 0.9 to 1.2 s in horizontal plane; the main stresses in the steel bridge structures from DLC No. 2, 3, 4, 6, 7, 9, 10, 12, 13, 15, 16, 28, 31 exceed the yield strength of steel. At the same time, the places of stress concentration and their distribution pattern are identical for all DLC, except DLC No. 3, where an excess of the yield strength of steel occurs in the lower zones of inclined racks and in the support tables of cantilever beams; stress concentrators in computational model, to some extent, can be caused by peculiarities of mathematical algorithm of finite element method, and assumptions that were adopted in computational model. Practical value. Making final decisions about strengthening the bridge structural elements in places of stress concentration, it is necessary to perform a duplicate calculation in another software package; if the result is confirmed, it is necessary to strengthen the bridge elements according to the specially developed project and consider reducing the load (mobile and pedestrian); to reduce the temperature load on the steel bridge structures, it is urgent to overhaul the moving rollers of shore anchors No. 0 and No. 3 in accordance with the project [4]; engineering and geodetic observations should be continued for vertical displacements of bridge elements and horizontal displacements of pedestrian part of the bridge.

Structural response of a concrete cable-stayed bridge under thermal loads

Daily and seasonal temperature variations have a significant influence on the structural response of bridges, inducing strains, displacements or rotations of the same order of magnitude, or even larger, than those due to dead or live loads. Besides understanding the structural behaviour under the operational loads, the characterization of the structural response induced by the daily and seasonal temperature variations is mandatory for the critical assessment of the bridge structural condition and when proactive conservation is envisaged. In this study, a methodology is proposed for the simulation of the structural response of large concrete bridges under the effects of realistic temperature variations, aiming at the optimum compromise between accuracy and simplicity of the involved procedures. The transient temperature field in a set of representative cross-sections is obtained from the available meteorological data via two-dimensional thermal analyses. The temperature field is decomposed into uniform, linear and non-linear components, the former two being introduced in a mechanical model of the bridge to obtain the transient structural response. The methodology is applied to a concrete cable-stayed bridge equipped with a permanent structural monitoring system. The measured and calculated hourly temperatures, deflections, bearing displacements, rotations and stay-cable forces are compared during a period of 17 months and good agreement is generally found. The consideration of the radiative cooling effects is demonstrated to be essential in other to obtain a good estimation of the thermal field of the bridge. The behaviour of the bridge is discussed and the relative contribution of each temperature component to a given structural response is disclosed. A discussion on the optimal deployment location of a minimum set of embedded temperature sensors in order achieve the best estimators of the temperature components (uniform and linear) is also presented.

Thermal strain extraction methodologies for bridge structural condition assessment

This paper presents a feature extraction method to uncover the temperature effects on bridge responses, which combines mode decomposition, data reduction, and blind separation. For mode decomposition, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) have been executed, followed by principal component analysis (PCA) for data size compression. Independent component analysis (ICA) is then employed for blind separation. The unique feature of the proposed method is the blind separation, which enables temperature-induced response to be extracted from the mixed structural responses without any prior information of the loading conditions and structural physical models. This study further evaluates the effects of extracting a temperature-induced response on damage detectability when using Moving Principal Component Analysis (MPCA). Numerical analysis for a truss bridge model is first conducted to evaluate the proposed method for thermal feature extraction, followed by a real truss bridge test in the structural laboratory at the University of Warwick. Results from the numerical study show that the method enables the separation of a temperature-induced response; and furthermore, the EEMD, utilized in mode decomposition, has a positive influence on the blind separation compared with EMD when combined with PCA and ICA. Finally, the real truss bridge test demonstrates that the feature extraction method can enhance the probability of MPCA to uncover the damage as the MPCA fails to discern damage without the proposed method.

Nalysis of Changes to Steel Footbridge Geometry Within 25 Years in Service

Abstract Identification of the technical condition of bridge structures should be supplemented by determination of structural displacements in relation to the adopted frame of reference and measurement of changes to the geometry of its individual elements. Of special value are the results of measurements of the same values obtained along an extended service life. They provide an opportunity to compare results, track down tendencies, and more quickly recognise signs of aggravating technical condition of structures. The paper presents an analysis of changes to the geometry of a bridge structure – a cable– stayed footbridge used for pedestrian and bicycle traffic across the Brda river in Bydgoszcz. Measurements were made by means of surveying techniques within a period of 25 years of the structure service life. The collected data enabled numerous comparative analyses to be conducted. Displacements of footbridge structural elements observed across time led to formulation of recommendations for further use of the footbridge. For instance, recommendations for footbridge adjustment, including improvement of its geometry, were provided. In addition, a general analysis of the effect of footbridge displacements on its static operation was conducted.

Bridge Damage Detection Based on Vibration Data: Past and New Developments

Overtime, the structural condition of bridges tends to decline due to a number of degradation processes, such as creep, corrosion and cyclic loading, among others. Considerable research has been conducted over the years to assess and monitor the rate of such degradation with the aim of reducing structural uncertainty. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters and subsequently comparing them to numerical models. These traditional techniques are generally time consuming and can often mistake changing environmental and operational conditions as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data, but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls. A case study of the S101 Bridge is also presented to test the damage sensitivity a chosen methodology.