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Recently, novel methods to monitor the strength development of concrete during curing process have been reported based on electro-mechanical impedance measurement using piezoelectric sensors. However, the previous research works could not provide the information about the absolute strength of concrete. In order to estimate the absolute strength directly, an embedded piezoelectric sensor system based strength monitoring technique was proposed in this study. To avoid the degradation of a piezoelectric sensor due to external and internal impacts and/or environmental variations, the piezoelectric sensor soldered with a lead wire is inserted into a small concrete block and then this block is embedded in larger concrete specimen. While the concrete is cured, the electro-mechanical impedance and guided wave signals, self-measured from the embedded piezoelectric sensor, would be changed because those are related to the material properties of the concrete such as the strength and the stiffness. Hence, the strength of concrete can be monitored by analyzing the root-mean-square-deviation (RMSD) of the impedance signals and the amplitude variation of the guided wave signals. Specific equations to estimate the strength of the concrete are derived using a regression analysis based on the features extracted from the signal variations. Finally, to verify the effectiveness of the proposed approach, a series of experimental studies using miscellaneous concrete specimens are conducted and further research issues will be discussed for real-world implementation of the proposed approach.

This study presents the development of a multi-functional wireless sensor node for the impedance-based SHM. The bottom line is to provide multifunctional wireless sensor nodes for low cost and low power excitation/sensing, structural damage detection/sensor self-diagnosis using embedded algorithms, temperature/power monitoring, and energy scavenging. A miniaturized impedance measuring chip is utilized for low cost and low power structural excitation/selfsensing. Then, structural damage detection/sensor self-diagnosis are executed on the on-board microcontroller. Moreover, it can use the harvested power from solar energy to measure and analyze the impedance data. Simultaneously it can monitor temperature and power consumption. In order to validate the feasibility of this multi-functional wireless impedance sensor node, a series of experimental studies have been carried out for detecting loose bolts and crack damages on a lab-scale steel structure as well as on real steel bridge/building structures. As a result, it has been found that the proposed wireless impedance sensor nodes can be effectively used for local health monitoring of structural components and for constructing a low-cost and low-power but multifunctional SHM system as "place and forget" sensors.

A new concept of reference-free damage detection methodology is developed using transfer impedances to detect crack damage in a plate-like structure without using previously collected baseline data. Conventional impedance-based damage detection techniques have been shown to be vulnerable to other types of changes such as temperature variation that may not be relevant to defects of interest. One of potential disadvantages of the conventional techniques is frequent falsealarms due to these undesirable variations that may occur particularly for field applications. In order to reduce these false-alarms, this paper proposes a new methodology that utilizes transfer impedances obtained between two pairs of collocated PZT patches instead of the electromechanical impedance obtained at one PZT patch. The proposed technique seeks Lamb mode conversion effects caused by the presence of crack damage in plate structures. Furthermore, an instantaneous damage classification is carried out by comparing mode conversion energy among several combinations of measured signals without any user-specified threshold or relying on the baseline data. The feasibility of the proposed reference-free methodology using transfer impedances is investigated via a series of experiments conducted on an aluminum plate.

Economic and reliable online health monitoring strategies are very essential for safe operation of civil, mechanical and aerospace structures. Furthermore, a PZT sensor self-diagnostic and validation procedure is quite important issue for successful impedance-based SHM system's implementation. This study presents online sensor self-diagnosis technique over structural health monitoring (SHM) strategies using wireless impedance sensor nodes. The wireless impedance sensor node incorporating a miniaturized impedance measuring chip, a microcontroller, and a radio-frequency (RF) telemetry is equipped with the capabilities for temperature-sensing, multiplexing of several sensors, and local data analysis. A temperature effects-free sensor self-diagnosis algorithm is embedded into the sensor node and its feasibility is examined from the experiments monitoring the integrity of each piezoelectric sensor on a wireless sensor network.

This paper presents novel structural health monitoring techniques for critical members of civil structures using electro-mechanical impedance sensors. The basic concept of this technique is to monitor critical locations of a structure for changes in structural impedance that would indicate imminent damage. In this paper, principal hardware and software issues on this topic are reviewed. An active sensing node incorporating on-board microprocessor and radio frequency telemetry is introduced in a sense of tailoring wireless sensing technology to the impedance method. A data compression algorithm using a principal component analysis is embedded into the on-board chip of the active sensing node. Finally, a method for compensating the temperature effects on the impedance measurements using cross-correlation analysis with effective frequency shifts is presented.

This paper presents several challenging issues on wireless structural health monitoring techniques for critical members of civil infrastructures using piezoelectric active sensors. The basic concept of the techniques is to monitor remotely the structural integrity by observing the impedance variations at the piezoelectric active sensors distributed to critical members of a host structure. An active sensing node incorporating on-board microprocessor and radio frequency telemetry is introduced in a sense of tailoring wireless sensing technology to the impedance method. A data compression algorithm using principal component analysis is embedded into the on-board chip of the active sensing node. The data compression algorithm would promote efficiency in terms of both power management and noise elimination of the active sensor node. Finally, a piezoelectric sensor self-diagnosis issue is touched introducing a new impedance model equation that incorporates the effects of sensor and bonding defects.

A self-contained active sensor system is proposed for the practical use of an electro-mechanical impedance-based structural health monitoring (SHM) technique for civil infrastructures. This system, which consists of a miniaturized impedance measuring chip (AD5933) and a self-sensing macro-fiber composite (MFC) patch, can be a powerful tool for a variety of in-situ SHM applications in civil, mechanical, and aerospace systems, since (1) the AD5933 is low-cost, portable, and readily combined with a wireless telemetry and (2) the MFC patch can be permanently attached to a structure and provide meaningful information regarding the structure's integrity through its self-sensing function. In this study, the effectiveness of the proposed active sensor device has been verified through a series of experimental studies: (a) inspecting loosening bolts in a bolt-jointed structure and (b) detecting corrosion in an aluminum beam.

In this paper, a sensor network system based on an active sensing scheme is introduced to identify cracks which may occur at a welded zone of a steel truss member. The active sensing network system offers special potential for real world applications, as it is light, cheap, and useful as a built-in system. Four pairs of pitch-catch Lamb wave signals are utilized from the active sensing network system. In order to extract damage sensitive features from the dispersive Lamb waves, a robust wavelet transform is applied to the original response signals. Peak values in the wavelet coefficients corresponding to the A0 Lamb mode are only considered for application to the damage index. The root-mean-square change of the peak values due to damage is proposed as a damage index. In addition, a least-square curve-fitting algorithm is applied to the damage indices in order to obtain a practical damage indicator with a threshold value that presents the damage tolerance. Finally, damage localization is carried out by investigating the change rates of the damage index according to each damage step. The applicability of the proposed methods has been demonstrated by an experimental study.

Many researches have been reported on the condition monitoring of civil infrastructures by means of piezoelectric sensors. Most of them made use of the impedance change of the piezoelectric device in relation to the creation of internal damages to the structure. The impedance measurement is a well accepted method in the piezoelectric sensor area, and has been proved by many authors to be useful for civil structure diagnosis. However, the impedance measurement normally requires sophisticated equipment and analysis technology. For more general and wide application of the piezoelectric diagnosis tool, a new methodology is desired to overcome the limitations of the impedance measurement. This paper presents the feasibility of a piezoelectric oscillator sensor to detect the damages in civil infrastructures. The oscillator sensor is composed of an electronic feedback oscillator circuit and a piezoelectric thickness mode vibrator to be attached to the structure of interest. Damage to the structure causes a change in the impedance spectrum of the structure, which results in a corresponding change of the resonant frequency of the structure. The oscillator sensors can instantly detect the frequency change in a very simple manner. Feasibility of the piezoelectric oscillator sensor was verified in this work with a sample aluminum plate where artificial cracks of different depth were imposed in sequence. Validity of the measurement was confirmed through comparison of the experimental data with the results of finite element analyses of the plate with cracks. Performance of the oscillator sensor was also compared with that of its conventional counterpart, i.e. impedance measurement, to manifest the superiority of the oscillator sensor.

This methodological study was designed to replicate three previous studies of depressive symptoms, compare assessment of antepartum depressive symptoms among high-risk pregnant women using three standardized instruments, and evaluate the psychometric properties of the instruments. The sample consisted of 89 high-risk pregnant women treated with bed rest, of whom 37 remained hospitalized at 4 weeks. Depressive symptoms were measured by the Multiple Affect Adjective Checklist Revised (MAACL-R) Dysphoria construct, the Profile of Mood States (POMS) Depression scale, and the Center for Epidemiologic Studies Depression Scale (CES-D) across antepartum hospitalization. Internal consistency, test-retest reliability, and convergent validity were high. Depressive symptoms were high on admission as measured by all three instruments and significantly decreased across time when measured by the MAACL-R and POMS.

In a structure, several types of damage can occur, ranging from micro-cracking to corrosion or loose bolts. This makes identifying the damage difficult with a single mode of sensing. Therefore, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In self-sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this experimental study, a pipeline system under water flow operation was examined to verify the effectiveness and robustness of the proposed structural health monitoring approach. Different types of structural damage were inflicted artificially on the pipeline system. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented by composing a three-dimensional space using the damage indices extracted from the impedance and guided wave features as well as temperature variations. For a more systematic damage classification, several control parameters were optimized to determine an optimal decision boundary for the supervised learning-based pattern recognition. Further research issues are also discussed for real-world implementations of the proposed approach.

The impedance-based structural health monitoring (SHM) method has come to the forefront in the SHM community due to its practical potential for real applications. In the impedance-based SHM method, the selection of optimal frequency ranges plays an important role in improving the sensitivity of damage detection, since an improper frequency range can lead to erroneous damage detection results and provide false positive damage alarms. To tackle this issue, this paper proposes an innovative technique for autonomous selection of damage-sensitive frequency ranges using artificial neural networks (ANNs). First, the impedance signals are obtained in a wide frequency band, and the signals are split into multiple sub-ranges of this wide band. Then, the predefined damage index is evaluated for each sub-range by comparing impedance signals between the intact and the concurrent cases. Here, the cross correlation coefficients (CCs) are used as the predefined damage index. The ANN is constructed and trained using all CC values at multiple frequency ranges as multi-inputs and the real damage severity as the single output for various preselected damage scenarios, so that subsequent damage estimations may be carried out by selecting the governing frequency ranges autonomously. The performance of the proposed approach has been examined via a series of experimental studies to detect loose bolts and cracks induced on real steel bridge and building structures. It is found that the proposed approach autonomously determines the damage-sensitive frequency ranges and can be used for effective evaluation of damage severity in a wide variety of damage cases in real structures.

Economic and reliable online health monitoring strategies are very essential for safe operation of civil, mechanical and aerospace structures. This study presents online structural health monitoring (SHM) techniques using wireless impedance sensor nodes equipped with both functions of structural damage identification and sensor self-diagnosis. The wireless impedance sensor node incorporating a miniaturized impedance measuring chip, a microcontroller and radio-frequency (RF) telemetry is equipped with the capabilities for temperature sensing, multiplexing of several sensors, and local data analysis. The feasibility of the sensor node for structural damage identification is firstly investigated through a series of experimental studies inspecting loosened bolt damage and cut damage cases. Additionally, a temperature effects-free sensor self-diagnosis algorithm is embedded into the sensor node and its feasibility is examined from the experiments monitoring the integrity of each piezoelectric sensor on a wireless sensor network.

References to “most promising practices” in educational programs for students with severe disabilities reflect a mix of social values as well as empirically validated instructional strategies. A literature search and polling of nationally recognized experts in severe disabilities was conducted to generate a listing of 123 items representing Program Quality Indicators. This initial listing was then rated by six groups (a total N = 254) representing various interests and constituencies: (a) behavior therapy experts, (b) experts in severe disabilities, (c) experts in deaf-blind, (d) mental retardation researchers, (e) state directors of special education, and (f) parents involved in advocacy efforts. Mean ratings for each item support the relative values of each of the indicators, and the results of factor analysis of the item ratings revealed five dimensions, which were valued differentially by the six respondent groups. Results are discussed in terms of future efforts to translate most promising practices into typical practices for students with severe disabilities in public school settings.