Detailed load rating analyses of bridge populations using nonlinear finite element models and artificial neural networks

For assessing load rating capacity of bridges, American Association of State Highway and Transportation Officials Manual (AASHTO) recommends a simple method, where distribution of the forces in transverse direction is estimated by axle-load distribution factors on a simply supported beam. Although the method is practical in the sense that it allows for rapid evaluation of bridge populations, it leads to over-conservative load ratings. A finite element (FE) based load rating analysis is conceived as a more accurate strategy, yet the need for constructing and analyzing a FE model for every single bridge in the population makes it impractical for load rating analyses of a bridge population. In this study an efficient method is developed for detailed load rating analyses of bridge populations through nonlinear FE models and artificial neural networks (ANNs). In this method, geometric-replica 3D FE models are used for nonlinear response analyses and load rating calculations for a sample bridge set. ANNs are then trained to learn implicit relationships between the governing bridge parameters and the resulting load ratings using this sample bridge set, and to make cost-free load rating estimations for other bridges that are not included in the set. The single-span reinforced concrete T-beam bridge population in Pennsylvania State is used to demonstrate a practical case study for application of the method. The results indicate that FE based load rating calculation procedure integrated with ANNs can be used as efficient tools for in-depth condition assessment of bridge populations

Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks

The key parameters affecting dynamic and static responses of structural systems often change during their life cycles due to aging, deterioration, damage and rehabilitation. Model updating is a major research field that investigates numerical methods to improve simulation ability of finite element (FE) models by identifying the modified parameters in structural systems based on data collected from field experiments and/or laboratory tests. In this paper, artificial neural networks (ANNs) are used to develop an efficient method for finite element (FE) model updating of reinforced concrete (RC) T-beam bridges. The FE model of a sample bridge selected from Pennsylvania's bridge population is calibrated using neural networks trained according to datasets generated from linear and non-linear analyses separately. The simulated responses obtained from calibrated FE models are compared to the field-measured responses of the bridge to quantify accuracy of parameter estimation and success of the model updating process. The present study evinces the fact that ANNs can still be used efficiently and reliably for parameter estimation tasks under a high level of uncertainty and complexity that arises from aging and deterioration of RC bridges as well as nonlinear material properties of concrete. The study also indicates significance of non-linear response analysis for parameter identification for RC bridges, and underlines that only consideration of dynamic responses for model updating may lead to erroneous parameter predictions especially when the calibration is based on linear bridge responses

Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks

The key parameters affecting dynamic and static responses of structural systems often change during their life cycles due to aging, deterioration, damage and rehabilitation. Model updating is a major research field that investigates numerical methods to improve simulation ability of finite element (FE) models by identifying the modified parameters in structural systems based on data collected from field experiments and/or laboratory tests. In this paper, artificial neural networks (ANNs) are used to develop an efficient method for finite element (FE) model updating of reinforced concrete (RC) T-beam bridges. The FE model of a sample bridge selected from Pennsylvania's bridge population is calibrated using neural networks trained according to datasets generated from linear and non-linear analyses separately. The simulated responses obtained from calibrated FE models are compared to the field-measured responses of the bridge to quantify accuracy of parameter estimation and success of the model updating process. The present study evinces the fact that ANNs can still be used efficiently and reliably for parameter estimation tasks under a high level of uncertainty and complexity that arises from aging and deterioration of RC bridges as well as nonlinear material properties of concrete. The study also indicates significance of non-linear response analysis for parameter identification for RC bridges, and underlines that only consideration of dynamic responses for model updating may lead to erroneous parameter predictions especially when the calibration is based on linear bridge responses

Monitoring Of A Movable Bridge Mechanical Components For Damage Identification Using Artificial Neural Networks

This paper presents a review of the results of a structural health monitoring (SHM) study to track the performance of a gearbox and rack-pinion of an operating movable bridge. These mechanical components are critical parts of bascule type bridges and damage of these components need to be identified and diagnosed, since an early detection of faults may help to avoid major damage to the structure and also avoid unexpected bridge closures. The prediction of the gearbox and rack-pinion fault detection is carried out with artificial neural networks (ANN) using the time domain vibration signals. Several statistical parameters are selected as characteristic features of the time-domain vibration signals. Monitoring data is collected during regular opening and closing of the bridge, as well as during artificially induced damage conditions. The results indicate that the vibration monitoring data, with selected statistical parameters and particular network architecture, give good results to predict the undamaged and damaged condition of the bridge

Structural Monitoring Of Movable Bridge Mechanica Components For Maintenance Decision-Making

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges

Heavy Movable Structure Health Monitoring: A Case Study With A Movable Bridge In Florida

A large number of movable bridges exist in Florida. These movable bridges are at critical intersection points of highway and marine traffic. Since these structures are composed of structural, mechanical and electrical components, encountered maintenance problems are different in nature and are observed more frequently. Therefore, movable bridge rehabilitation and maintenance costs are considerably higher than those of fixed bridges. The main issues are the deterioration due to their proximity to waterways, mechanical system failures, and fatigue due to the stress fluctuations during the operation. To improve their maintenance and predict possible problems ahead of time, continuous monitoring of these structures can be considered as a promising approach. In this study, the authors first discuss the problems and monitoring needs of such bridges. In the second part, design of the sensor network, data acquisition setup and data analysis methodologies are reviewed. Then, the field implementation and related challenges are described for a particular bridge. Finally, preliminary sample results from the analysis of the field data are discussed from safety, operation and maintenance point of view. © 2010 American Society of Civil Engineers

Time-Variant Reliability And Load Rating Of A Movable Bridge Using Structural Health Monitoring

After the recent bridge collapses in the US, the engineering community demands critical effects of bridge deterioration over long-term to be investigated and closely monitored. Combined with reliability techniques, structural health monitoring (SHM) can provide objective and accurate assessment of existing condition for safety and serviceability trends based on collected data. Two main approaches for this evaluation are rating and reliability. These approaches will be demonstrated analytically on a finite element model and the SHM data of a movable bridge: Sunrise Boulevard Bridge. Firstly, the reliability index and load rating will be analyzed under truck loading. A moving truck will be simulated on the model, obtaining the response reliability indices and ratings as a function of time. Strains from the model will be evaluated as monitoring data and component reliabilities will be calculated according to assumed limit states using random variables for material properties. Then, the same procedure is repeated but this time using the real-time data collected from the actual bridge. Finally, the results coming from both cases are compared and interpreted. These demonstrations will establish a guideline for applying reliability assessment based on monitoring data. ©2010 Society for Experimental Mechanics Inc

Use Of Statistical Analysis, Computer Vision, And Reliability For Structural Health Monitoring

Structural health monitoring (SHM) of civil infrastructures is becoming more feasible with the help of recent developments in sensing and computing technologies being more available and affordable. The SHM system may contain various types of measurements including, but not limited to, vibration, strain and image data. In this paper, the authors provide a general discussion of the two critical aspects of SHM: assessment of the current condition and future performance prediction from their recent studies at the University of Central Florida. First, SHM data can be used to track and evaluate the current condition of the structure with the help of statistical pattern recognition algorithms and computer vision techniques. Statistical analysis of these types of data can provide rapid extraction of information about the changes in structural behavior whereas the use of the computer vision technologies in a monitoring system offers to detect events visually. Subsequently, the available information obtained can be used for decision-making about the future performance of the structure. Prediction of the future performance is a very crucial step in better managing the life cycle safety, serviceability and costs. © 2010 American Society of Civil Engineers