John P. Caffrey

Monitoring civil structures with a wireless sensor network

Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a buildings structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.

A vision-based approach for the direct measurement of displacements in vibrating systems

This paper reports the results of an analytical and experimental study to develop, calibrate, implement and evaluate the feasibility of a novel vision-based approach for obtaining direct measurements of the absolute displacement time history at selectable locations of dispersed civil infrastructure systems such as long-span bridges. The measurements were obtained using a highly accurate camera in conjunction with a laser tracking reference. Calibration of the vision system was conducted in the lab to establish performance envelopes and data processing algorithms to extract the needed information from the captured vision scene. Subsequently, the monitoring apparatus was installed in the vicinity of the Vincent Thomas Bridge in the metropolitan Los Angeles region. This allowed the deployment of the instrumentation system under realistic conditions so as to determine field implementation issues that need to be addressed. It is shown that the proposed approach has the potential of leading to an economical and robust system for obtaining direct, simultaneous, measurements at several locations of the displacement time histories of realistic infrastructure systems undergoing complex three-dimensional deformations.

Structural damage detection and localization using NETSHM

Structural health monitoring (SHM) is an important application area for wireless sensor networks. SHM techniques attempt to autonomously detect and localize damage in large civil structures. Structural engineers often implement and test SHM algorithms in a higher level language such as C/Matlab. In this paper, we describe the design and evaluation of NETSHM, a sensor network system that allows structural engineers to program SHM applications in Mat-lab or C at a high level of abstraction. In particular, structural engineers do not have to understand the intricacies of wireless networking, or the details of sensor data acquisition. We have implemented a damage detection technique and a damage localization technique on a complete NETSHM prototype. Our experiments on small and medium-scale structures show that NETSHM is able to detect and localized damage perfectly with very few false-positives and no false negatives, and that it is robust even in realistic wireless environments

Identification of the state equation in complex non-linear systems

Abstract Building on the basic idea behind the Restoring Force Method for the non-parametric identification of non-linear systems, a general procedure is presented for the direct identification of the state equation of complex non-linear systems. No information about the system mass is required, and only the applied excitation(s) and resulting acceleration are needed to implement the procedure. Arbitrary non-linear phenomena spanning the range from polynomial non-linearities to the noisy Duffing–van der Pol oscillator (involving product-type non-linearities and multiple excitations) or hysteretic behavior such as the Bouc–Wen model can be handled without difficulty. In the case of polynomial-type non-linearities, the approach yields virtually exact results for sufficiently rich excitations. For other types of non-linearities, the approach yields the optimum (in least-squares sense) representation in non-parametric form of the dominant interaction forces induced by the motion of the system. Several examples involving synthetic data corresponding to a variety of highly non-linear phenomena are presented to demonstrate the utility as well as the range of validity of the proposed approach.

Classification and Prioritization of Essential Systems in Hospitals under Extreme Events

This paper presents a classification and prioritization of nonstructural systems, including medical equipment, in hospitals based upon the results of extensive surveys of effects of major seismic events. Surveys included damage surveys, interviews of medical and administrative personnel, and solicitation of expert opinion. As part of a larger study on nonstructural mitigation in hospitals, this effort sought to identify the importance and interdependence of various nonstructural systems subjected to earthquakes and other extreme events. Focused information was obtained for the 1994 Northridge earthquake. Additional information was obtained from experiences in the 1995 Kobe earthquake, the 1999 Chi-Chi earthquake, and the 1999 Kocaeli earthquake. Survey results led to a prioritized list of hospital nonstructural systems that can aid mitigation efforts in maximizing the continued functionality of essential medical facilities when exposed to extreme events.

Comparison of Modeling Approaches for Full-scale Nonlinear Viscous Dampers

A study is presented comparing several identification approaches, both parametric and nonparametric, for developing reduced-order nonlinear models of full-scale nonlinear viscous dampers commonly used with large flexible bridges. Such models are useful for incorporation into large-scale computational models, as well as for use as part of structural health monitoring studies based on vibration signature analysis. The paper reports the analysis results from a large collection of experimental tests on a 1112 kN (250 kip) orifice viscous damper under a wide range of frequency and amplitude oscillations. A simplified parametric design model is used in the parametric phase, as well as two different nonparametric methods: the Restoring Force Method, and artificial neural networks. The variations of model parameters with the excitation and response characteristics are investigated, and the relative accuracy and fidelity of the modeling approaches are compared and evaluated.

Application of Orthogonal Decomposition Approaches to Long-Term Monitoring of Infrastructure Systems

The long-range monitoring of civil infrastructure systems monitored with dense sensor arrays that are capable of generating voluminous amounts of data from continuous online monitoring requires the implementation of a proper data processing and archiving scheme to maximize the benefits of structural health monitoring operations. This paper focuses on the areas of data management, data quality control, and feature extraction of meaningful parameters to describe the response of large-scale infrastructure systems to ambient excitation in the context of structural health monitoring (SHM). Recordings from the monitoring system installed on the Vincent Thomas Bridge (VTB) in San Pedro, California form the database of the proposed data-management and archiving methodology. The data processing methodology for the VTB is based on the calculation of the sensor array acceleration covariance matrices for every hour of available data and the subsequent orthogonal decomposition of the covariance matrices. The dominant proper orthogonal modes of the bridge are determined, and their statistical variations over an extended observation period covering several months of continuous data are quantified and analyzed. The empirical probability density functions for the mean daily bridge accelerations are computed and used to compare the statistical variations in different periods of operation of the bridge (working days, weekends, holidays). It is shown that the computed statistical distributions of the bridge response can provide a quantitative baseline through which to facilitate the early detection of any anomalies indicative of a possible structural deterioration resulting from fatigue (service loads) or extreme loading events, i.e., earthquakes, artificial hazards, or other natural hazards.

Stochastic Nonparametric Models of Uncertain Hysteretic Oscillators

A study is presented of the significant issues encountered in the modeling and characterization of uncertainties in the parameters of hysteretic nonlinear systems that are subjected to deterministic excitations. A single-degree-of-freedom system, with bilinear hysteretic characteristics, is employed to investigate the propagation of uncertainties in the dominant parameters that control the idealized restoring force associated with such a system. Random variations are introduced in the nominal value of each of the dominant parameters. Monte Carlo simulation approaches are used to generate a large, statistically significant, ensemble of time history records that are subsequently used to determine the distribution of the corresponding transient response and establish the probabilistic bounds on the response time history. The restoring force method is used to determine the power-series coefficients that define an approximating surface that characterizes the system behavior. The statistics of the identified coefficients are determined and shown to provide a powerful tool for quantifying the level and features of the uncertainty in the nonlinear system. Furthermore, it is shown that the general methodology presented allows the estimation through analytical procedures of the uncertain systems response bounds when it is excited by a different dynamic load than the one used to identify it.

A System Identification and Change Detection Methodology for Stochastic Nonlinear Dynamic Systems

In this paper a component‐level detection methodology for system identification and change detection is discussed. The methodology is based on non‐parametric, data‐driven, stochastic system identification classifications using statistical pattern recognition techniques. In order to validate the methodology discussed in this paper an experimental study was performed using a complex nonlinear magneto‐rheological (MR) damper. The results of this study show that the proposed methodology is very promising to detect interpret changes in critical structural components such as nonlinear springs joints as well as various types of dampers.

Networked active sensing of structures

Structural Health Monitoring (SHM) focuses on developing technologies and systems for detecting and locating damages in structures such as buildings, bridges, and aerospace structures. SHM techniques typically analyze changes in the structural response induced in a structure (from before and after possible damage) due to ambient (such as heavy winds or passing vehicles) or forced (shakers and impact hammers) excitation sources to detect and locate damages. Untethered wireless sensor network-based structural sensing can significantly drive down cabling installation and maintenance costs while allowing flexible, dense, deployments. Use of wirelessly controlled actuators at various locations in the structure, capable of delivering deterministic excitations can lead to automated SHM sensor-actuator networks that allow for very low-duty cycle operations. We envision autonomous sensor-actuator networks that test structures by periodically exciting them at pre-determined locations and analyzing the structural responses. Such sensor-actuator networks promise to bring about a fundamental paradigm shift in SHM.