Masoud Sanayei

Damage assessment of structures using static test data

An analytical method is presented for identifying the properties of structural elements from static test data. A set of static forces is applied to a set of degrees of freedom (DOF) and displacements are measured at another set of DOF. Utilizing this analytical method, the structural element stiffnesses are identified using the applied forces and measured displacements. This method is capable of determining changes in structural element stiffnesses, including element failure. The identified cross-sectional properties of the structural elements can be used for damage assessment and to determine the structures load-carrying capacity.

Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements

A baseline finite element model was developed for bridge management and calibration using nondestructive test data. The model calibration technique was evaluated on the Vernon Avenue Bridge over the Ware River in Barre, Massachusetts. This newly constructed bridgewas instrumented throughout its construction phases in preparation for a static truck load test performed before the bridge opening. The strain data collected during the load test was used to calibrate a detailed baseline finite element model in an effort to represent the 3D system behavior of the bridge. Three methods of load ratings were used and compared: (1) conventional method, (2) conventional method updated by using NDT data, and (3) finite element model calibrated with NDT data. DOI: 10.1061/(ASCE)BE.1943-5592.0000228.

Selection of Noisy Measurement Locations for Error Reduction in Static Parameter Identification

An incomplete set of noisy static force and displacement measurements is used for parameter identification of structures at the element level. Measurement location and the level of accuracy in the measured data can drastically affect the accuracy of the identified parameters. A heuristic method is presented to select a limited number of degrees of freedom (DOF) to perform a successful parameter identification and to reduce the impact of measurement errors on the identified parameters. This pretest simulation uses an error sensitivity analysis to determine the effect of measurement errors on the parameter estimates. The selected DOF can be used for nondestructive testing and health monitoring of structures. Two numerical examples, one for a truss and one for a frame, are presented to demonstrate that using the measurements at the selected subset of DOF can limit the error in the parameter estimates.

Significance of Modeling Error in Structural Parameter Estimation

Structural health monitoring systems rely on algorithms to detect potential changes in structural parameters that may be indicative of damage. Parameter estimation algorithms seek to identify changes in structural parameters by adjusting parameters of an a priori finite-element model of a structure to reconcile its response with a set of measured test data. Modeling error, represented as uncertainty in the parameters of a finite-element model of the structure, curtails capability of parameter estimation to capture the physical behavior of the structure. The performance of four error functions, two stiffness-based and two flexibility-based, is compared in the presence of modeling error in terms of the propagation rate of the modeling error and the quality of the final parameter estimates. Three different types of parameters are used in the parameter estimation procedure: (1) unknown parameters that are to be estimated, (2) known parameters assumed to be accurate, and (3) uncertain parameters that manifest the modeling error and are assumed known and not to be estimated. The significance of modeling error is investigated with respect to excitation and measurement type and locations, the type of error function, location of the uncertain parameter, and the selection of unknown parameters to be estimated. It is illustrated in two examples that the stiffness-based error functions perform significantly better than the corresponding flexibility-based error functions in the presence of modeling error. Additionally, the topology of the structure, excitation and measurement type and locations, and location of the uncertain parameters with respect to the unknown parameters can have a significant impact on the quality of the parameter estimates. Insight into the significance of modeling error and its potential impact on the resulting parameter estimates is presented through analytical and numerical examples using static and modal data.

Finite Element Model Updating Using Frequency Response Function of Incomplete Strain Data

A method is presented to detect changes in stiffness and mass parameters of a structure using strain data in the frequency domain. Sensitivity of the strain-based frequency response function is characterized as a function of the changes in stiffness, mass, and damping at the element level. The transfer function of a damaged structure is approximated using the measured natural frequencies of the damaged structure and the analytical mode shapes of the intact structure. These approximations result in a set of quasi-linear sensitivity equations. A sensitivity-based algorithm is proposed for finite element model updating. These sensitivities are used to select the measured frequency points for model updating and to investigate the weighting of sensitivity equations. The proposed method was successfully applied to a plane truss and a plane frame structure using numerically simulated, error-contaminated strain data.

PREDICTION AND MITIGATION OF BUILDING FLOOR VIBRATIONS USING A BLOCKING FLOOR

AbstractBuildings that are located near transportation corridors often experience floor vibrations induced by passing trains or traffic, which causes building owners some concern. In this paper, a mathematical, impedance-based (wave propagation) model is presented for predicting train-induced floor vibrations in buildings. The model analytically predicts velocities, velocity ratios, and impedances. The analytical predictions of the model were compared and validated with the measured floor vibrations in a 4-story scale model building constructed by the writers. These predictions closely mimicked the measured responses. Using the results from the method presented indicate that the vibrations on the upper floors can be mitigated by increasing the thickness of a floor at a lower level in the building. This lower-level floor with the increased thickness is called a blocking floor. The scale model building was tested with and without a blocking floor. The predicted and measured responses of the scale model buil...

Quasi-linear sensitivity-based structural model updating using experimental transfer functions

Experimental validation of a new model updating method is presented for structural mass and stiffness estimation using vibration data. The method uses transfer functions for finite element model updating via a quasi-linear sensitivity equation of the structural response. Excitation frequencies are selected in the most sensitive ranges of transfer functions for robust updating of the structural parameters. In addition, noisy regions are omitted from measured transfer functions. A least squares algorithm with appropriate normalization is used for solving the overdetermined system of equations. The method is verified using experimental data from a one-bay, one-story aluminum frame. Fast and accurate prediction of stiffness and mass parameters using a subset of measured transfer functions in selected frequency ranges illustrated the success and robustness of the method in the presence of measurement and modeling errors.

Statistical Bridge Signatures

AbstractInstrumentation of bridge structures provides a stream of data representing operational structural response under loading. The authors define the term bridge signature as the expected response of a particular bridge under loading, as measured by different instruments. In this research, the authors propose a new method to develop and evaluate a bridge signature. The signature can be monitored over time and statistically evaluated to detect potential structural deterioration and damage. An instrumentation system was implemented on the Powder Mill Bridge in Barre, Massachusetts, as a research prototype for the development of a structural health monitoring (SHM) system. Heavy truck events due to daily traffic were collected using an automatic measurement system, which triggers above a given threshold of recorded strains. Using the measured strain data due to daily traffic, a bridge signature was created using nonparametric statistical techniques. Maximum experimental strain values from heavy truck eve...

Full-Scale Bridge Finite-Element Model Calibration Using Measured Frequency-Response Functions

AbstractA frequency-response function–based parameter-estimation method was used for finite-element (FE) model calibration of a full-scale bridge using measured dynamic test data. Dynamic tests were performed on the bridge to obtain measured frequency-response functions. Data quality was ensured by comparing measured data with the FE model and removing erroneous data. A coherence data selector was developed to remove the noise-contaminated portions of the measured frequency-response functions. An unrefined FE model was created using design information for the geometry and structural parameters. This model was improved to a refined model by using concrete cylinder property data, as-built drawing geometry, and the addition of components that participate in the dynamic response of the bridge. Simulations were performed using the model to ensure both observability and identifiability of structural parameters. The model of the bridge was then calibrated successfully using measured frequency-response functions....

Objective Load Rating of a Steel-Girder Bridge Using Structural Modeling and Health Monitoring

AbstractThe future of highway infrastructure in the United States is at a critical junction. Nearly one-third of U.S. bridges are nearing the end of their design life, and one in ten bridges is categorized as structurally deficient. While the design and construction of the next generation of U.S. highway bridges is underway, existing bridges must be maintained through proper inspection and load rating. This paper proposes an objective load rating protocol that takes advantage of a shift in the bridge design, construction, and management paradigm to include structural modeling, instrumentation, and nondestructive testing. A baseline structural model is created and verified using structural health monitoring (SHM) data collected during a controlled static load test. The structural model is then used to calculate load rating factors of the bridge at both current and simulated damaged conditions. The resulting load rating factors are compared with the AASHTO load resistance factor rating method.