Raspberry Pi Platform Wireless Sensor Node for Low-Frequency Impedance Responses of PZT Interface.

A wireless impedance monitoring system, called SSeL-Pi, is designed to have cheap, mobile, and handy practical features as compared to wired commercial impedance analyzers. A Raspberry Pi platform impedance sensor node is designed to measure signals at a low-frequency range of up to 100 kHz. The low-frequency impedance measurement via the proposed node has been combined with a new PZT interface technique for measuring local responses sensitive to structural damage. The new PZT interface can work as a surface-mounted or embedded sensor, and its local dynamic characteristics are numerically analyzed to pre-determine an effective impedance resonant frequency range of less than 100 kHz. Next, a software scheme was designed to visualize the input/output parameters of the proposed SSeL-Pi system (i.e., Raspberry Pi platform and PZT interface) and automate signal acquisition procedures of the impedance sensor node. The calibration for impedance signals obtained from the proposed system was performed by a series of procedures, from acquiring real and imaginary impedance to adjusting them with respect to a commercial impedance analyzer (HIOKI-3532). The feasibility of the wireless impedance monitoring system was experimentally evaluated for PZT interfaces that were subjected to various compressive loadings. The consistent results analyzed from signals measured by the SSeL-Pi and HIOKI 3532 systems were observed. Additionally, the strong relationships between impedance features (frequency shift and RMSD index) and compressive stresses of the PZT interfaces showed the potential for axial force/stress variation monitoring in real structures using the Raspberry Pi platform impedance sensor node and developed PZT interface.

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways to overcome the above limitation. Besides, the previous corroded bolt detection method has focused on classifying only two classes, clean and fully rusted bolts, and its performance for detecting partially rusted bolts is still questionable. This study presents a deep learning method to identify corroded bolts in steel structures using a mask region-based convolutional neural network (Mask-RCNN) trained on synthesized data. The Resnet50 integrated with a feature pyramid network is used as the backbone for feature extraction in the Mask-RCNN-based corroded bolt detector. A four-step data synthesis procedure is proposed to autonomously generate the training datasets of corroded bolts with different severities. Afterwards, the proposed detector is trained by the synthesized datasets, and its robustness is demonstrated by detecting corroded bolts in a lab-scale steel structure under varying capturing distances and perspectives. The results show that the proposed method has detected corroded bolts well and identified their corrosion levels with the most desired overall accuracy rate = 96.3% for a 1.0 m capturing distance and 97.5% for a 15° perspective angle.

Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring.

In this article, a new capsule-like smart aggregate (CSA) is developed and verified for impedance-based stress monitoring in a pre-determined frequency range of less than 100 kHz. The pros and cons of the existing smart aggregate models are discussed to define the requirement for the improved CSA model. The conceptual design and the impedance measurement model of the capsule-like smart aggregate (CSA) are demonstrated for concrete damage monitoring. In the model, the interaction between the CSA and the monitored structure is considered as the 2-degrees of freedom (2-DOF) impedance system. The mechanical and impedance responses of the CSA are described for two conditions: during concrete strength development and under compressive loadings. Next, the prototype of the CSA is designed for impedance-based monitoring in concrete structures. The local dynamic properties of the CSA are numerically simulated to pre-determine the sensitive frequency bands of the impedance signals. Numerical and experimental impedance analyses are performed to investigate the sensitivity of the CSA under compressive loadings. The changes in the impedance signals of the CSA induced by the compressive loadings are analyzed to assess the effect of loading directions on the performance of the CSA. Correlations between statistical impedance features and compressive stresses are also made to examine the feasibility of the CSA for stress quantification.

Smart PZT-Embedded Sensors for Impedance Monitoring in Prestressed Concrete Anchorage.

This study investigates the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in prestressed concrete (PSC) anchorages. Firstly, the concept of impedance-based damage monitoring for the concrete anchorage is concisely introduced. Secondly, a prototype design of PZT-embedded rebar and aggregate (so-called smart rebar-aggregate) is chosen to sensitively acquire impedance responses-induced local structural damage in anchorage members. Thirdly, an axially loaded concrete cylinder embedded with the smart rebar-aggregate is numerically and experimentally analyzed to investigate their performances of impedance monitoring. Additionally, empirical equations are formulated to represent the relationships between measured impedance signatures and applied compressive stresses. Lastly, an experimental test on a full-scale concrete anchorage embedded with smart rebar-aggregates at various locations is performed to evaluate the feasibility of the proposed method. For a sequence of loading cases, the variation in impedance responses is quantified to evaluate the accuracy of smart rebar-aggregate sensors. The empirical equations formulated based on the axially loaded concrete cylinder are implemented to predict compressive stresses at sensor locations in the PSC anchorage.

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone.

This study investigates the feasibility of smart aggregate (SA) sensors and their optimal locations for impedance-based damage monitoring in prestressed concrete (PSC) anchorage zones. Firstly, numerical stress analyses are performed on the PSC anchorage zone to determine the location of potential damage that is induced by prestressing forces. Secondly, a simplified impedance model is briefly described for the SA sensor in the anchorage. Thirdly, numerical impedance analyses are performed to explore the sensitivities of a few SA sensors in the anchorage zone under the variation of prestressing forces and under the occurrence of artificial damage events. Finally, a real-scale PSC anchorage zone is experimentally examined to evaluate the optimal localization of the SA sensor for concrete damage detection. Impedance responses measured under a series of prestressing forces are statistically quantified to estimate the performance of damage monitoring via the SA sensor in the PSC anchorage.

Piezoelectric Sensor-Embedded Smart Rock for Damage Monitoring in a Prestressed Anchorage Zone.

In this paper, a piezoelectric sensor-embedded smart rock is proposed for the electromechanical impedance monitoring of internal concrete damage in a prestressed anchorage zone. Firstly, a piezoelectric sensor-embedded smart rock is analyzed for impedance monitoring in concrete structures. An impedance measurement model is analyzed for the PZT (lead zirconate titanate)-embedded smart rock under compression in a concrete member. Secondly, a prototype of the smart rock embedded with a PZT sensor is designed in order to ascertain, sensitively, the variations of the impedance signatures induced by concrete damage in an anchorage zone. Thirdly, the performance of the smart rock is estimated from a numerical analysis and experimental tests. Variations in the impedance signals under compressive test cases are analyzed in order to predetermine the sensitive frequency band for the impedance monitoring. Lastly, an experiment on an anchorage zone embedded with the smart rocks and surface-mounted PZT sensors is conducted for the impedance measurement under a series of loading cases. The impedance variations are quantified in order to comparatively evaluate the feasibility of the sensor-embedded smart rock for the detection of internal concrete damage in the anchorage zone. The results show that the internal concrete damage was successfully detected using the PZT-embedded smart rock, thus enabling the application of the technique for anchorage zone health monitoring.