Bridge Condition Assessment Research Articles

A BIM Based Approach for Structural Health Monitoring of Bridges

With the development of bridges, independent condition assessment of large-scale bridges has garnered significant attention over the past few decades. Data-driven structural health monitoring (SHM) techniques offer valuable information on the existing health of the structures, maintain safety, and uninterrupted use under varied operational conditions by undertaking timely risk and hazard mitigation. Traditional approaches, however, are not enough to monitor a large amount of SHM data and conduct systematic decision making for future maintenance. In this paper, a bridge health monitoring system is developed through the combination of building information modeling (BIM) and traditional bridge health monitoring that can organize and visualize a considerable amount of sensor data and subsequent structural health information over a prolonged period. The system can identify the structural damage by evaluating the data from sensors using Wigner-Ville distribution (WVD) in bilinear time-frequency analysis. A BIM-enabled platform is utilized to develop the proposed visualization tool for a long-span bridge and enable automated sensor data inventory into the BIM environment. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective bridge information management.

Data Acquisition and Structural Analysis for Bridge Deck Condition Assessment Using Ground Penetration Radar

AbstractBridge networks are a pillar of any national economy, and regular maintenance should be a high priority. Many transportation agencies require the condition assessment of concrete bridge dec...

Artificial Intelligence Based Structural Assessment for Regional Short- and Medium-Span Concrete Beam Bridges with Inspection Information

The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges.

Mapping textual descriptions to condition ratings to assist bridge inspection and condition assessment using hierarchical attention

Current bridge management strategies rely on experience-driven manually assigned condition ratings that are vulnerable to human subjectivity and experience variance. To improve the consistency of the condition rating practices, this study identifies narrative descriptions from bridge inspection reports as an untapped data source and proposes a data-driven framework as a supportive tool for two applications: automated condition recommendation and real-time quality control. A hierarchical architecture employing recurrent neural network encoders with an attention mechanism was developed using a collection of reports from the Virginia Department of Transportation. The condition recommendation application performed a classification task and demonstrated improved performance over a variety of baseline systems. The quality control application learns a data-driven decision threshold to decide whether to accept or reject an inspector-provided rating, which provides a cyber-human collaboration route for condition assessment. Visualization of the resulting attention patterns was shown to provide interpretable insights which highlight potentially-overlooked indicators.

Using measured rotation on a beam to detect changes in its structural condition

ABSTRACT A recent survey of Europe’s highway infrastructure has concluded that almost half of Europe’s bridges are nearing the end of their design live. Work in the wider Structural Health Monitoring sector is aiming to develop reliable and cost-effective methods for verifying condition, remaining service life and safety of ageing structures. Most bridge condition assessment methods are based on deflection, acceleration or strain measurements. This paper looks at the possibility of using rotation measurements as a main parameter to identify damage. This study looks at numerical analyses of a moving point load on a one-dimensional bridge model to provide the theoretical basis of the proposed damage detection method. It is shown that when local damage occurs, even when it is remote from a sensor location, it results in an increase in the magnitude of rotation measurements. This study looks at how best to exploit this fact for damage detection. A number of damage scenarios, sensor locations, and load arrangements are investigated in this study and their influence on the ability of the algorithm to detect damage are reported.

An accurate approach for obtaining spatiotemporal information of vehicle loads on bridges based on 3D bounding box reconstruction with computer vision

Vehicle load is an important basis for bridge design, condition assessment, and maintenance and strengthening, and its statistical laws can help us further understand the behavior of bridges under vehicle loads. Therefore, it is important to obtain accurate vehicle weight and vehicle spatiotemporal information that reflects the load time history. As of now, vehicle weight can be obtained using WIM technology, but there are still some issues in the methods of obtaining vehicle information. The coordinate transformation method is simple to operate, but sacrifices accuracy. The existence of a certain distance from the tail of vehicles to bridge decks makes the results based on dual-target detection differ from the actual. Therefore, an accurate approach for obtaining spatiotemporal information of vehicle loads on bridges based on 3D bounding box reconstruction with computer vision is proposed in this paper. To achieve this, a deep convolutional neural network (DCNN) and the You Only Look Once (YOLO) detector are used to detect vehicles and get the 2D bounding box. By establishing the relationship between 2D and 3D bounding box of the vehicle, an algorithm for 3D bounding box reconstruction of vehicles is proposed to get the sizes and position of vehicles. The spatiotemporal information of the vehicle loads is finally obtained by using multiple objects tracking (MOT). To verify the accuracy and reliability of the proposed approach, a bridge vehicle loads identification system (BVLIS) was developed and tested on a cable-stayed bridge in operation. The results show that the approach is accurate and reliable, and can be used to obtain vehicle information and provide vehicle load boundary conditions for bridge finite element modeling.

Performance-Based Ranking of Existing Road Bridges

Bridge condition assessment in most European countries is based on visual inspection in combination with damage assessment of bridge components. For adequate bridge management, the assessment needs to be further developed to move from the bridge component level to the system functionality level and finally to the priority ranking level for repairs in the network. Although visual inspection provides only qualitative insights into bridge condition and cannot predict load-carrying capacity, it is still very often the only way to collect data on existing bridges and can provide very important information for evaluating structural safety, traffic safety, durability, and overall bridge condition. Therefore, this paper presents a unique procedure that establishes a relationship between a country-specific bridge condition assessment procedure based on visual inspection and the systematization of key bridge performance indicators developed within the European integrated management approach at three complementary and interrelated levels—component, system, and network levels. The assessment procedure for existing bridges initiates with damage assessment based on visual inspection of bridge components and runs through weighting at component, system, and network levels to the six most important key performance indicators (KPIs) for road bridges, which are organized as graphical and numerical inputs for ranking priority maintenance. These are bridge condition assessment, structural safety, traffic safety, durability indicator, availability, and the importance of the bridge in the network. The procedure is validated on a case study set of five real bridges, using the decision-making process as an example for the small sample size. The case study bridges differ in cross-section, type, and span (which vary from 9.5 to 72 m). The bridges were built between 1958 and 2001 and are located either on state or municipal roads in Croatia. The results, in terms of condition classification and priorities of future interventions within the representative group of bridges, justify the application of the described assessment procedure. Additional digitization efforts could easily implement the described assessment approach at the infrastructure network level.

UAV-Based Remote Sensing Applications for Bridge Condition Assessment

Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Maintenance and rehabilitation needs of bridge infrastructure are periodically monitored and assessed, typically every two years. Existing inspection techniques, such as visual inspection, are time-consuming, subjective, and often incomplete. Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency, deterrence of traffic closure, and improved safety during inspection. The primary objective of this study is to conduct a comprehensive review of the application of UAVs in bridge condition monitoring, used in conjunction with remote sensing technologies. Remote sensing technologies such as visual imagery, infrared thermography, LiDAR, and other sensors, integrated with UAVs for data acquisition are analyzed in depth. This study compiled sixty-five journal and conference papers published in the last two decades scrutinizing NDT-based UAV systems. In addition to comparison of stand-alone and integrated NDT-UAV methods, the facilitation of bridge inspection using UAVs is thoroughly discussed in the present article in terms of ease of use, accuracy, cost-efficiency, employed data collection tools, and simulation platforms. Additionally, challenges and future perspectives of the reviewed UAV-NDT technologies are highlighted.

Limited sensor-based bridge condition assessment using vehicle-induced nonstationary measurements

Retrofitting and maintenance of bridges using vehicle-induced measurements have attracted increasing attention in the field of vibration-based bridge health monitoring. In this paper, a novel condition assessment method is proposed that uses a fewer number of sensors for monitoring bridges when subjected to moving vehicles. As a vehicle travels over a bridge, the bridge responses are measured at different locations, and the traffic-induced vibrations are analyzed to identify modal parameters of the bridge using a limited number of sensors. This study investigates a viable solution to various practical challenges associated with bridge inspection using vehicle-induced measurements, such as sensor location, vehicle speeds and vehicle parameters, and severity of the damage. Unlike ambient vibration measurements, moving vehicle-induced bridge vibration is nonstationary in nature. In this paper, a novel hybrid method is proposed by integrating Time-Varying Filtering-based Empirical Mode Decomposition (TVF-EMD) with Synchro-extracting Transform (SET) to track the nonstationary modal parameters of the bridge. The proposed approach is validated using numerical and full-scale studies comprising of a steel bridge subjected to passing vehicles.

Integration of practical supplemental measurements into bridge condition visual inspection grading

The reliability of condition assessment of bridges obtained from analysis of visual inspection data is always a big concern among structural engineers. It has been known that the condition grading of a bridge is very subjective and can convey limited information to the end user. To finalize and verify the reported condition grading, inspectors and bridge owners have mainly been relying on images. It has been known that the image observation may not be sufficient to address all kinds of problems associated with visual condition grading. The integration of practical supplemental measurements into condition grading as proposed in this paper will contribute to minimize the errors in visual inspection. Measurement of vehicle induced vibrations through wireless accelerometers can be used to determine the natural frequencies of the bridge that can be reported at each inspection. The change in frequencies can be an indication of deterioration in stiffness of bridge over the years. Taking concrete samples by chipping at the inspection site and analyzing them under scanning electron microscope (SEM) at the laboratory can be used to identify the current problems with concrete degradation. In this scope, the regular bridge inspection procedure with the proposed enhancements has been performed on field inspection of highway bridges in Turkey to determine the practicality of the quick supplemental measurements and to analyze the difference in grading of the three different inspectors with different level of experiences.

Indirect Measurements of Bridge Vibrations as an Experimental Tool Supporting Periodic Inspections

Recent collapses and malfunctions of European bridges threatened the service conditions of road networks and pointed out the need for robust procedures to mitigate the impact of material degradation and overloading of existing bridges. Condition assessment of bridges remains a challenging task, which could take advantage of cost-effective and reliable inspection strategies. The advances in sensors as well as Information and Communication Technologies (ICT) ensure a significant enhancement of the capabilities in recording and processing physical and mechanical data. The present paper focuses on the paradigm of indirect vibration measurements for modal parameter identification in operational conditions. It is very attractive because of the related opportunities of application of dynamic tests as a tool for periodic inspections while significantly mitigating their impact on the traffic flow. In this framework the instrumented vehicle acts as a dynamic measurement device for periodic inspections and provides valuable information on the structural response of the bridge at a low-cost. Vehicle-bridge interaction models are here applied to realistically simulate the traffic-induced vibration response of bridges and assess the accuracy of modal parameter estimates obtained from indirect vibration measurements characterized by different noise levels.

Fast Falling Weight Deflectometer Method for Condition Assessment of RC Bridges

In this paper, the use of Fast Falling Weight Deflectometer (Fast-FWD) is analyzed as a non-destructive and quick test procedure to evaluate the efficiency of short-span bridges. The Fast-FWD is an instrument that can produce a broadband dynamic force up to an impact value of 120 KN: The impact is constant and replicable, providing accurate action measures of bridge stiffness in a truly short period (30 ms). In this paper, a single-span reinforced concrete bridge is investigated, using the Fast-FWD. The considered bridge, approximately 12.0 m long and 15.5 m wide, was in critical condition. The bridge is in a suburban principal road near to the City of Cagliari in Sardinia (Italy), with an Annual Average Daily Traffic of 13,500 vehicles/day, and was suddenly closed, creating serious problems for urban mobility. In these conditions, the investigation through other standard techniques is time-consuming and labor intensive. For this reason, it is important to introduce methods that can be rapid, accurate and cost-efficient. In this paper, bridge stiffness values obtained during the in situ experimental campaign were compared with finite element models values. The Fast-FWD has the potential to provide engineering information that can help us to better understand bridge condition, in a rapid and cost-effective procedure.

Knowledge-Based Fuzzy Expert System to the Condition Assessment of Historic Road Bridges

A systematic methodology for condition assessment of the historic road bridges was needed because of the poor and inadequate condition of bridges which cannot satisfy everyday-day dynamic loads and deteriorations due to the aging process. Thus in this study, a new expert system based on the knowledge approach has been proposed to develop a systematic procedure for condition assessment of these bridges using fuzzy logic and sets of α-cuts. Each bridge is divided into three components: superstructure, substructure, and equipment, and each component is divided into relevant elements. These elements are evaluated by an expert and their ratings are fuzzified according to defined fuzzy sets, their membership functions, and linguistic values. Furthermore, fuzzy structural importance is given to ratings of each element. Combinations of these two values are calculated to obtain a fuzzy rating of the component using the Fuzzy Weighted Geometric Mean (FWGM). Finally, for the defuzzification of the component rating, the centroid method is proposed. The Analytic Hierarchy Process (AHP) is used for comparison of the components. The bridge condition rating is achieved by summering all the components ratings multiplied by their relative importance, and it is presented as a value of the Historic Road Bridge Condition Assessment Index (HRBCAI). The validation is conducted on the bridges built until the end of the Austro-Hungarian Monarchy in Split-Dalmatia County, Croatia.

PENILAIAN KONDISI DAN PREDIKSI UMUR SISA BERDASARKAN BRIDGE MANAGEMENT SYSTEM 1993 (STUDI KASUS JEMBATAN ATINGGOLA, PROVINSI GORONTALO)

Bridge inspection is the process of collecting physical data to ensure that the bridge is safe and thus securing the investment value of the bridge. Inspection results are used for planning the program of maintenance, rehabilitation, strengthening, and replacement of bridges. In this study, the assessment of the Atinggola bridge condition, located in Gorontalo Province, Indonesia, uses the reference standard from the Bridge Management System 1993, including the bridge top structure, the bridge bottom structure, complementary structures, and the river basin around the bridge. Previous bridge condition data which are based on the inspection result in 2017 are used as a comparison for the future. Condition values are used to predict the remaining life of a bridge using an equation developed by Bina Marga. Overall result shows the value of the bridge condition is 2 with the predicted remaining life of the bridge is 18.88 years. These results indicate that the bridge suffered minor damage and need routine maintenance. Therefore, the bridge is able to perform optimally until the end of the planned life.

LR-BCA: Label Ranking for Bridge Condition Assessment

Bridge condition assessment (BCA) plays an important role in modern bridge management. Existing assessment methods are time-consuming, labor-intensive and error-prone. The use of machine learning for BCA can effectively solve the above problems. However, the large amount of label noise in the dataset severely affected the performance of the BCA model. In this paper, we present an effective label ranking approach for BCA (LR-BCA). Our proposed LR-BCA method considers the natural order relationship between bridge condition ratings. Moreover, a heuristic data cleaning (HDC) approach is proposed for cleaning bridge condition dataset. The HDC method firstly identifies all the label conflict examples, then iteratively filters out the noise. Experimental results on real-world dataset confirm the effectiveness of the HDC method and demonstrate that our proposed LR-BCA method achieves 99% Top-2 accuracy, which is highly competitive compared to baseline methods.

Simultaneous identification of the frequencies and track irregularities of high-speed railway bridges from vehicle vibration data

Bridge frequencies and track irregularities are both the focuses of railway bridge condition assessment, which are coupled with each other in a vehicle-bridge system. Available algorithms face a great challenge when applied to simultaneously identify the natural frequencies and track irregularities of railway bridges using on-board measurement data. This paper proposes a novel algorithm for simultaneously identifying the frequencies and track irregularities of high-speed railway bridges using vehicle dynamic responses for the first time. An extended state-space model with unknown input condensation is established for time-dependent vehicle-bridge systems. We subsequently propose a new extended Kalman filter algorithm with an adaptive procedure for accelerating the convergence of estimation, which can simultaneously identify the frequencies and track irregularities of a railway bridge when a vehicle is running on it. The effectiveness of the proposed algorithm has been illustrated via numerical simulations of two real high-speed railway bridges. The proposed algorithm provides a low-cost and high-efficient approach for identifying the natural frequencies and track irregularities of high-speed railway bridges.

Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model

Bridge condition assessment by use of structural health monitoring (SHM) data has been recognized as a promising approach towards the condition-based preventive maintenance. In-service bridges are normally subjected to multiple types of loads such as highway traffic, railway traffic, wind and thermal effect, resulting in heterogeneous and multimodal data structure of strain/stress responses. This study aims to develop an SHM-based bridge reliability assessment procedure in terms of parametric Bayesian mixture modelling. The parametric mixture model admits representation of multimodal structural responses, while the Bayesian paradigm enables both aleatory and epistemic uncertainties to be accounted for in modelling. By defining appropriate priors for the mixture parameters that are viewed as random variables to interpret the model uncertainty, an analytical form of the full conditional posteriors is derived. A Markov chain Monte Carlo (MCMC) algorithm in conjunction with Bayes factor is explored to determine the optimal model order and estimate the joint posterior of the mixture parameters. In full compliance with the Bayesian framework, a conditional reliability index is elicited with the parametric Bayesian mixture model by using the first-order reliability method. The estimated value of the reliability index, which serves as a quantitative measure of health condition for the in-service bridge, can be successively updated with the accumulation of monitoring data. The proposed method is exemplified by using one-year strain monitoring data acquired from the instrumented Tsing Ma Suspension Bridge, in which the evolution of the estimated reliability index is obtained.

Identifying damage on a bridge using rotation-based Bridge Weigh-In-Motion

Bridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.

Long‐term performance monitoring and assessment of concrete beam bridges using neutral axis indicator

Long-term performance of existing bridge structures provides stakeholders with information that facilitates decision making for reasonable and optimal maintenance and management. The neutral axis (NA) is a potentially reliable and robust indicator to evaluate bridge performance since it depends on the loss of elastic modulus or effective sectional area. In this study, the NA indicator is defined and derived, and it is combined with the statistical characteristics of the NA distribution, to assess long-term structural performance. A systematic long-term performance assessment framework based on NA is also proposed. The NA is estimated from strain measurements and related practical signal processing methods. To validate the proposed framework, strain signals acquired from a preinstalled structural health monitoring system on an existing concrete beam bridge were analyzed. For the long-term performance assessment, an NA database was established. The statistical characteristics of the NA sample in the daily evaluation cycle were obtained to evaluate the performance. The results show that different cross sections have their own estimated NA; and during the monitoring period, the NA indicators fluctuate within a reasonable range. With the increase of monitoring data, these results can be continually updated. It can be concluded that the proposed NA indicator, along with the related framework, can be employed effectively in the condition assessment of concrete box girder bridges for long-term health monitoring.

Structural condition assessment of reinforced-concrete bridges based on acoustic emission and strain measurements

A reliable novel monitoring approach is developed to assess the structural condition of reinforced-concrete bridge elements. The approach is based on combining acoustic emission technique and strain gauge measurements, and it is illustrated by a case study of a composite steel–concrete viaduct in service since 1957. Monitoring was performed on its reinforced-concrete deck slab under traffic and environmental loading for one year. The monitoring setup and procedure are presented. The variation of acoustic emission signals is evaluated regarding strain and temperature measurements. Parametric study, pencil-break test, statistical analysis, crack classification and b-value analysis are performed to assess the structural condition. The acoustic emission activity of the reinforced-concrete slab is evaluated for 1 year under operational conditions. From the monitoring approach, it was possible to identify the nature of the cracking activity in the concrete slab as a function of traffic loading and temperature and to assess in time the condition of the slab-girder connection.