H. Burak Gokce

Predictive Analysis by Incorporating Uncertainty through a Family of Models Calibrated with Structural Health-Monitoring Data

AbstractComplex analysis and design of structures, especially landmark structures such as long-span bridges, have been conducted by many engineers and researchers. Currently, it is possible to collect more and precise monitoring data as well as to develop complex three-dimensional (3D) FEM models. These models, which can be calibrated using structural health-monitoring (SHM) data, can be used for the estimation of component and system reliability of bridges. However, the uncertainties related to the data, analysis, and nonstationary nature of the structural behavior need to be better incorporated by using a set of models that are continuously updated with monitoring data. This set of models constitutes a family as a result of the approach by which the models are obtained and the relationships among them. The objective of this paper is to explore the impact of uncertainty in predicting the system reliability obtained by a one-time, initially calibrated FEM model as well as by a family of FEM models continu...

Evaluation of Load Rating and System Reliability of Movable Bridge

Structural health monitoring (SHM) techniques can provide effective complementary means for the current bridge management system by reducing the uncertainty and by seamlessly integrating with state-of-the-art data management and Internet-based communication systems through information technology. However, SHM applications and studies do not fully exploit either statistical analysis of the collected data or structural reliability on the basis of sensor information. These additions can greatly enhance SHM to allow for more accurate and efficient data analysis, to predict future conditions, and to minimize life-cycle cost. An analytical simulation was set up with a field-validated finite element model (FEM) of an existing movable bridge to demonstrate how component and system reliability analysis can be performed. The validation of the FEM was performed with the use of the monitoring data from the bridge balance tests as well as a load test with a specialized truck. The validated model was then analyzed to estimate the load rating and reliability of the system when the model was loaded under HL-93 load (lane load and moving design truck). The load rating at critical locations and bridge system reliability as a function of truck location are presented.

Practical Approach for Estimating Distribution Factor for Load Rating: Demonstration on Reinforced Concrete T-Beam Bridges

This paper presents a new, practical methodology with simple analysis methods coupled with rapid experimental tests to identify distribution factors (DFs) for existing highway bridges. This approach is demonstrated on a reinforced concrete T-beam bridge population. It is shown that the moment DFs of single-span T-beam bridges can be determined by using skew angle, modal frequency, and the flexibility coefficient, where the frequency and the flexibility coefficient can be identified by means of rapid impact testing that can be conducted using an impactor, such as a falling weight deflectometer (FWD). This approach is first demonstrated using finite-element model (FEM) simulations. Moment approximation to FEM results with the new approach is 6%, whereas this approximation is on the order of 30% compared with the conventional beamline analysis given in the AASHTO code. This new approach is then demonstrated by using experimental data from four real-life bridges for the computation of moment values as well as the load ratings. It is shown that this new approach can conservatively approximate live load increases for the four existing bridges.

Critical issues, condition assessment and monitoring of heavy movable structures: emphasis on movable bridges

In this paper, a relatively less studied class of structures is presented based on the research conducted on Floridas movable bridges over the last several years. Movable bridges consist of complex structural, mechanical and electrical systems that provide versatility to these bridges, but at the same time, create intermittent operational and maintenance challenges. Movable bridges have been designed and constructed for some time; however, there are fewer studies in the literature on movable bridges as compared to other bridge types. In addition, none of these studies provide a comprehensive documentation of issues related to the condition of movable bridge populations in conjunction with possible monitoring applications specific to these bridges. This paper characterises and documents these issues related to movable bridges considering both the mechanical and structural components. Considerations for designing a monitoring system for movable bridges are also presented based on inspection reports and expert opinions. The design and implementation of a monitoring system for a representative bascule bridge are presented along with long-term monitoring data. Various movable bridge characteristics such as opening/closing torque, bridge balance and friction are shown since these are critical for maintenance applications on mechanical components. Finally, the impact of environmental effects (such as wind and temperature) on bridge mechanical characteristics is demonstrated by analysing monitoring data for more than 1000 opening/closing events.

A Characterization of Traffic and Temperature Induced Strains Acquired Using a Bridge Monitoring System

This study reviews different data analysis approaches to demonstrate possible means for getting information at different levels from the most commonly used strain sensors in an SHM system for short term and long term applications. The different methods are discussed from a real life monitoring study. First, the test bridge is presented with a brief introduction of the monitoring system and the sensor locations. Then, pre-processing and evaluation of raw strain data is discussed to identify the functionality of the installed sensors. Afterwards, strain data characterization by means of traffic and temperature induced strains is shown for more in-depth data analysis techniques. Finally, a new damage identification methodology based on strain correlation is discussed briefly. It is shown that there should be a suite of techniques and features to be available for implementation in SHM systems since one particular technique/feature may not be very reliable or sufficient for a particular case and/or structure.

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.

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.

Use of Family of Models for Performance Predictions and Decision Making

The issue about performance prediction is the need of a well representative model which can be analytical or mathematical due to non-availability of the future data. Analytical or mathematical models can be identified with the help of current data coming from structural health monitoring (SHM) systems and these models can be used for future performance predictions by incorporating uncertainties coming from modeling and monitoring data. In this study, a well calibrated finite element model (FEM) of a real life structure, which is accepted as the parent model of the family models, is introduced. Based on this model, offspring models, which include the modeling and measurement uncertainties are generated. In the offspring generation process uncertainties such as boundary conditions, loads, geometric and mechanical properties of the elements are defined with distributions. After the generation process, offspring models are analyzed and set of results are obtained for a family of models. These results are used for structural reliability calculations in the performance prediction part of the paper. At this point, the other important considerations such as system model definition and correlation of the components for the system reliability approach are also taken into account. Finally, future performances in the case of instantaneous or continuous structural changes are considered for structural system reliability prediction, which is critical for decision making about future performance of the structure, by incorporating uncertainties from measurement through modeling on the movable bridge.

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.

A Novel Approach to Analyze Existing Bridges Efficiently

Analyses of existing bridges are conducted for different reasons such as load rating and retrofit simulations. It is desirable to have full 3D finite element models for analysis purposes; however, development of such models may not be feasible since these 3D models require considerable time, effort and expertise. As a result, AASHTO provides practical methods for the analysis of the bridges. The basic approach which is commonly used by design offices, DOT engineers and other practitioners is the beam line analysis using distribution factors. The recent AASHTO LRFD Bridge Design Specifications provides formulations where span length, beam spacing, beam stiffness and slab thickness are used to obtain girder moment and shear distribution factors. Apart from field tests to determine the load distribution, there has been several other studies that led to the distribution factor equations given in The American Association of State Highway and Transportation Officials (AASHTO) bridge design specifications for many years. The first studies were conducted by Westergaard (1930) and Newmark (1948). After these studies, the main philosophy is to understand the bridge response such as shear and moment values with a relatively simple formulation. This distribution factor formulation started with a simple ratio of girder spacing (ft) over a constant which depends on bridge type. These basic ratio formulas were easy to use but could be very conservative in some cases. Zokaie et al. (1991a) showed that this ratio does not estimate shear and moment values properly because they were inappropriate for skewed bridges Therefore, a new research was carried out under National Cooperative Highway Research Program (NCHRP) Project (Zokaie et al., 1991b) that provides more accurate (but also a little bit more complex) formulations, which include girder spacing, girder stiffness, span length, slab thickness and skew angle. In addition, Barr et al. (2001) and Catbas et al. (2003) emphasized that one of the significant parameter for a bridge is skew angle. Moreover, new distribution factors for reinforced concrete (RC) T-beam bridges were discussed and offered by Catbas et al. (2005). With these studies, it is seen that the distribution factors are commonly used for bridge analysis and simple yet more reliable formulations for distribution factors have been under investigation. Based on their previous work, the writers explore the use of simply measurable quantities to obtain distribution factors more reliably, thus obtain better models that describe the actual condition of bridges. In this paper, the writers present their study to investigate moment and shear distribution factors of simple span T-beam bridges by using frequency ” i ω ”, flexibility