Anne S. Kiremidjian

An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge

The authors wish to express their sincere thanks to Dr Charles R. Farrar and Dr Scott W. Doebling of the Los Alamos National Laboratory for providing the experimental data of the Alamosa Canyon Bridge. This research was sponsored by the National Science Foundation under Grant No. CMS- 95261-2 and the National Aeronautics and Space Administration under Grant No. NAG2-1065.

Two-tiered wireless sensor network architecture for structural health monitoring

In this paper, we make a brief study of some of the important requirements of a structural monitoring system for civil infrastructures and explain the key issues that are faced in the design of a suitable wireless monitoring strategy. Two-tiered wireless sensor network architecture is proposed as a solution to these issues and the protocol used for the communication in this network is described. The power saving strategies at various levels, from the network architecture, to communication protocol, to the sensor unit architecture are explained. A detailed analysis of the network is done and the implementation of this network in a laboratory setting is described.

Assembly-Based Vulnerability of Buildings and Its Use in Performance Evaluation

Assembly-based vulnerability (ABV) is a framework for evaluating the seismic vulnerability and performance of buildings on a building-specific basis. It utilizes the damage to individual building components and accounts for the buildings seismic setting, structural and nonstructural design and use. A simulation approach to implementing ABV first applies a ground motion time history to a structural model to determine structural response. The response is applied to assembly fragility functions to simulate damage to each structural and nonstructural element in the building, and to its contents. Probabilistic construction cost estimation and scheduling are used to estimate repair cost and loss-of-use duration as random variables. It also provides a framework for accumulating post-earthquake damage observations in a statistically systematic and consistent manner. The framework and simulation approach are novel in that they are fully probabilistic, address damage at a highly detailed and building-specific level, and do not rely extensively on expert opinion. ABV is illustrated using an example pre-Northridge welded-steel-moment-frame office building.

Embedding damage detection algorithms in a wireless sensing unit for operational power efficiency

A low-cost wireless sensing unit is designed and fabricated for deployment as the building block of wireless structural health monitoring systems. Finite operational lives of portable power supplies, such as batteries, necessitate optimization of the wireless sensing unit design to attain overall energy efficiency. This is in conflict with the need for wireless radios that have far-reaching communication ranges that require significant amounts of power. As a result, a penalty is incurred by transmitting raw time-history records using scarce system resources such as battery power and bandwidth. Alternatively, a computational core that can accommodate local processing of data is designed and implemented in the wireless sensing unit. The role of the computational core is to perform interrogation tasks of collected raw time-history data and to transmit via the wireless channel the analysis results rather than time-history records. To illustrate the ability of the computational core to execute such embedded engineering analyses, a two-tiered time-series damage detection algorithm is implemented as an example. Using a lumped-mass laboratory structure, local execution of the embedded damage detection method is shown to save energy by avoiding utilization of the wireless channel to transmit raw time-history data.

Statistical Analysis of Bridge Damage Data from the 1994 Northridge, CA, Earthquake

This paper presents the significant findings from a study on damage to bridges during the January 17, 1994 Northridge, CA earthquake. The damage and repair cost data were compiled in a database for bridges in the Greater Los Angeles area. Observed damage data for all bridges were discriminated by structural characteristics. The analyses of data on bridge damage showed that concrete structures designed and built with older design standards were more prone to damage under seismic loading. Repair and/or reconstruction of collapsed structures formed seventy five percent of the total estimated repair cost. Peak ground acceleration values were also estimated at all bridge locations as part of this study. Empirical relationships between ground motion and bridge damage, and repair cost ratio were developed in the form of fragility curves and damage probability matrices, respectively. A comparison of the empirical and available ground motion-damage relationships demonstrated that the relationships that are currently in use do not correlate well to the observed damage.

Shadow detection and radiometric restoration in satellite high resolution images

In this paper a new transformation which enables us to detect boundaries of cast shadows in high resolution satellite images is introduced. The transformation is based on color invariant indices. Different radiometric restoration techniques such as gamma correction, linear-correlation correction and histogram matching are introduced in order to restore the brightness of detected shadow area

Stochastic modeling of fatigue crack growth

Abstract A stochastic model for the variations of a materials resistance to fatigue crack growth along the path of a crack is integrated for fatigue life. The results are compared against a well known data set and against the predictions of a more conventional probabilistic model. It is shown that it is important to model the statistical correlation of resistances at different points along the crack path, especially for small increments of crack growth.

Time series based structural damage detection algorithm using gaussian mixtures modeling

In this paper, a time series based detection algorithm is proposed utilizing the Gaussian Mixture Models. The two critical aspects of damage diagnosis that are investigated are detection and extent. The vibration signals obtained from the structure are modeled as autoregressive moving average (ARMA) processes. The feature vector used consists of the first three autoregressive coefficients obtained from the modeling of the vibration signals. Damage is detected by observing a migration of the extracted AR coefficients with damage. A Gaussian Mixture Model (GMM) is used to model the feature vector. Damage is detected using the gap statistic, which ascertains the optimal number of mixtures in a particular dataset. The Mahalanobis distance between the mixture in question and the baseline (undamaged) mixture is a good indicator of damage extent. Application cases from the ASCE Benchmark Structure simulated data have been used to test the efficacy of the algorithm. This approach provides a useful framework for data fusion, where different measurements such as strains, temperature, and humidity could be used for a more robust damage decision.

Time series analysis of fatigue crack growth rate data

Abstract Previous treatments of fatigue crack growth rate (FCGR) statistics have sought to determine the best testing and data analysis techniques with which to process fatigue crack growth data. The best techniques were denned as the ones that produced the least scatter in the data. Recent studies suggest that the scatter in FCGR data has physical significance, which must be understood in order to predict the growth of small cracks. This paper presents a stochastic model which treats the materials resistance to fatigue crack growth as a spatial stochastic process evolving along the path of the crack. The parameters of the model are found by a time series analysis which accounts for the statistical correlation that has been observed between adjacent FCGR measurements. The data to be analyzed is assumed to consist of an ensemble of replicate crack growth tests sampled at equal increments of crack growth. The model is shown to be useful for explaining the moderate scatter observed in mid-range ΔK tests. It is also likely to be useful for understanding the large scatter observed in small crack tests.

A review of earthquake occurrence models for seismic hazard analysis

Abstract A large number of probabilistic earthquake occurrence models are currently available for seismic hazard assessment. This paper reviews the basic assumptions of the various models, summarizes their stochastic representations and discusses the parameters needed for applications. While the Poisson model is one of the most commonly used in practice it is limited in its representation of the physical earthquake driving mechanism and in its characterization of distinct seismicity patterns. From comparisons of the various models, it is observed that while the Poisson model may apply to regions characterized by moderate frequent earthquakes, other stochastic representations such as the Markov and semi-Markov models describe the sequences of events more adequately at regions with large infrequent earthquakes. Regions that have unique seismicity patterns such as clustering foreshock-mainshock-aftershock sequences are better represented by other stochastic models. It is found, however, that some of these models are difficult to implement and rather restrictive primarily because they require a considerable amount of additional data for model parameter estimation.