Search

With the advent of the Fourth Industrial Revolution, the economic, social, and technological demands for pipe maintenance are increasing due to the aging of the infrastructure caused by the increase in industrial development and the expansion of cities. Owing to this, an automatic pipe damage detection system was built using a laser-scanned pipe’s ultrasonic wave propagation imaging (UWPI) data and conventional neural network (CNN)-based object detection algorithms. The algorithm used in this study was EfficientDet-d0, a CNN-based object detection algorithm which uses the transfer learning method. As a result, the mean average precision (mAP) was measured to be 0.39. The result found was higher than COCO EfficientDet-d0 mAP, which is expected to enable the efficient maintenance of piping used in construction and many industries.

Given the explosive growth of information technology and the development of computer vision with convolutional neural networks, wildfire field data information systems are adopting automation and intelligence. However, some limitations remain in acquiring insights from data, such as the risk of overfitting caused by insufficient datasets. Moreover, most previous studies have only focused on detecting fires or smoke, whereas detecting persons and other objects of interest is equally crucial for wildfire response strategies. Therefore, this study developed a multilabel classification (MLC) model, which applies transfer learning and data augmentation and outputs multiple pieces of information on the same object or image. VGG-16, ResNet-50, and DenseNet-121 were used as pretrained models for transfer learning. The models were trained using the dataset constructed in this study and were compared based on various performance metrics. Moreover, the use of control variable methods revealed that transfer learning and data augmentation can perform better when used in the proposed MLC model. The resulting visualization is a heatmap processed from gradient-weighted class activation mapping that shows the reliability of predictions and the position of each class. The MLC model can address the limitations of existing forest fire identification algorithms, which mostly focuses on binary classification. This study can guide future research on implementing deep learning-based field image analysis and decision support systems in wildfire response work.

It has been proposed that pre-stressed concrete bridges improve load performance by inducing axial pre-stress using pre-stress tendons. However, the tensile force of the pre-stress tendons could not be managed after construction, although it directly supports the load of the structure. Thus, the tensile force of the pre-stress tendon should be checked for structural health monitoring of pre-stressed concrete bridges. In this study, a machine learning–based tensile force estimation method for a pre-stressed concrete girder is proposed using an embedded elasto-magnetic sensor and machine learning method. The feedforward neural network and radial basis function network were applied to estimate the tensile force of the pre-stress tendon using the area ratio of the magnetic hysteresis curve measured by the embedded elasto-magnetic sensor. The feedforward neural network and radial basis function network were trained using 213 datasets obtained in laboratory experiments, and trained feedforward neural network and radial basis function network were applied to a 50-m real-scale pre-stressed concrete girder test for estimating tensile force. Nine embedded elasto-magnetic sensors were installed on the sheath, and the magnetic hysteresis curves of the pre-stress tendons were measured during tensioning. The area ratio was extracted and inputted to the trained feedforward neural network and radial basis function network to estimate the tensile force. The estimated tensile force was compared with the reference tensile force measured by the load cell. According to the result, the estimated tensile force can represent the actual tensile force of the pre-stress tendon without calibrating tensile force estimation algorithms at the site. In addition, it can measure the actual friction loss by estimating the tensile force at the maximum eccentric part. Based on the results, the proposed method might be a solution for the structural health monitoring of pre-stressed concrete bridges with field applicability.

As civil engineering structures become larger, non-contact inspection technology is required to measure the overall shape and size of structures and evaluate safety. Structures are easily exposed to the external environment and may not be able to perform their original functions depending on the continuous load for a long time. Therefore, in this study, we propose a method for estimating the vertical displacement of structures using light detection and ranging, which enables non-contact measurement. The point cloud acquired through laser scanning was rearranged into a three-dimensional space, and internal nodes were created by continuously dividing the space. The generated node has its own location information, and the vertical displacement value was calculated by searching for the node where the deformation occurred. The performance of the proposed displacement estimation technique was verified through static loading experiments, and the octree space partitioning method is expected to be applied and utilized in structural health monitoring.

In the construction industry, it is difficult to predict occupational accidents because various accident characteristics arise simultaneously and organically in different types of work. Furthermore, even when analyzing occupational accident data, it is difficult to deduce meaningful results because the data recorded by the incident investigator are qualitative and include a wide variety of data types and categories. Recently, numerous studies have used machine learning to analyze the correlations in such complex construction accident data, however, heretofore the focus has been on predicting severity with various variables, and several limitations remain when deriving the correlations between features from various variables. Thus, this paper proposes a data processing procedure that can efficiently manipulate accident data using optimal machine learning techniques and derive and systematize meaningful variables to rationally approach such complex problems. In particular, among the various variables, the most influential variables are derived through methods such as clustering, chi-square, Cramer&rsquo, s V, and predictor importance, then, the analysis is simplified by optimally grouping the variables. For accident data with optimal variables and elements, a predictive model is constructed between variables, using a support vector machine and decision-tree-based ensemble, then, the correlation between the dependent and independent variables is analyzed through an alluvial flow diagram for several cases. Therefore, a new processing procedure has been introduced in data preprocessing and accident prediction modelling to overcome difficulties from complex and diverse construction occupational accident data, and effective accident prevention is possible by deriving correlations of construction accidents using this process.

In recent scenario, the information about buried objects needed for redevelopment and reorganization of the complicated urban environment. Accidents caused by pipeline damages such as gas, communication and underground electric power lines result in human and financial loss. Therefore, the information of underground obscured materials is essential for smart city realization and construction. GPR (Ground Penetrating Radar) investigation has advantages of high resolution detection, ease of utilization and strong electromagnetic signal to noise ratio when using high frequency. However, the GPR detected image data is not visible and has a problem that it may be interpreted differently based on the approaching skill of the inspector. Therefore, this study was conducted to verify the visualization of detected data using computer vision based on the data from GPR. Canny edge and Harris corner detection were applied to the GPR image data to detect the hyperbolic shape. Using these techniques to enhance visibility will contribute to the reliable result in the buried materials detection.

As high-rise buildings and aging structures increase, researches are underway to effectively inspect structures and prevent accidents in Korea. The structure cannot maintain its original shape due to natural disasters and loads, and deformation occurs. The accidents caused by the deformation of the structure have become a common news in the surroundings. Therefore, this study proposes a noncontact shape management monitoring method that can check structures and detect deformation.

To detect local damage for inspecting the steel chains of the escalator, a magnetic flux leakage (MFL) method was used in this study. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the steel chain. To prepare the damaged steel chain specimens, several amounts of artificial damages were formed on steel chains. The MFL sensor head scanned the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing processes, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analysed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analysed quantitatively by extracting the magnetic features from the MFL signals. For the quantitative analysis, damage indexes based on the enveloped MFL signal and the threshold value were extracted. By using the damage indexes for the MFL method, the detected MFL signals for each damage type were quantified. Finally, multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the information of damage. To analyse the reliability of the MFL based automated steel chain NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage information and the actual damage.

The actual tensile force of pre-stressed (PS) tendons of a pre-stressed concrete (PSC) girder is one of the important factors for evaluating the performance of PSC girder bridges. To measure the tensile force of the PS tendon, this study proposed a radial basis function network (RBFN) based tensile force estimation method using embedded elasto-magnetic (EM) sensors. The magnetic hysteresis of PS tendons are changed according to the applied tensile force. To measure the magnetic hysteresis of PS tendon of PSC girder, the EM sensor should be embedded in the PSC girder because the PS tendons were located in inside of PSC girder. The RBFN was used to estimate the tensile force using the variations in magnetic hysteresis. To verify the proposed method, the in-field tests were performed. The embedded EM sensors were embedded into PSC girder specimen and the magnetic hysteresis changes due to the variations in tensile forces were measured using embedded EM sensors. The tensile forces were estimated using trained RBFN and they compared with reference tensile forces measured by hydraulic jacking machine. According to the measurement results, the proposed method can be a one of the solution to monitor the tensile force of PS tendons.

As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than &plusmn, 10%. However, the result obtained from the TLS were better than those obtained from the DC.

Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, the Republic of Korea, in April 2020. Then, the proposed two-patch-level deep-learning models were implemented. A patch-level 1 network was trained using the UNet++ architecture. The output prediction of this network was used as a position input for the second network, which used UNet. It took the reference position from the first network as its input and refined the results. Finally, the final performance of our proposed method was compared with a state-of-the-art image-segmentation algorithm to prove its robustness. Comparative research on the loss functions was also performed. Our proposed approach demonstrated its effectiveness in extracting burnt areas from UAV images and can contribute to estimating maps showing the areas damaged by forest fires.

To minimize the damage caused by wildfires, a deep learning-based wildfire-detection technology that extracts features and patterns from surveillance camera images was developed. However, many studies related to wildfire-image classification based on deep learning have highlighted the problem of data imbalance between wildfire-image data and forest-image data. This data imbalance causes model performance degradation. In this study, wildfire images were generated using a cycle-consistent generative adversarial network (CycleGAN) to eliminate data imbalances. In addition, a densely-connected-convolutional-networks-based (DenseNet-based) framework was proposed and its performance was compared with pre-trained models. While training with a train set containing an image generated by a GAN in the proposed DenseNet-based model, the best performance result value was realized among the models with an accuracy of 98.27% and an F1 score of 98.16, obtained using the test dataset. Finally, this trained model was applied to high-quality drone images of wildfires. The experimental results showed that the proposed framework demonstrated high wildfire-detection accuracy.

The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it. First, a Q-switched Nd:YAG pulsed laser and an acoustic emission sensor were used as exciting and sensing ultrasonic signals, respectively. Then, a 3D full-field ultrasonic data set was created using an ultrasonic wave propagation imaging (UWPI) process, after which several signal processing techniques were applied to generate the processed data. By using a deep convolutional neural network (DCNN) with a VGG-like architecture based regression model, the estimated error was calculated to compare the performance of a DCNN on different processed data set. The proposed approach was also compared with a K-nearest neighbor, support vector regression, and deep artificial neural network for regression to demonstrate its robustness. Consequently, it was found that the proposed approach shows potential for the incorporation of laser-generated ultrasound and DL algorithms. In addition, the signal processing technique has been shown to have an important impact on the DL performance for automatic looseness estimation.

In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused only on storms, floods, and earthquakes, and they have not used past data. This research proposes a new conceptual framework of an IDSS for disaster management, with particular attention paid to wildfires and cold/heat waves. The IDSS uses big data collected from open application programming interface (API) and artificial intelligence (AI) algorithms to help decision-makers make faster and more accurate decisions. In addition, a simple example of the use of a convolutional neural network (CNN) to detect fire in surveillance video has been developed, which can be used for automatic fire detection and provide an appropriate response. The system will also consider connecting to open source intelligence (OSINT) to identify vulnerabilities, mitigate risks, and develop more robust security policies than those currently in place to prevent cyber-attacks.

Terrestrial laser scanning (TLS) provides a rapid remote sensing technique to model 3D objects but can also be used to assess the surface condition of structures. In this study, an effective image processing technique is proposed for crack detection on images extracted from the octree structure of TLS data. To efficiently utilize TLS for the surface condition assessment of large structures, a process was constructed to compress the original scanned data based on the octree structure. The point cloud data obtained by TLS was converted into voxel data, and further converted into an octree data structure, which significantly reduced the data size but minimized the loss of resolution to detect cracks on the surface. The compressed data was then used to detect cracks on the surface using a combination of image processing algorithms. The crack detection procedure involved the following main steps: (1) classification of an image into three categories (i.e., background, structural joints and sediments, and surface) using K-means clustering according to color similarity, (2) deletion of non-crack parts on the surface using improved subtraction combined with median filtering and K-means clustering results, (3) detection of major crack objects on the surface based on Otsu&rsquo, s binarization method, and (4) highlighting crack objects by morphological operations. The proposed technique was validated on a spillway wall of a concrete dam structure in South Korea. The scanned data was compressed up to 50% of the original scanned data, while showing good performance in detecting cracks with various shapes.

This study estimates the strength of a special mixture of high-strength concrete (HSC) with admixtures for use in a nuclear power plant (NPP). Nuclear power plant structures need a HSC with some additional qualities to operate the safe options. For this purpose, the experimented concrete was specially designed to fulfill the required qualities of NPP. For gaining these desirable qualities, it needs to monitor the concrete strength development process. Here, the PZT materials were used as sensors to acquire data by measuring the electromechanical impedance (EMI), and then cross correlation (CC) was calculated to look at changes according to strength development. Data were measured for 28 days, and over this period concrete can gain up to 96% of its design strength. This technique is based on a single sensor. After casting concrete, the PZT material starts vibrating as an actuator to produce vibrations. At the same time, it also works as a sensor to measure the dynamic response of the structure to the vibrations. With strength development, the resonant frequencies of the EMI start changing. To estimate the strength development, a fuzzy logic tool was used to analyze the parameters, allowing for us to estimate and predict the concrete strength. For cross-checking, the estimated strength was compared with the actual strength of concrete; this was determined by examining cuboid cores taken from specimens during experiments at the 1st, 3rd, 7th, 14th, and 28th days. According to the results, this approach of strength estimation and monitoring the strength development is useful for forecasting the stability of structures.

In this study, the representative data extraction is performed for the shape information model of civil infrastructures. The scan data is extracted by octree data processing. The octree data processing generates a voxel of 3D space which is recursively subdivided into eight sub-voxels. The point cloud of the scan data was converted to voxels and sampled. The experimental site for terrestrial laser scanning is located at Sungkyunkwan University. The scanned structure is a steel girder bridge. For Terrestrial Laser Scanning(TLS), the Leica ScanStation C10 was used. The scan data was condensed 92% and the octree model was constructed with a 2 millimeter resolution. Accuracy verification was carried out in order to confirm that the data characteristic were retained. The objective of this study is to use Octree data processing to reduce large amounts of point clouds. Octree data processing will be the foundation for shape information model of the large structures such as double-deck tunnels, buildings and bridges. The research will be expected to improve the efficiency of shape information model.

It is highly necessary to evaluate strength development during the curing process to ensure the quality of concrete in construction using concrete. In particular, curing strength monitoring at early age is very important to reduce the construction cost and time, because it can provide the information required for the decision-making to safely progress to the next process. In this study, a guided wave-based non-destructive curing strength gain monitoring method that can be used even for early-age concrete is proposed. A steel plate-type piezoelectric sensor module was embedded in the concrete media at the same time as concrete placement to measure the signal from early-age concrete. The guided wave signals were measured continuously using the pitch-catch method at regular intervals. The wavelet transform process was performed to improve the quality of the signal. The guided wave's velocity of each measurement time was varied by extracting the time of flight. The wave velocity hysteresis curve according to ...

Wire ropes used in various applications such as elevators and cranes to safely carry heavy weights are vulnerable to breakage or cross-sectional loss caused by the external environment. Such damage can pose a serious risk to the safety of the entire structure because damage under tensile force rapidly expands due to concentration of stress. In this study, the magnetic flux leakage (MFL) method was applied to diagnose cuts, corrosion, and compression damage in wire ropes. Magnetic flux signals were measured by scanning damaged wire rope specimens using a multi-channel sensor head and a compact data acquisition system. A series of signal-processing procedures, including the Hilbert transform-based enveloping process, was applied to reduce noise and improve the resolution of signals. The possibility of diagnosing several types of damage was verified using enveloped magnetic flux signals. The characteristics of the MFL signals according to each damage type were then analyzed by comparing the extracted damage indices for each damage type. For automated damage type classification, a support vector machine (SVM)-based classifier was trained using the extracted damage indices. Finally, damage types were automatically classified as cutting and other damages using the trained SVM classifier.

Owing to the large ratio of consumption in the building sector, energy-saving strategies are required. Energy feedback is an energy-saving strategy that prompts consumers to change their energy-consumption behaviors. The strategy has been principally focused on providing energy-consumption information. However, the realization of energy savings using only consumption information remains limited. In this paper, a building-energy, three-dimensional (3D) visualization solution is thus proposed. The aim is to determine if the building manager will replace the facility after our recommendation to improve the building-energy efficiency derived from the energy information is given. This solution includes the process of diagnosing a building and providing a prediction of energy requirements if a building improvement effort is undertaken. Accurate diagnostic information is provided by real-time measurement data from sensors and building models using a close-range photogrammetry method, without depending on blueprints. The information is provided by employing visualization effects to increase the energy-feedback efficiency. The proposed strategy is implemented on two testbeds, and building diagnostics are performed accordingly. For the first testbed, the predicted energy improvement amount resulting from the facility upgrade is provided. The second testbed is provided with a 3D visualization of the energy information. The predicted value of energy improvement was derived from the improvement plan through energy diagnosis in each testbed as about 30% and as about 28%, respectively. Unlike existing systems, which provide only ambiguous data that lack quantitative information, this study is meaningful because it provides energy information with the aid of visualization effects before and after building improvements.

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Recently, novel methods to estimate the strength of concrete have been reported based on numerous NDT methods. Especially, electro-mechanical impedance technique using piezoelectric sensors are studied to estimate the strength of concrete. However, the previous research works could not provide the general information about the early-age strength important to manage the quality of concrete and/or the construction process. In order to estimate the early-age strength of concrete, the electro-mechanical impedance method and the artificial neural network(ANN) is utilized in this study. The electro-mechanical impedance varies with the mechanical properties of host structures. Because the strength development is most influential factor among the change of mechanical properties at early-age of curing, it is possible to estimate the strength of concrete by analyzing the change of E/M impedance. The strength of concrete is a complex function of several factors like mix proportion, temperature, elasticity, etc. Because of this, it is hard to mathematically derive equations about strength of concrete. The ANN can provide the solution about early-age strength of concrete without mathematical equations. To verify the proposed approach, a series of experimental studies are conducted. The impedance signals are measured using embedded piezoelectric sensors during curing process and the resonant frequency of impedance is extracted as a strength feature. The strength of concrete is calculated by regression of strength development curve obtained by destructive test. Then ANN model is established by trained using experimental results. Finally the ANN model is verified using impedance data of other sensors.

In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one scale of sensing. Hence, a multi-scale actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-scale actuated sensing, one scale provides a wide frequency-band structural response from the self-sensed impedance measurement and the other scale provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study using the pipeline system under a water flow-operation is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a three-dimensional space using the damage indices extracted from the impedance and guided wave features as well as temperature variation. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learningbased pattern recognition are optimized. Finally, 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.

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.