Probabilistic Performance-Based Procedure to Evaluate Pile Foundations at Sites with Liquefaction-Induced Lateral Displacement

Liquefaction-induced ground deformation has caused major damage to bridge and wharf structures in past earthquakes. Large lateral ground displacements may induce significant forces in the foundation and superstructure, which may lead to severe damage or even collapse. A performance-based earthquake engineering (PBEE) approach can provide an objective assessment of the likely seismic performance, so that agencies can evaluate bridge or wharf structures, compare retrofit strategies, and rank them within their overall system. In this paper, a probabilistic PBEE design procedure that incorporates findings from recent research on this problem is presented. The proposed approach can provide answers in terms that are meaningful to owners, such as expected repair costs and downtimes. The methodology is validated through its application to a well-documented case history. Results show that the proposed approach provides a good estimate of the seismic performance of pile-supported structures at sites with liquefaction-induced lateral displacement.     

Use of Supervisory Control and Data Acquisition for Damage Location of Water Delivery Systems

Urban water delivery systems can be damaged by earthquakes or severely cold weather. In either case, the damage cannot easily be detected and located, especially immediately after the event. In recent years, real-time damage estimation and diagnosis of buried pipelines attracted much attention of researchers focusing on establishing the relationship between damage ratio (breaks per unit length of pipe) and ground motion, taking the soil condition into consideration. Due to the uncertainty and complexity of the parameters that affect the pipe damage mechanism, it is not easy to estimate the degree of physical damage only with a few numbers of parameters. As an alternative, this paper develops a methodology to detect and locate the damage in a water delivery system by monitoring water pressure on-line at some selected positions in the water delivery systems. For the purpose of on-line monitoring, emerging supervisory control and data acquisition technology can be well used. A neural network-based inverse analysis method is constructed for detecting the extent and location of damage based on the variation of water pressure. The neural network is trained by using analytically simulated data from the water delivery system with one location of damage, and validated by using a set of data that have never been used in the training. It is found that the method provides a quick, effective, and practical way in which the damage sustained by a water delivery system can be detected and located.     

Estimating Fatigue Life of Bridge Components Using Measured Strains

The design fatigue life of a bridge component is based on the stress spectrum the component experiences and the fatigue durability. Changes in traffic patterns, volume, and any degradation of structural components can influence the fatigue life of the bridge. A fatigue life evaluation reflecting the actual conditions has value to bridge owners. Procedures are outlined in the AASHTO Guide Specifications for Fatigue Evaluation of Existing Steel Bridges to estimate the remaining fatigue life of bridges using the measured strain data under actual vehicular traffic. This paper presents the methodology with an actual case study of Patroon Island Bridge. The Patroon Island Bridge consists of ten spans. Spans 3 through 9 are considered the main spans and consist of steel trusses and concrete decks. Spans 1, 2, and 10 are considered approach spans and consist of plate girders. The overall bridge length is 1,795 feet. Strain data from critical structural members were used to estimate the remaining fatigue life of selected bridge components. The results indicate that most of the identified critical details have an infinite remaining safe fatigue life and others have a substantial fatigue life. Cracked floor beams were not addressed in this analysis, but have been recommended for retrofitting or replacement.     

Parameter Estimation for Multiple-Input Multiple-Output Modal Analysis of Large Structures

Experimental modal analysis (EMA) has been explored as a technology for condition assessment and damage identification of constructed structures. However, successful EMA applications such as damage detection to constructed systems pose certain difficulties. The properties of constructed systems are influenced by temperature changes as well as other natural influences such as movements in addition to any deterioration and damage. Writers were challenged in their attempts to measure the dynamic properties of an aged bridge by EMA due to inconsistencies within the data set due to short-term variations in ambient conditions. A complex interaction was observed between the dynamic properties of the bridge, hour-to-hour changes in temperature, and controlled damages applied to the bridge. Inconsistencies in the data set made curve fitting difficult for some common parameter estimation algorithms that have been designed to handle consistent data sets. Although the quality of measurements within the entire data set was affected by time variance and nonlinearity, increasing the number of reference measurements significantly improved the reliability of the information which could be extracted. In conjunction with the multiple-input multiple-output technique, a parameter estimation method using complex mode indicator function (CMIF) was developed and implemented in this study to determine the modal properties with proper scaling to obtain modal flexibility. This method proved to be very successful among many others with the data acquired from the aged and deteriorated highway bridge. In this paper, challenges in reliable identification of modal parameters from large structures are reviewed and the new CMIF based algorithm is documented. The method is evaluated on actual bridge data sets from a damage detection research study.     

Probabilistic Capacity Models and Fragility Estimates for Reinforced Concrete Columns Incorporating NDT Data

Knowing the ability of reinforced concrete (RC) bridges to withstand future seismic demands during their life-cycle can help bridge owners make rational decisions regarding optimal allocation of resources for maintenance, repair, and/or rehabilitation of bridge systems. The accuracy of a reliability assessment can be improved by incorporating information about the current aging and deterioration conditions of a bridge. Nondestructive testing (NDT) can be used to evaluate the actual conditions of a bridge, avoiding the use of deterioration models that bring additional uncertainties in the reliability assessment. This paper develops probabilistic deformation and shear capacity models for RC bridge columns that incorporate information obtained from NDT. The proposed models can be used when the flexural stiffness decays nonuniformly over a column height. The flexural stiffness of a column is estimated based on measured acceleration responses using a system identification method and the damage index method. As an application of the proposed models, a case study assesses the fragility (the conditional probability of attaining or exceeding a specified capacity level) of the column in the Lavic Road Overcrossing for a given deformation or shear demand. This two-span concrete box-girder bridge located in Southern California was subject to the Hector Mine Earthquake in 1999. Pre- and postearthquake estimates of the univariate shear and deformation fragilities and of the bivariate shear-deformation fragility are computed and compared. Both displacement and shear capacities are found to decrease after the earthquake event. Additionally, the results show that the damage due to the Hector Mine Earthquake has a larger impact on the shear capacity than the deformation capacity, leading to a more significant increment in the shear fragility than in the deformation fragility.     

Estimating Liquefaction-Induced Lateral Displacements Using the Standard Penetration Test or Cone Penetration Test

A semiempirical approach to estimate liquefaction-induced lateral displacements using standard penetration test (SPT) or cone penetration test (CPT) data is presented. The approach combines available SPT- and CPT-based methods to evaluate liquefaction potential with laboratory test results for clean sands to estimate the potential maximum cyclic shear strains for saturated sandy soils under seismic loading. A lateral displacement index is then introduced, which is obtained by integrating the maximum cyclic shear strains with depth. Empirical correlations from case history data are proposed between actual lateral displacement, the lateral displacement index, and geometric parameters characterizing ground geometry for gently sloping ground without a free face, level ground with a free face, and gently sloping ground with a free face. The proposed approach can be applied to obtain preliminary estimates of the magnitude of lateral displacements associated with a liquefaction-induced lateral spread.     

Identification of a Distribution of Stiffness Reduction in Reinforced Concrete Slab Bridges Subjected to Moving Loads

The method for identifying arbitrary stiffness reduction in damaged reinforced concrete slab bridges under moving loads is proposed and dynamic signals measured at several points are used as response data to reflect the properties of the moving loads sensitivity. In particular, the change in stiffness in each element before and after damage, based on the system identification method, is described and discussed by using a modified bivariate Gaussian distribution function. The proposed method in this work is more feasible than the conventional element-based damage detection method from the computational efficiency because the procedure of finite-element analysis coupled with microgenetic algorithm using six unknown parameters irrespective of the number of elements are considered. The validity of the technique is numerically verified using a set of dynamic data obtained from a simulation of the actual bridge modeled with a three-dimensional solid element. The numerical calculations show that the proposed technique is a feasible and practical method that can prove the exact location of a damaged region as well as inspect the complex distribution of deteriorated stiffness, although there is a modeling error between actual bridge results and numerical model results as well as a measurement error like uncertain noise in the response data.     

Case Study: New Zealand Bridge Scour Experiences

Details of three case studies of scour damage for New Zealand bridges are presented. These cases cover ranges of bed materials, flood magnitudes, and river morphologies, and illustrate a range of scour processes occurring at bridge foundations. The case study details facilitate evaluation of proposed bridge scour calculation methodologies. Such a proposed methodology for the assessment of scour depths at a bridge site is illustrated for the presented case studies. The resulting scour predictions highlight the value of a judicious use of the proposed methodology, the effect of sediment supply and transport balance considerations for engineering projects, aspects of river morphology to be considered in bridge foundation design, and that the combination of various components of scour needs to be considered when assessing bridge scour.     

Structural Reliability Estimation with Vibration-Based Identified Parameters

This paper presents a unique structural reliability estimation method incorporating structural parameter identification results based on the seismic response measurement. In the shaking table test, a three-bent concrete bridge model was shaken to different damage levels by a sequence of earthquake motions with increasing intensities. Structural parameters, stiffness and damping values of the bridge were identified under damaging seismic events based on the seismic response measurement. A methodology was developed to understand the importance of structural parameter identification in the reliability estimation. Along this line, a set of structural parameters were generated based on the Monte Carlo simulation. Each of them was assigned to the base bridge model. Then, every bridge model was analyzed using nonlinear time history analyses to obtain damage level at the specific locations. Last, reliability estimation was performed for bridges modeled with two sets of structural parameters. The first one was obtained by the nonlinear time history analysis with the Monte Carlo simulated parameters which is called nonupdated structural parameters. The second one was obtained by updating the first set in Bayesian sense based on the vibration-based identification results which is called updated structural parameters. In the scope of this paper, it was shown that residual reliability of the system estimated using the updated structural parameters is lower than the one estimated using the nonupdated structural parameters.     

Case-Based Reasoning for Converting Working Stress Design-Based Bridge Ratings to Load Factor Design-Based Ratings

In 1995, the Federal Highway Administration (FHWA) required that all bridges, regardless of the design method used for the original design, be based on the load factor design (LFD) method. In order to comply with the FHWA requirements, state departments of transportation have converted to the LFD method for all new bridge ratings. Further, all existing bridges previously rated using the working stress design (WSD) method must be converted to the LFD method. Consequently, thousands of bridges must be rerated using the LFD method. Steel bridges rated by the WSD method have critical data missing to make the proper conversion to the LFD method. This paper presents a methodology and an intelligent decision support system to help bridge engineers convert a WSD-based bridge rating to the LFD-based rating with little human effort using the artificial intelligence approach of case-base reasoning. The proposed methodology can help bridge engineers create the missing LFD-based data efficiently and quickly with a minimum amount of work. This research demonstrates how bridge engineers can use a novel computing paradigm and modern computer tool to convert an antiquated database to current design.     

Bridge Structural Condition Assessment Using Systematically Validated Finite-Element Model

The structural condition assessment of highway bridges is largely based on visual observations described by subjective indices, and it is necessary to develop a methodology for an accurate and reliable condition assessment of aging and damaged structures. This paper presents a method using a systematically validated finite-element model for the quantitative condition assessment of a damaged reinforced concrete bridge deck structure, including damage location and extent, residual stiffness evaluation, and load-carrying capacity assessment. In a trial of the method in a cracked bridge beam, the residual stiffness distribution was determined by model updating, thereby locating the damage in the structure. Furthermore, the damage extent was identified through a defined damage index and the residual load-carrying capacity was estimated.     

Diagnosis of Reinforced Concrete Structural Damage Base on Displacement Time History using the Back-Propagation Neural Network Technique

A state-of-the-art methodology is proposed for damage diagnosis of structures, such methodology being presented in the example of a simply supported reinforced concrete (RC) beam. The severity and location of defects within the RC structures can be assessed much more conveniently by using the back-propagation neural network technique. A simply supported RC beam with specified size (i.e., rectangular cross section and 4 m span) and assumed defects is theoretically analyzed by a finite-element program to generate training and the testing of numerical examples necessary to assess the damaged RC structure by using the neural network (NN). Numerical examples are then generated according to the displacement time history of the defected beams loaded by an impact force at the beam center. In addition, 10 sets of test beam with the assumed damage and same specified size of the numerical examples are constructed in full scale. The damage scenario of each test beam is also diagnosed by using the well-trained NN according to the displacement time history, which is the history of the responses caused by the impact loading acting at the beam centers. Based on the study and test results, the damage scenarios of the 10 sets of test beams are successfully classified.     

Damage Localization and Finite-Element Model Updating Using Multiresponse NDT Data

New techniques for both finite-element model updating and damage localization are presented using multiresponse nondestructive test (NDT) data. A new protocol for combining multiple parameter estimation algorithms for model updating is presented along with an illustrative example. This approach allows for the simultaneous use of both static and modal NDT data to perform model updating at the element level. A new damage index based on multiresponse NDT data is presented for damage localization of structures. This index is based on static and modal strain energy changes in a structure as a result of damage. This method depicts changes in physical properties of each structural element compared to its initial state using NDT data. Deficient or potentially damaged structural elements are then selected as the unknown parameters to be updated by parameter estimation. Error function normalization, error function stacking, and multiresponse parameter estimation methods are proposed for using multiple data types for simultaneous stiffness and mass parameter estimation. Also, multiple sets of measurements with various sizes and missing data points can be utilized. This paper uses a laboratory grid model of a bridge deck built at the University of Cincinnati Infrastructure Institute and the corresponding NDT data for validation of the above damage localization and model updating methods. Multiresponse parameter estimation has been utilized to update the stiffness of bearing pads, and both the stiffness and mass of the connections, using static and dynamic NDT data. The static and modal responses of the updated grid model presented a closer match with the NDT data than the responses from the initial model.     

Shake Table Studies of a Concrete Bridge Pier Utilizing Pipe-Pin Two-Way Hinges

Pipe-pin two-way hinge details were recently developed by California Department of Transportation (Caltrans) to eliminate moments while transferring shear and axial loads from integral bridge bent caps to reinforced concrete bridge columns. The hinges consist of a steel pipe that is anchored in the column with an extended segment into the cap beam. There is no specific design guideline for these hinges, and the current design method is primeval and only controls shear failure of the steel pipe. In this study, a rational method is proposed on the basis of the possible limit states to obtain the lateral capacity of these hinges. To validate the proposed method, a large-scale two-column bridge pier model utilizing pipe-pin hinges was tested on a shake table. The model was subjected to increasing levels of one of the Sylmar-Northridge 1994 earthquake records. A comprehensive analytical modeling of the pier was also performed using OpenSees; for this purpose, a macro model was developed for pipe-pin hinges in this study. The experimental results confirmed that the hinges designed on the basis of the proposed guideline remain elastic with no damage. The good correlation between the analytical and experimental data indicated that the macro model and other modeling assumptions were appropriate.     

Multicrack Detection on Semirigidly Connected Beams Utilizing Dynamic Data

The problem of crack detection has been studied by many researchers, and many methods of approaching the problem have been developed. To quantify the crack extent, most methods follow the model updating approach. This approach treats the crack location and extent as model parameters, which are then identified by minimizing the discrepancy between the modeled and the measured dynamic responses. Most methods following this approach focus on the detection of a single crack or multicracks in situations in which the number of cracks is known. The main objective of this paper is to address the crack detection problem in a general situation in which the number of cracks is not known in advance. The crack detection methodology proposed in this paper consists of two phases. In the first phase, different classes of models are employed to model the beam with different numbers of cracks, and the Bayesian model class selection method is then employed to identify the most plausible class of models based on the set of measured dynamic data in order to identify the number of cracks on the beam. In the second phase, the posterior (updated) probability density function of the crack locations and the corresponding extents is calculated using the Bayesian statistical framework. As a result, the uncertainties that may have been introduced by measurement noise and modeling error can be explicitly dealt with. The methodology proposed herein has been verified by and demonstrated through a comprehensive series of numerical case studies, in which noisy data were generated by a Bernoulli–Euler beam with semirigid connections. The results of these studies show that the proposed methodology can correctly identify the number of cracks even when the crack extent is small. The effects of measurement noise, modeling error, and the complexity of the class of identification model on the crack detection results have also been studied and are discussed in this paper.     

Bridge Management and Nondestructive Evaluation

The City and County of Denver (CCD) Public Works Department owns, inspects, and maintains 531 bridges in its inventory of which 264 are considered major structures spanning over 6.1?m in length. In this paper, a methodology using the CCD major bridge network for the application of nondestructive evaluation (NDE) methods in bridge inspections is explained. The methodology, called Bridge Evaluation using Nondestructive Testing (BENT) helps systematically integrate NDE methods and conventional bridge management systems by using a Markovian deterioration model. Although the BENT method can be applied to timber, steel, and concrete bridges, in this paper the application of the method will be restricted to concrete bridges. The BENT system is part of a comprehensive geographic information system whereby database queries can be completed using a map interface. The database contains a wide array of information in the CCD infrastructure inventory including bridges, pavements, alleys, and street subsystems.     

Damage Detection Utilizing the Damage Index Method to a Benchmark Structure

This paper addresses the first generation benchmark problem on structural health monitoring developed by the ASCE Task Group on Structural Health Monitoring. The focus of the problem is a four-story model of an existing physical model at the University of British Columbia where simulated data are used for the system identification. Modal parameters were extracted using the frequency domain decomposition method. Rather than relying on data from the undamaged structure, a new proposed methodology based on ratios between stiffness and mass values from the eigenvalue problem is presented to identify the undamaged state of the structure. Once the structural identification is complete, the damage index method is used to detect the location and severity of damage. By not relying on undamaged structure information, this approach may be applicable to existing structures that may already incorporate some amount of damage.     

Application of Fuzzy Sets for Remaining Life Assessment of Corrosion Affected Reinforced Concrete Bridge Girders

A methodology for remaining life assessment of reinforced concrete bridge girders affected by chloride-induced corrosion of reinforcement is proposed. The uncertainties in the values of the parameters characterizing the environment and, the variables affecting the time to corrosion initiation, and the rate of corrosion propagation are taken into account by treating them as fuzzy variables. The usefulness of the proposed methodology is illustrated through a case study, by comparing the times to reach different damage levels for a severely distressed beam, of Rocky Point Viaduct, determined using the proposed methodology with the observations from the field investigations. It is noted that the predicted times are in satisfactory agreement with the reported values. Hence, the methodology will be useful for the scheduling of inspections for reinforced concrete girders subjected to chloride-induced corrosion of reinforcement.     

Inverse First-Order Reliability Method for Probabilistic Fatigue Life Prediction of Composite Laminates under Multiaxial Loading

A new methodology for concurrent dynamic analysis and structural fatigue prognosis is proposed in this paper. The proposed methodology is based on a novel small timescale formulation of material fatigue crack growth that calculates the incremental crack growth at any arbitrary time within a loading cycle. It defines the fatigue crack kinetics based on the geometric relationship between the crack-tip opening displacement and the instantaneous crack growth rate. The proposed crack growth model can be expressed as a set of first-order differential equations. The structural dynamics analysis and fatigue crack growth model can be expressed as a coupled hierarchical state-space model. The dynamic response (structural level) and the fatigue crack growth (material level) can be solved simultaneously. Several numerical problems with single-degree-of-freedom and multiple-degree-of-freedom cases are used to show the proposed methodology. Model predictions are validated by using coupon testing data from open literature. Following this, the methodology is demonstrated by using a steel girder bridge. The proposed methodology shows that concurrent structural dynamics and material fatigue crack growth analysis can be achieved. Cycle-counting method in the conventional fatigue analysis can be avoided. Comparison with experimental data for structural steels shows a satisfactory accuracy by using the proposed coupled state-space model.