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This study focuses on the assessment of magnetic Hall sensors’ orientation-based application through magnetic flux leakage excitation data from a sample target ferromagnetic structure. Initially, we designed a prototype of a rectangular sensor head with ten channel compartments, each equipped with Hall magnetic sensors, to conduct contact-based experiments on a ferromagnetic structural component. The target specimen, a steel plate, was subjected to a strong magnetic field using a Neodymium-35 magnet. Various-sized metal loss anomalies were deliberately induced in the target plate structure. Upon scanning the target specimen at a constant speed, the Hall magnetic sensor collected and stored data on both intact and damaged magnetic flux. To enhance accuracy and extract essential features, the raw flux leakage input underwent sequential signal adjustment steps. Signal averaging was employed to improve the overall data gathered from repeated experiments. The magnetic induction signal curve was analyzed to observe the nature of flux leakage in the magnetic Hall sensors. Frequency analysis and sorted amplitude analysis were conducted to gain a deeper understanding of significant features. Following box plot-based outlier identification, a low-pass filter and fast Fourier transform-based filtering were applied to eliminate unwanted flux leakage. Data detrending and signal-to-noise ratio analysis were employed to assess the baseline variation filtering results for improving the original flux leakage data. To allocate defects, a three-step iteration-based wavelet decomposition was proposed, successfully determining the defect region. Anomaly size estimation was carried out using a support vector machine, yielding promising results.

Tendon damage is a major risk of prestressed structures. Ground anchors are structural elements that introduce high levels of prestress, typically over 1000 kN. Tendon damage can threaten the stability of the structure they support. Particularly, the visual inspection of buried ground anchor tendons is impossible. Thus, assessing tendon damage is essential during its service life. In this study, an embedded tendon damage detection method based on the magnetostriction effect is developed. A parametric study was conducted to optimize the sensor parameters through numerical simulations based on the finite element method (FEM). Based on the results, the elasto-magnetic (EM) sensor was fabricated. Different damage degrees in tendons were measured at room temperature using the fabricated EM sensor. Subsequently, the induced electromotive force (EMF) and magnetic flux density were obtained. The finite element simulation results showed a quadratic relationship between the effective cross-sectional area reduction ratio (ARR) of the specimen due to damage and the peak of magnetic flux density. The experimental results were compared with the simulation results. This study introduces a promising nondestructive evaluation (NDE) method for detecting damage in the embedded tendon of ground anchors and demonstrates a design methodology for EM sensors suitable for the target object.

This study investigates the impact of fly ash on hydration and carbonation processes in carbonation-cured fly ash cements. Cement paste samples with fly ash replacement levels of 0 %, 5 %, 10 % and 30 % underwent carbonation curing for 28 days. Results reveal that the CaO content does not affect the final CO2absorption capacity. Instead, optimal fly ash replacement of 10 % enhances CO2uptake, yielding 52.9 g of CaCO3on the surface after 28 days of curing, compared to 43.4, 43.1 and 48.4 g for 0 %, 5 % and 30 % replaced samples, respectively. Additionally, fly ash incorporation significantly enhances belite reaction and improved CO2binding capacity. However, the reaction extent of alite was lower when exposed to CO2-curing conditions. These findings advance understanding of blended cements in carbonation-curing, facilitating the development of environmentally friendly and durable concrete structures.

Membrane filtration is a technology that removes contaminants or impurities from water by passing water through membranes; it has various applications such as in water and sewage treatment. However, a disadvantage of this method is that it has difficulties responding to cases of accidental water pollution or membrane damage. Therefore, membrane damage should be monitored to prevent the contamination of drinking water. In this study, the membrane integrity test was performed by measuring the high-frequency electromechanical impedance (EMI) using a piezoelectric sensor. An attached piezoelectric sensor can detect changes in the physical properties of the membrane due to inner pressure changes. During the pressure decay test, the pressure drop due to membrane damage can be determined by measuring the EMI through an attached piezoelectric sensor. A real-scale test was performed to verify the proposed technology. During the experiment, 20 measurements were taken at each pressure step to increase reliability. According to the results, membrane damage can be monitored by detecting microscopic pressure drops using our proposed method.

The water industry is increasingly using membrane technology for water treatment, wastewater treatment, and water reuse. However, the damage of membrane causes a drop in removal efficiency and threatens downstream use. Thus, the integrity of membrane should be monitored. To detect damage of membrane, this study used piezoelectric sensor-based high-frequency response measurement method which is used in structural health monitoring. The high-frequency responses can monitor the changes in physical properties of host structures. The pressure decay that caused by membrane damage during pressure decay test can be measured by high-frequency response signals measured using piezoelectric sensor. To verify its applicability, a lab-scale test was performed. The electromechanical impedance and guided wave were measured with different membrane conditions, and the variations in signals were quantified using root-mean-square deviation. Also, the support vector machine was used to classify the condition of membrane. According to the results, the proposed method can classify the operation condition and pressure decay of membrane.

Sulfate attack on concrete induces volumetric changes resulting in a severe damage to the structure. The structural deterioration can be coupled with carbonation in practice, while such coupled effect is relatively unknown. Herein, thermodynamic calculations are used to study the combined effect of sulfate attack and carbonation on slag-blended Portland cement. The results show that the resultant product of sulfate attack varies according to the cations being used. Gypsum and M-S-H are the major products when Mg2+is present in the sulfate solution, while ettringite and gypsum are predominant in the presence of Na+. Higher levels of carbonation are found to accelerate the effects of sulfate attack, while slag replacement tends to lower the carbonation degree required for destabilization and precipitation of certain phases.

High temperature thermal energy storage is important to determine the imbalance between power supply and demand in concentrating solar power. Recently, latent heat storage technique has received considerable attention for enhancing the storage density and ultimately reducing the size of the storage system. This study aims to develop a surfactant-free sol-gel based synthesis protocol for microencapsulating a molten salt (potassium nitrate, KNO3) with titanium dioxide (TiO2) that is thermally stable and robust and investigate the effects of the synthesis parameters on thermal performance and thermal reliability of the molten salt microcapsules. Solvent was observed to determine the sol-gel reaction speed (i.e., TiO2shell formation speed). Acetone is most appropriate for attaining robust TiO2shells against temperature changes. Additionally, the effects of catalyst and precursor were also investigated to determine their optimal amounts in terms of the robustness and sustainability of KNO3@TiO2microcapsules. Morphological characteristics including core-shell structure and chemical composition of KNO3@TiO2microcapsules were analyzed by scanning electron microscopy and Fourier transform infrared spectroscopy. Thermal performance of microcapsules was measured using differential scanning calorimetry in ranges of 250–390 °C. Remarkably high encapsulation ratio and encapsulation efficiency were measured up to 70.3 % and 69.3 %, respectively. To demonstrate outstanding thermal reliability of KNO3@TiO2microcapsules, thermal performance of the microcapsules was investigated via DSC for wide range of ramping (heating/cooling) rates (5, 10, and 20 °C/min) and for 100 repeated thermal cycles. The optimal synthesis recipe was determined to obtain robust and sustainable KNO3@TiO2microcapsules, and furthermore outstanding thermal performance and reliability were achieved at elevated temperature condition up to 400 °C.

The steel cables in long span bridges such as cable-stayed bridges and suspension bridges are critical members which suspend the load of main girders and bridge floor slabs. Damage of cable members can occur in the form of crosssectional loss caused by fatigue, wear, and fracture, which can lead to structural failure due to concentrated stress in the cable. Therefore, nondestructive examination of steel cables is necessary so that the cross-sectional loss can be detected. Thus, an automated cable monitoring system using a suitable NDE technique and a cable climbing robot is proposed. In this study, an MFL (Magnetic Flux Leakage- based inspection system was applied to monitor the condition of cables. This inspection system measures magnetic flux to detect the local faults (LF) of steel cable. To verify the feasibility of the proposed damage detection technique, an 8-channel MFL sensor head prototype was designed and fabricated. A steel cable bunch specimen with several types of damage was fabricated and scanned by the MFL sensor head to measure the magnetic flux density of the specimen. To interpret the condition of the steel cable, magnetic flux signals were used to determine the locations of the flaws and the level of damage. Measured signals from the damaged specimen were compared with thresholds set for objective decision making. In addition, the measured magnetic flux signal was visualized into a 3D MFL map for convenient cable monitoring. Finally, the results were compared with information on actual inflicted damages to confirm the accuracy and effectiveness of the proposed cable monitoring method.

Cost-effective and reliable damage detection models are crucial for successful monitoring of any ancient or modern age engineering structure. Lead Zirconate Titanate (PZT) based electromechanical impedance (EMI) method is emerging as a promising alternate for conventional structural health monitoring (SHM) of various engineering structures. The PZT patches are usually surface bonded and then excited in the presence of electric field to a desired frequency spectrum. The excitations result in prediction of unique frequency dependent electromechanical (EM) admittance signature. Any change in the signature during the monitoring period indicates dis-integrity/ damage in the host structure. However, apart from locating damages, the increase in severity of damages has to be predicted on time to avoid collapse of the entire structure. This paper presents such a model which had effectively predicted the severity of damages along a principle direction of the structure. This was achieved by experimental damage study on plates and subsequent verification by semi numerical 3D model. Statistical root mean square deviation (RMSD) index was used for evaluating the damages made on plates. Additionally, a new frequency proximity index (FPI) was introduced to measure the effectiveness of the model. RMSD measures the changes in height of peaks of signature and FPI scales the frequency spectrum of signature. Thus results of RMSD index and FPI are used as complementary to each other to study damage propagation in a structure.

During the construction of concrete structures, real-time monitoring for their strength development is very crucial to determine the structures' readiness for in-service. However, it is very hard to estimate the compressive strength of the concrete nondestructively and in real-time. To provide the solution for this limitation, this study proposes a guided wavebased concrete strength estimation system using an embedded smart sensor module system. Because the guided waves could not propagate clearly inside the concrete, an embedded smart sensor module system was developed by attaching two piezoelectric ceramic sensors on a thin steel plate that could provide a propagating path for the guided waves. Because the boundary condition of the steel plate is changed according to the variation of the concrete strength, the guided wave signal obtained from the piezoelectric sensors might be changed with a certain pattern affected by the boundary condition. Therefore, the strength of the concrete can be estimated by analyzing the pattern-variations of the guided wave signals. To confirm the feasibility of the proposed methodology, an experimental study using a concrete specimen with the aforementioned embedded smart sensor module system is carried out and the optimized strength estimation equation is derived throughout a regression analysis.

Recently, the longitudinal, shear and surface waves have been very widely used as a kind of ultrasonic wave exploration methods to identify internal defects of metallic structures. The ultrasonic wave-based non-destructive testing (NDT) is one of main non-destructive inspection techniques for a health assessment about nuclear power plant, aircraft, ships, and/or automobile manufacturing. In this study, a noncontact pulsed laser-based flaw imaging NDT technique is implemented to detect the damage of a plate-like structure and to identify the location of the damage. To achieve this goal, the Nd:YAG pulsed laser equipment is used to generate a guided wave and scans a specific area to find damage location. The Nd: YAG pulsed laser is used to generate Lamb wave and piezoelectric sensors are installed to measure structural responses. Ann aluminum plate is investigated to verify the effectiveness and the robustness of the proposed NDT approach. A notch is a target to detect, which is inflicted on the surface of an aluminum plate. The damagesensitive features are extracted by comparing the time of flight of the guided wave obtained from an acoustic emission (AE) sensor and make use of the flaw imaging techniques of the aluminum plate.

This paper reports the application of a non-linear impedance technique under a low-frequency vibration to detect structural defects of contact type such as fatigue crack. If the contact-type damage is developed within the structure due to the low-frequency dynamic load, the vibration can cause the fluctuation of structural impedance nonlinearly because of the contact acoustic nonlinearity (CAN). This nonlinear effect can lead to amplitude modulation and phase modulation of the current flow. The nonlinear characteristics of the structural impedance can be extracted by observing coupled electromechanical impedance of a piezoelectric active sensor and a nonlinear wave modulation spectroscopy. For experiment, a low-frequency vibration is applied to a notched coupon at a certain natural frequency by a shaker so that the nonlinear fatigue crack can be formed artificially at the notch tip. Then, the nonlinear features are extracted based on a self-sensing impedance measurement from a host structure under a low-frequency vibration. Damage metric is established based on the nonlinear fluctuation of the impedance due to the CAN.

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.

A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. Abuilt-inactive sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole-damage 0.5cm in diameter at the web section and transverse cut damage 7.5cm in length and 0.5cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: a) feature I: root mean square deviations (RMSD) of impedance signatures, and b) feature II: sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining damage indices from these two damagesensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes (OSH) were successfully established by the two-step SVM classifier: Damage detection was accomplished by the first step-SVM, and damage classification was carried out by the second step-SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by thirty test patterns prepared in advance from the intact state and two damage states.

This paper presents the results of experimental studies on piezoelectric lead-zirconate–titanate (PZT)-based active damage detection techniques for nondestructive evaluations (NDE) of steel bridge components. PZT patches offer special features suitable for real-time insitu health monitoring systems for large and complex steel structures, because they are small, light, cheap, and useful as built-in sensor systems. Both impedance and Lamb wave methods are considered for damage detection of lab-size steel bridge members. Several damage-sensitive features are extracted: root mean square deviations (RMSD) in the impedances and wavelet coefficients (WC) of Lamb waves, and the times of flight (TOF) of Lamb waves. Advanced signal processing and pattern recognition techniques such as continuous wavelet transform (CWT) and support vector machine (SVM) are used in the current system. Firstly, PZT patches were used in conjunction with the impedance and Lamb waves to detect the presence and growth of artificial cracks on a 1/8 scale model for a vertical truss member of Seongsu Bridge, Seoul, Korea, which collapsed in 1994. The RMSD in the impedances and WC of Lamb waves were found to be good damage indicators. Secondly, two PZT patches were used to detect damage on a bolt-jointed steel plate, which was simulated by removing bolts. The correlation of the Lamb wave transmission data with the damage classified by in and out of the wave path was investigated by using the TOF and WC obtained from the Lamb wave signals. The SVM was implemented to enhance the damage identification capability of the current system. The results from the experiments showed the validity of the proposed methods.