Structural System Identification of Buildings by a Wave Method Based on a Nonuniform Timoshenko Beam Model

Complex, high‐rate dynamic structures, such as hypersonic air vehicles, space structures, and weapon systems, require structural health monitoring (SHM) methods that can detect and characterize damage or a change in the system’s configuration on the order of microseconds. While high‐rate SHM methods are an area of current research, there are no benchmark experiments for validating these algorithms. This paper outlines the design of an experimental test bed with user‐selectable parameters that can change rapidly during the system’s response to external forces. The test bed consists of a cantilever beam with electronically detachable added masses and roller constrains that move along the beam. Both controllable system changes can simulate system damage. Experimental results from the test bed are shown in both fixed and changing configurations. A sliding mode observer with a recursive least squares parameter estimator is demonstrated that can track the system’s states and changes in its first natural frequency.

This paper presents an overview of current research and development in the field of sensor placement methods in structural health monitoring. Due to the limitations of equipment facilities and cost, the number of sensors to be installed in a structure is relatively few. In many cases, three are always degrees of freedoms in a structure are not easily accessible, or eventually inaccessible. Moreover, actual structures are all continuous and have essentially infinite degrees of freedoms, therefore, it is impossible to instrument on all the degrees of freedoms. The methods of sensor placement are mainly dealing with accelerometers for global dynamic testing, or structural health monitoring. Three major concerns are discussed, the necessary number of sensors to be installed, where to deploy these sensors, and how to evaluate the effectiveness of these deployed sensors. Finally, a new loading dependent sensor placement method is proposed, and verified by the experimental data of a simple cantilever beam structure.

Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring

Summary Identifiability of parameters in structural system identification (SSI) is of primary importance in any SSI method. It depends on the number and the location of the measurements, which is linked with sensor configuration. In this paper, under the framework of SSI by observability method (OM), the number of necessary measurements to identify all parameters of structural system was clarified first. Then, an example was solved step by step to show the lacking constraints among unknowns in SSI by OM. In a frame example, it was found that no measurement set having as many measurements as the number of unknowns was able to identify all parameters. To further understand this phenomenon, the observability of a simply supported beam was analyzed in an exhaustive way using 252 possible measurement sets. Three quarters of these sets were not able to identify all the parameters. In order to solve this issue, for the very first time, SSI by constrained observability method (COM), which appends the nonlinear constraints to SSI by OM, was proposed. With SSI by COM applied, the observability of the structural parameters with respect to the 252 sets was greatly improved. Finally, the efficacy of SSI by COM was verified by a 13-story frame building.

<div class="htmlview paragraph">Structural system identification was applied to small sample problems and to large automotive structures. The technique combines mass and stiffness matrices from a finite element model with experimental mode shapes and natural frequencies to produce improved mass and stiffness matrices. In numerical experiments on small problems the procedure enabled us to identify regions of original finite element models where changes of model parameters were required to bring finite element predictions into better agreement with exact results. Also, node point displacements were calculated for small structures subjected to time-dependent sinusoidal loads, and it was found that predictions of displacements from “improved” models were more accurate than original finite element model predictions when forcing frequencies were within the range of experimental frequencies used with structural system identification calculations. When applied to two large automotive structures. regions within the finite element models identified as candidates for parameter changes to, in one case, bring about a shift in predicted frequencies, and, in the other case, improve correlation with test results, were found to be consistent with our engineering judgement.</div>

Structural Health Monitoring (SHM) seeks to evaluate the condition of a structure by instrumenting the structure with sensors and then using a combination of structural and data analysis. One unifying factor across all structures is the effect of temperature changes on the behavior of the structure. Temperature Driven - Structural Health Monitoring (TD-SHM) considers temperature as the driving force in structural behavior, and consequently the main input in SHM. Temperature as an input is related to strain and displacement as output to form three-dimensional temperature signatures for the structures. These signatures are descriptive of the thermal behavior, as well as sensitive to changes in structural condition. The two main benefits of TDSHM are the applicability to any structure that undergoes daily or seasonal temperature changes, and the possibility to measure temperature as an input for a complete input-output model. This work examines one type of three-dimensional signature: the relationship between changes in temperature with changes in strain and rotation or tilt on a beam-like concrete structure. The three-dimensional figure is fit with a surface to quantify the behavior, and changes in either the goodness of fit or in the fit itself are indicative of changes in structural behavior. These methods are validated using data from a real structure; Streicker Bridge on campus at Princeton University.

The Lamb wave inspection has emerged as a promising method for structural health monitoring and nondestructive testing. However, because of the highly dispersive and multimodal features, the Lamb wave mode separation has become a challenging problem. Based on the dispersion curve analysis, a new signal processing method is proposed in this study to solve this problem. First, a novel function based on the Hessian matrix is constructed to enhance the energy concentration of the dispersion curve in the time–frequency representation to reduce the dispersion effect. Subsequently, the constrained penalty function algorithm is developed for detecting dispersion curves. Finally, a mode reconstruction algorithm is developed to recover Lamb wave modes. The proposed method can separate overlapping wave modes and detect the crack fault by enhancing the time–frequency feature of the Lamb wave signal. Two experiments are carried out to verify the effectiveness of the proposed method for Lamb wave mode separation.

A model is generally not needed for the basic damage identification problem when using the electromechanical impedance-based method of structural health monitoring (SHM). However, modeling becomes necessary when more information is needed for more complex functions of the SHM system, such as estimation of remaining life. In addition, suitable models would aid in more accurately identifying and locating damage and in designing the SHM system. Since impedance-based SHM relies on high frequency excitation of the structure using piezoelectric patches, finite element modeling may not be computationally efficient. In this study, the spectral element method (SEM) is used in combination with electric circuit analysis for impedance modeling. SEM more accurately models higher frequency vibrations than finite element methods since the mass is modeled exactly and it incorporates higher order models more easily. Simulations of sensor multiplexing, high frequency response, and the inclusion of damage are presented. Experimental verification is also included.

Piezoresistive intrinsic sensing methods for structural health monitoring of composites

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

In composite structures, damages are often invisible from the surface and can grow to reach a critical size, potentially causing catastrophic failure of the entire structure. Thus safe operation of these structures requires careful monitoring of the initiation and growth of such defects. Ultrasonic methods using guided waves offer a reliable and cost-effective method for structural health monitoring in advanced structures. Guided waves allow for long monitoring ranges and are very sensitive to defects within their propagation path. In this work, the relevant properties of guided Lamb waves for damage detection in composite structures are investigated. An efficient numerical approach is used to determine their dispersion characteristics, and these results are compared to those from laboratory experiments. The experiments are based on a pitch-catch method, in which a pair of movable transducers is placed on one surface of the structure to induce and detect guided Lamb waves. The specific cases considered include an aluminum plate and an aluminum honeycomb sandwich panel with woven composite face sheets. In addition, a disbond of the interface between one of the face sheets and the honeycomb core of the sandwich panel is also considered, and the dispersion characteristics of the two resultant waveguides are determined. Good agreement between numerical and experimental dispersion results is found, and suggestions on the applicability of the pitch-catch system for structural health monitoring are made.

Non-invasive Diagnostic Methods for Structural Health Monitoring of Concrete Gravity Dams—Case Study of Umiam Dam

Recent development of space systems has highlighted a need for various modes of structural diagnostics. It is expected that development of structural health monitoring (SHM) for space system will improve system’s reliability and reduce operational costs. One promising SHM technology that has already been tested during spaceflight is the electromechanical impedance method. This contribution explains operation and provides theoretic description of the electromechanical impedance method in simple 1D structures. Three damping models were considered for calculation of the electromechanical impedance response primarily to explain considerable damping noticeable in composite materials. Theoretical modeling and experimental testing were conducted for metallic and composite samples. Several examples of laboratory testing of representative space structures using the electro-mechanical impedance method were discussed. Miniaturization of impedance measurement hardware has been demonstrated. Practical approach for characterization of SHM piezoelectric sensor in LEO environment on ISS has been presented.

A novel nonlinear ICA method for structural health monitoring under nonlinear environmental or operational influences

In this article, we present a vibration measurement system based on low-frequency cantilever-based fiber Bragg grating accelerometers (CFAs) for a suspension bridge. Each accelerometer has an end-loaded cantilever beam, specifically tailored to achieve a uniform sensitivity for a frequency range of 0-4 Hz, a suitable detection range for the vibration analysis. In the field test, seven CFAs were installed at specific positions along the deck of a 110-m-long suspension bridge for synchronous multipoint vibration measurements. The reflection spectra of the CFA array were recorded and processed using the pseudo-high-resolution scheme to improve the signal quality and measurement accuracy. Three natural vibration frequencies: 1.15, 1.54, and 3.17 Hz have been identified from the measurement. Following that, the acquired time-domain signals were processed by a digital bandpass filter to retrieve the waveform at each natural frequency to determine the corresponding mode shapes. The results are in agreement with the phase difference between the frequency domain signal for each natural frequency. This investigation has shown the feasibility of the proposed measurement system for determining the mode shapes and dynamic frequency analysis of a suspension bridge. It is a potential method for structural health monitoring for other similar civil structures.