Bridge Condition Research Articles

LSTM approach for condition assessment of suspension bridges based on time-series deflection and temperature data

Deflection data provides important information about the mechanical characteristics and structural health condition of bridges. The study presented here pertains to development of a deep learning based approach for structural health monitoring by employing the bridge deflections. The method presented herein uses the long short-term memory (LSTM) framework in detecting the state of damage by tracking the feature changes of time-series deflection and temperature data. Deflection and temperature data of Chongqing Egongyan Rail Transit Suspension Bridge was employed over a period of 15 months to develop the proposed method. The concept of square error index (SE) is introduced as an assessment tool for estimation of the bridge damage level. Results from the present study indicated that the statistical characteristics of SE index are proportional to the level of damage, and are only sensitive to abnormal changes in deflection. Structural health monitoring data over the period of 15 months indicated that the proposed approach has the capability to detect cable damages as low as 0.5%.

Vehicle parameter identification based on vehicle frequency response function

Accurate vehicle parameter information plays an important role in assessing the conditions of roads and bridges, along with the corresponding maintenance. This study considered a vehicle parameter identification method based on a vehicle frequency response function (FRF). First, the vehicle FRF was deduced with respect to the displacements of the vehicle-road contact points, thereby building the relationships among the FRF, vehicle responses, and road profile in the frequency domain. Next, using the responses of vehicles passing over on-road bumps of known size, a direct estimation of the vehicle FRF was described. Then, a combination of Tikhonov regularization and a shape function method was used to update the estimated vehicle FRF by removing the singular data owing to the direct computation of the vehicle FRF. Subsequently, the modifying factors of the vehicle parameters were iteratively identified based on a sensitivity analysis of the estimated FRF to the vehicle parameters. A numerical simulation for vehicle parameter identification was performed to test the effectiveness of the proposed methods, considering a 5% Gaussian noise pollution and the influences of different driving speeds. At last, field tests of a vehicle passing over bumps were performed for the verification of vehicle parameter identification.

Bridge Frequency Identification Using Multiple Sensor Responses of an Ordinary Vehicle

This paper presents a novel strategy for indirectly identifying the natural frequency of a bridge via multiple sensors on an ordinary vehicle. It does not require specifically designed cars or multiple cars as the excitation sources, as many earlier studies have used, but only one normal vehicle, providing practicability. A signal processing method incorporating cross-spectrum estimation can extract the bridge frequency among complex and varied frequency components in the vehicle spectrogram. The bridge natural frequency as a common vibration component among different sensor responses is identified as a clear peak that occurs in the cross-spectrum. The methodology is validated by an experimental study using a steel beam and a scale truck model with an engine to construct the vehicle–bridge interaction system. With this approach, the first mode frequency of the bridge is successfully identified under various bridge conditions. In addition, the performance of the proposed strategy in bridges with and without road roughness is discussed, and the influence of vehicle weight on the success rates of frequency identification is also confirmed.

Long‐term bridge performance assessment using clustering and Bayesian linear regression for vehicle load and strain mapping model

The weigh-in-motion (WIM) system and the structural health monitoring (SHM) system have been used as two separate modules playing different roles in bridge operation and providing different information for bridge maintenance. This study proposes a novel bridge safety condition assessment method that utilizes long-term monitoring data from the WIM system and the SHM system. The method uses the slope of the established vehicle load and vehicle-induced strain mapping model as the evaluation indicator for bridge condition assessment and early warning by clustering and Bayesian linear regression. The proposed method is verified with the continuous monitoring data of a concrete box girder bridge. The results show that the slope indicator of the mapping model changes with the variation of bridge performance, which is stable and can reflect the bridge state in time. The evaluation method can integrate the WIM system with the SHM system and evaluate the bridge health condition based on the correspondence between the two systems, which can make full use of the data.

АНАЛІТИЧНА ІНФОРМАЦІЯ З ПРОГРАМНОГО КОМПЛЕКСУ АЕСУМ ЩОДО МОСТІВ СТАНОМ НА 2022 РІК

Introduction. Effective development and maintenance of transport infrastructure is the key to the strategic development of the state. Bridges are the objects of critical infrastructure and, accordingly, an important component of it. In order to make effective management decisions at the level of the State Road Agency of Ukraine in the field of bridge construction, an Analytical Expert Bridge Management System (AESUM) software complex is implemented. Problem statement. Actually, AESUM contains brief technical information on all bridges on public roads, namely on 16,142 transport facilities. Bridge economy is a living system where everything is changing : the number of bridges due to the construction of new ones, the transformation of small bridges into culverts; operating organizations, and also, which is very important — the technical state of bridges. Today, the sampling of bridges inspected and included in the AESUM software complex is 75 % on roads of state importance, 15% on local roads. Accordingly, information on bridges is constantly changing and needs to be updated. It is also important to improve the bridge operation strategy considering the scientific and analytical apparatus of AESUM. Objective. Implementation of the effective strategy for bridges operation on the roads in order for planning financial resources for their repairing and maintenance. Providing the general public with up-to-date statistical information on bridges from AESUM will be useful for use in scientific and scientific-practical purposes, and the identification of certain analytical trends will allow improving AESUM regarding the formation of a bridge operation strategy. Materials and methods. Analytical, statistical, system analysis. Results. The study presents analytical and statistical information about bridges on the public roads of Ukraine. Certain trends and keen attention to the technical condition of bridges have been observed. Conclusions. Actually The technical state of road bridges in Ukraine at the moment is unsatisfactory, which causes significant social and material damage and requires an increase in allocations for their repair and reconstruction. Currently, there is an urgent need for improving and implementing strategies for bridges operation, considering the scientific and analytical apparatus of AESUM for optimal priority planning and timely implementation of rehabilitation works on bridges and maintenance of bridges in proper state with rational use of financial resources.

A new model to simulate the site-specific traffic load with consideration of precise axle load and microscopic behavior

Traffic load is one of the important external loads of bridges and simulating the site-specific traffic load is important for bridge condition assessment. This study proposes a new microscopic model to simulate the stochastic traffic load of any time period at bridge site based on weigh in motion (WIM) data. A model for simulating the microscopic interaction between vehicles was first proposed, by which free driving, car following and sudden braking can be reasonably simulated. Axle-weight information from the WIM data was embedded into the model to precisely simulate the vehicle load. A new vehicle sequence generation method based on WIM data was then proposed to generate the site-specific vehicle sequence of any time period with consideration of non-stationarity. Finally, a framework for simulating the site-specific stochastic traffic load is established. The proposed framework is validated through application based on realistic WIM data in traffic-flow simulation and load effect quantification. Comparisons of simulation results with the existing methods were also conducted to show the reasonability and novelty. The results indicate that the proposed framework can produce reasonable site-specific stochastic traffic load over any time period based on WIM data.

Influence lines-based model updating of suspension bridges considering boundary conditions

The boundary conditions of the main girder are complicated for suspension bridges. And they have a significant impact on the static and dynamic response of the structure. This study proposes a model updating method based on influence lines considering boundary conditions. First, rotational and translational spring elements are used to simulate the boundary conditions of the main girder. Then, rotational spring stiffness is estimated by constructing a sensitive objective function based on the influence lines. Translational spring stiffness is estimated by constructing a sensitive objective function based on the temperature-induced displacements at both ends of the girder. To improve calculation efficiency, a Gaussian process model is used instead of the finite element model (FEM). The estimation of boundary conditions is expressed as an optimization whose main goal is to iteratively estimate the boundary conditions to minimize the objective functions. After the boundary conditions are determined, the influence lines-based model updating is carried out by using the adaptive metamodel global optimization method. To validate the proposed method, a long-span suspension bridge is taken as a case study. The results show that the boundary conditions of the main girder have been successfully estimated. The accuracy of the model updating is improved after the boundary conditions are considered. The updated FEM with accurate boundary conditions can be a benchmark model for bridge damage identification or condition assessment.

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.

Evaluation Of Post Tensioned Bridges’ Tendon Ducts By NDT And Minor Invasive Measures

This presentation introduces the author’s accumulated long experience in applying state of the art NDT methods for post tensioned bridges’ grout injection evaluation together with minor invasive investigations for validating NDT results and for evaluation of prestressing system condition including duct’s grout injection and prestressing steel corrosion. The injection grout (cementitious based grout) has a crucial role in protecting the prestressing steel against corrosion caused by different environmental stresses. Corrosion of prestressing steel may cause dangerous fracture mechanisms such as hydrogen embrittlement and stress corrosion cracking. In addition, the injection grout has the important role of intermediating the prestressing forces from the prestressing steel to the concrete structure. Therefore, successful grout injection is critical for the correct structural functionality of a post tensioned bridge. The introduced NDT methods (ultrasound tomography and Impact-Echo), used together with invasive investigations, have already been successfully applied by the author in over 110 inspections of post tensioned bridges in the past 12 years, mostly in Finland. In situ evaluation of NDT testing results pinpoint the logical locations for invasive investigations. These include opening of tendon ducts and evaluation of the steel duct, injection grout and condition of the prestressing steel (ocular corrosion evaluation). There are thousands of aging post-tensioned bridges, frequently on critical transportation routes, built mostly in the 1960s-1970s. Occasionally the prestressing was applied in a faulty manner due to various reasons. In some cases, when stress from the environment is added to faulty construction, the consequences may be severe and possibly affect the safe usage of bridges. Faults and damages in prestressing systems are hidden from the eyes. Therefore the severity of the situation is unknown and it may result in extreme cases even in sudden collapse of bridges - as witnessed in the 2018 catastrophic collapse of the Morandi Bridge in Genoa Italy where apparently, according to publications, a post tensioned main structural part failed. Significant part of the aged post tensioned bridges have already reached renovation time. Therefore, their condition should be assessed. Bridge condition evaluation usually involves “traditional” inspection techniques which do not provide information on the internal prestressing system even though it has a crucial role in the bridge load carrying capacity. The abovementioned NDT methods combined with invasive investigations enable this critical evaluation task. In addition, those abovementioned NDT methods are suitable as quality assurance measures in newly constructed post tensioned bridges for ensuring long service life of bridges since day one.

Deep Reinforcement Learning–Enabled Bridge Management Considering Asset and Network Risks

Bridges deteriorate over time due to various environmental and mechanical stressors. Deterioration is a significant risk to bridge owners (asset risk) and the traveling public (network risk). To tackle this issue, transportation agencies carry out bridge management under limited resources to preserve bridge conditions and control the risks of bridge failure. Nonetheless, existing network-level analysis for bridge management cannot explicitly consider the effects of preservation actions on network risk, measured directly by functionality indicators such as network capacity. In this paper, a novel method based on deep reinforcement learning is proposed to devise network-level preservation policies that can reflect bridge importance to network functionality. The proposed method is based on the proximal policy optimization algorithm adapted for bridge management problems and improved via distributed computing and architecture. The method is applied to an illustrative bridge network. The results indicate that the proposed method can produce significantly better preservation policies in terms of minimizing long-term costs that include asset and network risks. The devised policies are also investigated in depth to allow for transparent interpretation and easy integration with existing bridge management systems.

EVALUATION OF THE AIR KANYUT BRIDGE CONDITIONS, BANGKA REGENCY, BANGKA BELITUNG PROVINCE USING THE BRIDGE INSPECTION METHOD DIRECTORATE GENERAL OF BINA MARGA

Bangka Regency is one of the regencies in Bangka Belitung Province, which has several potentials for improving regional economies, such as agriculture and plantation, fisheries and marine, mining and industry, and tourism. Bridges have an important role in supporting activities in the sector, but there are bridges that are estimated to have critical conditions in Bangka Regency, namely, Air Kanyut Bridge. Special handling is needed in the maintenance and repair of bridges and roads, so all of activities supporting economy can run well. Bridge maintenance conducted with regular and periodic inspections, if there is damage during the inspection, then proceed with a special inspection to determine the required preservation measures. The Ministry of Public Works and Public Housing through the Directorate General of Bina Marga has a guideline used in conducting bridge inspections, namely Bridge Inspection Guidelines Number 01/P/BM/2022. The guidelines contain general provisions and technical provisions regarding the planning, preparation, and implementation of bridge inspections required for inventory inspection, detailed inspection, routine inspection, and special inspection of bridge conditions.

Bridge modal property identification based on asynchronous mobile sensing data

There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.

Risk based maintenance for Swiss railway bridges: concept, implementation and first experiences

The Swiss Federal Railway (SBB) has an inventory of approx. 6,000 bridges. So far, condition classes of the individual bridges have been used for maintenance management. To improve the efficiency of the maintenance management, a long-term change from the condition class-based approach to a risk-based approach is considered. Such a risk-based approach was developed and implemented during a two-stage process into software (Excel and Python). The software was also linked to the SBB databases to access the relevant data. The Python software now includes 12 parameters to adapt the initial failure probability to specific bridge conditions and 15 damage parameters. So almost 30 parameters are used to compute the risk for each bridge. The software is also linked to geographical maps to show the location of the bridges. Besides the development of the approach, also the first experience of the application of this methodology will be discussed. For example, the risk-based ranking of the bridges clearly showed that specific bridge types are dominating. Also, some original ideas and concepts were not applicable due to difficulties in providing the required input data. However, currently the risk-based bridge ranking complies well with former investigations carried out by hand.

Identification of boundary conditions of railway bridges using artificial neural networks

This article presents a study that aims to identify the boundary conditions of a railway bridge using system identification and artificial neural networks. Vibrations generated by three different train types recorded during a 24-h long measurement campaign is used to identify the modal frequencies and mode shapes of a single-span 50 m long railway bridge. Frequency Domain Decomposition and Stochastic Subspace Identification with Covariance methods were used to identify the modal properties from the recorded vibrations and the effect of the used Operational Modal Analysis on the identified modal properties was evaluated. An initial finite-element (FE) model based on the design drawings was not able to replicate the observed dynamic behavior of the bridge. Using a sensitivity analysis, the key parameters of the finite-element model that impact the vibration frequencies of the bridge was determined. 300 finite-element models were created by changing the values of these key parameters within their effective range and were used to identify the relationship between these parameters and the vibration frequencies using Artificial neural networks (ANNs). Leveraging this relationship, the values of the FE model parameters that minimizes the error between the measured and computed frequencies was determined. As a result, the mean error between the computed and the identified vibration frequencies was reduced from 27.3% for the initial model to 3.0% for the updated model. The study indicates that boundary conditions are among the most influential parameter on the dynamic behavior of bridges and can deviate significantly from the simplistic models generally used in the FE models.

Reliability Assessment Method for Simply Supported Bridge Based on Structural Health Monitoring of Frequency with Temperature and Humidity Effect Eliminated

Structural health monitoring (SHM) has been widely used for the performance assessment of bridges, especially the methods based on dynamic characteristics. Meanwhile, bridge modal frequency is influenced significantly by environmental factors, such as temperature and humidity. Combined with SHM, a reliability assessment of bridges with the temperature and humidity effects eliminated is proposed. Firstly, the bridge deflection verification coefficient is adopted as the evaluation indicator for bridge condition, which is the ratio of deflection-measured value to deflection-calculated value. It is obtained from the relationship between verification coefficient and modal frequency through theoretical derivation. Secondly, a back propagation (BP) neural network is improved by using an artificial bee colony algorithm and employed as a surrogate model to eliminate the effect of temperature and humidity on frequency. Thirdly, a dynamic Bayesian network is applied to establish the reliability analysis model combined with the monitoring results, so that the probability distribution of bridge parameters is updated to improve the accuracy of the reliability analysis. Finally, a simply supported bridge is used as the case study, based on the proposed method in this work. The results indicate that the proposed method can eliminate the temperature and humidity effect on frequency precisely and effectively. With the effect of temperature and humidity on frequency eliminated, the bridge condition assessment can be evaluated accurately through the reliability analysis based on SHM and the dynamic Bayesian network.

Bridge damage detection using operating deflection shape ratios obtained from a passing vehicle

Large scale monitoring of bridge structures poses a significant challenge for infrastructure managers. This challenge could be overcome by using passing vehicles to monitor bridge condition. The operating deflection shapes of a bridge can theoretically be detected from in-vehicle measurements. This paper proposes a novel approach which introduces the concept of operating deflection shape ratios (ODSRs) from the responses measured on two following axles on a bridge. Unlike operating deflection shapes derived from drive-by measurements, ODSRs are non-dimensional values and are not a function of the bridge excitation level. This means that ODSRs are always constant for various vehicles passages. It is theoretically demonstrated in this study that ODSRs corresponding to each mode of bridge vibration can be approximately extracted from the responses of two following axles. Finite element modelling is employed to validate the feasibility of using ODSRs for bridge monitoring. The sensitivity of the ODSRs to damage is demonstrated numerically for damage at various locations, different vehicle speeds and different pavement roughness values. Laboratory experiments are then used to verify the theory in a practical environment. It is confirmed that the ODSR can be calculated from in-vehicle vibration measurements and can be used to detect changes in the structural behaviour of the bridge. The results of the study indicate that the ODSR represents a useful feature for drive-by bridge monitoring.

Developing Bridge Deterioration Models Using an Artificial Neural Network

The condition of a bridge is critical in quality evaluations and justifying the significant costs incurred by maintaining and repairing bridge infrastructures. Using bridge management systems, the department of transportation in the United States is currently supervising the construction and renovations of thousands of bridges. The inability to obtain funding for the current infrastructures, such that they comply with the requirements identified as part of maintenance, repair, and rehabilitation (MR&R), makes such bridge management systems critical. Bridge management systems facilitate decision making about handling bridge deterioration using an efficient model that accurately predicts bridge condition ratings. The accuracy of this model can facilitate MR&R planning and is used to confirm funds allocated to repair and maintain the bridge network management system. In this study, an artificial neural network (ANN) model is developed to improve the bridge management system (BMS) by improving the prediction accuracy of the deterioration of bridge decks, superstructures, and substructures. A large dataset of historical bridge condition assessment data was used to train and test the proposed ANN models for the deck, superstructure, and substructure components, and the accuracy of these models was 90%, 90%, and 89% on the testing set, respectively.

XImpact: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts.

Bridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge’s long-term degradation. Therefore, always-on monitoring is essential for deployed systems to enhance bridge safety through the reliable detection of such events and the rapid assessment of bridge conditions. Traditional bridge monitoring systems using wired sensors are too expensive for widespread implementation, mainly due to their significant installation cost. In this paper, an intelligent wireless monitoring system is developed as a cost-effective solution. It employs ultralow-power, event-triggered wireless sensor prototypes, which enables on-demand, high-fidelity sensing without missing unpredictable impact events. Furthermore, the proposed system adopts a smart artificial intelligence (AI)-based framework for rapid bridge assessment by utilizing artificial neural networks. Specifically, it can identify the impact location and estimate the peak force and impulse of impacts. The obtained impact information is used to provide early estimation of bridge conditions, allowing the bridge engineers to prioritize resource allocation for the timely inspection of the more severe impacts. The performance of the proposed monitoring system is demonstrated through a full-scale field test. The test results show that the developed system can capture the onset of bridge impacts, provide high-quality synchronized data, and offer a rapid damage assessment of bridges under impact events, achieving the error of around 2 m in impact localization, 1 kN for peak force estimation, and 0.01 kN·s for impulse estimation. Long-term deployment is planned in the future to demonstrate its reliability for real-life impact events.

Model updating-based dynamic collapse analysis of a RC cable-stayed bridge under earthquakes

This study proposes a general framework for progressive seismic collapse analysis of reinforced concrete (RC) bridges and it begins with an introduction of the progressive seismic collapse framework, which is mainly composed of five procedures. Subsequently, a 1:12 scale RC cable-stayed bridge together with the measurement system are presented, and the bridge model is subjected to a series of shake table tests. A conventional nonlinear finite element (FE) model, representing the undamaged condition of the cable-stayed bridge, is established, and the FE model is then nonlinearly updated based on the measured data from the measurement system when the bridge model is under small and moderate shake table excitations. The reliability of the updating process is validated through comparisons against another independent group of the measured data during the same moderate excitation. A more destructive ground motion is finally applied to the damaged FE model, where the progressive seismic collapse responses are obtained. The simulated seismic collapse responses based on the nonlinearly updated FE model give a good agreement with the measured data from the shake table tests employing the same destructive excitation, indicating that the model updating strategy proposed in this paper is efficient and reliable.

Monitoring the Condition of Narrow Bridges Using Data from Rotation-Based and Strain-Based Bridge Weigh-in-Motion Systems

Monitoring the Condition of Narrow Bridges Using Data from Rotation-Based and Strain-Based Bridge Weigh-in-Motion Systems