Search Results (135)

Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN. Full article

The need to strengthen the existing reinforced concrete (RC) elements is becoming increasingly crucial for modern cities as they strive to develop resilient and sustainable structures and infrastructures. In recent years, various solutions have been proposed to limit the undesirable effects of corrosion in RC elements. While C-FRP has shown promise in corrosion-prone environments, its use in structural applications is limited by cost, bonding, and anchorage challenges with concrete. To address these, the present research investigates the structural performance of RC beams reinforced with C-FRP bars under static loading using Structural Health Monitoring (SHM) with an Electro-Mechanical Impedance (EMI) system employing Lead Zirconate Titanate (PZT) piezoelectric transducers which are applied to detect damage development and enhance the protection of RC elements and overall, RC structures. This study underscores the potential of C-FRP bars for durable tensile reinforcement in RC structures, particularly in hybrid designs that leverage steel for compression strength. The study focuses on critical factors such as stiffness, maximum load capacity, deflection at each loading stage, and the development of crack widths, all analyzed through voltage responses recorded by the PZT sensors. Particular emphasis is placed on the bond conditions and anchorage lengths of the tensile C-FRP bars, exploring how local confinement conditions along the anchorage length influence the overall behavior of the beams. Full article

The search for new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human respiratory system is electromagnetic sensing, which is based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the electromagnetic signal passing through the body. The results of experiments on recording changes in the level of electromagnetic radiation passing through the human body performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated. Full article

Inductive proximity sensors are relevant in position-sensing applications in many industries but, in order to be used in harsh industrial environments, they need to be immune to electromagnetic interference (EMI). The use of conventional filters to mitigate these perturbations often compromises signal bandwidth, ranging from 100 Hz to 1.6 kHz. We have exploited recent advances in the field of artificial intelligence (AI) to study the ability of neural networks (NNs) to automatically filter out EMI features. This study offers an analysis and comparison of possible NN models (a 1D convolutional NN, a recurrent NN, and a hybrid convolutional and recurrent approach) for denoising EMI-perturbed signals and proposes a final model, which is based on gated recurrent unit (GRU) layers. This network is compressed and optimised to meet memory requirements, so that in future developments it could be implemented in application-specific integrated circuits (ASICs) for inductive sensors. The final RNN manages to reduce noise by 70% (MSE_red) while occupying 2 KB of memory. Full article

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA’s EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress–damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure. Full article

Climate change induces extreme effects with lower-than-designed restoration periods, imposing the necessity of strengthening the structural integrity of existing and mainly older RC structures, which are often demonstrated to be under-reinforced in terms of the shear capacity, mainly due to outdated and old design codes/standards. Thus, finding cost-effective and feasible methods to strengthen RC elements is becoming increasingly important. Thin RC layers for jacketing represent a modern advancement in repairing and retrofitting RC members. In this context, U-shaped mortar jackets were employed to strengthen three shear-critical beams. In addition, a critical aspect in the success of any jacketing method is the degree of bonding and interaction between the original member and the new jacket. Additionally, the performance of these U-shaped jackets was assessed using an Electro-Mechanical-Impedance-based (EMI-based) method using a Piezoelectric-Transducer-enabled (PZT-enabled) technique. The integration of advanced monitoring technologies in retrofitting applications offers valuable insights into the performance and longevity of the retrofit system. Therefore, this study aims to experimentally investigate the cohesion between construction materials and assess the effectiveness of U-shaped jackets. Through the proposed Structural Health Monitoring (SHM) technique, any degradation at the interface or slippage of the retrofitting jacket can be promptly detected, restraining further damage development and potential failure of the structure. Full article

The Environmental Trace Gases Monitoring Instrument (EMI) series are second-generation Chinese spectrometers on board the GaoFen-5 (GF-5) and DaQi-1 (DQ-1) satellites. In this study, a comparative analysis of EMI series data was conducted to determine the daily trend of ozone concentration changes owing to different transit times and to improve the overall quality and reliability of EMI series datasets. The daily EMI total ozone column (TOC) obtained using the Differential Optical Absorption Spectroscopy (DOAS) method were compared to vertical column density (VCD) gathered by the TROPOspheric Monitoring Instrument (TROPOMI). The results from October to November 2023 indicated a fine correlation (R = 0.98) between the daily EMI series data and a fine correlation (R ≥ 0.95) and spatial distribution closely resembling that of the TROPOMI TOCs. Furthermore, the EMI series data fusion results were highly correlated with TROPOMI TOCs (R = 0.99). Since the EMI series instruments had two different overpass times and the volume of available data at same pixel was increased by approximately three-fold, the temporal and spatial resolution was improved a lot. The results indicated that, compared to a single sensor, the EMI series DOAS TOCs generated more accurate and stable global TOC results and also enabled looking at the changes in the intraday TOCs. These outcomes highlight the potential of the EMI instruments for reliably monitoring the ozone variations in polar regions. Full article

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application. Full article

Developing multifunctional flexible composites with high-performance electromagnetic interference (EMI) shielding, thermal management, and sensing capacity is urgently required but challenging for next-generation smart electronic devices. Herein, novel nacre-like aramid nanofibers (ANFs)-based composite films with an anisotropic layered microstructure were prepared via vacuum-assisted filtration and hot-pressing. The formed 3D conductive skeleton enabled fast electron and phonon transport pathways in the composite films. As a result, the composite films showed a high electrical conductivity of 71.53 S/cm and an outstanding thermal conductivity of 6.4 W/m·K when the mass ratio of ANFs to MXene/AgNWs was 10:8. The excellent electrical properties and multi-layered structure endowed the composite films with superior EMI shielding performance and remarkable Joule heating performance, with a surface temperature of 78.3 °C at a voltage of 2.5 V. Additionally, it was found that the composite films also exhibited excellent mechanical properties and outstanding flame resistance. Moreover, the composite films could be further designed as strain sensors, which show great promise in monitoring real-time signals for human motion. These satisfactory results may open up a new opportunity for EMI shielding, thermal management, and sensing applications in wearable electronic devices. Full article

Unmanned aerial vehicles (UAVs) equipped with magnetic airborne detectors (MADs) represent a new combination for underground or undersea magnetic anomaly detection. The electromagnetic interference (EMI) generated by a UAV platform affects the acquisition of weak magnetic signals by the MADs, which brings unique conceptual design difficulties. This paper proposes a systematic and integrated low-EMI design method for small, fixed-wing UAVs. First, the EMI at the MAD is analyzed. Second, sensor layout optimization for a single UAV is carried out, and the criteria for the sensor layout are given. To enhance UAV stability and resist atmospheric disturbances at sea, the configuration is optimized using an improved genetic algorithm. Then, three typical multi-UAV formations are analyzed. Finally, the trajectory is designed based on an analysis of its influence on EMI at the MAD. The simulation results show that the low-EMI design can keep MADs away from the EMI sources of UAVs and maintain flight stability. The thread-like formation is the best choice in terms of mutual interference and search width. The results also reveal the close relationship between the low-EMI design and flight trajectory. This research can provide a reference for the conceptual design and trajectory optimization of small, fixed-wing UAVs for magnetic anomaly detection. Full article

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases. Full article

Structural health monitoring (SHM) is crucial for preventing and detecting corrosion, leaks, and other risks in reinforced concrete (RC) structures, ensuring environmental safety and structural integrity. Optical fiber sensors (OFS), particularly long-period fiber gratings (LPFG), have emerged as a promising method for SHM. Various LPFG sensors have been widely used in SHM due to their high sensitivity, durability, immunity to electromagnetic interference (EMI) and compact size. This review explores recent advancements in LPFG sensors and offers insights into their potential applications in SHM. Full article

Interface bonding conditions between concrete and steel materials play key roles in ensuring the composite effect and load-carrying capacity of concrete–steel composite structures such as concrete-filled steel tube (CFST) members in practice. A method using both surface wave and electromechanical impedance (EMI) measurement for detecting the existence and the location of inaccessible interface debonding defects between the concrete core and steel tube in CFST members using piezoelectric lead zirconate titanate (PZT) patches as actuators and sensors is proposed. A rectangular CFST specimen with two artificially mimicked interface debonding defects was experimentally verified using PZT patches as the actuator and sensor. By comparing the surface wave measurement of PZT sensors at different surface wave travelling paths under both a continuous sinusoidal signal and a 10-period sinusoidal windowed signal, three potential interface debonding defects are quickly identified. Furthermore, the accurate locations of the three detected potential interface debonding defects are determined with the help of EMI measurements from a number of additional PZT sensors around the three potential interface debonding defects. Finally, the accuracy of the proposed interface debonding detection method is verified with a destructive observation by removing the local steel tube at the three detected interface debonding locations. The observation results show that the three detected interface debonding defects are two mimicked interface debonding defects, and an unexpected debonding defect occurred spontaneously due to concrete shrinkage in the past one and a half years before conducting the test. Results in this study indicate that the proposed method can be an efficient and accurate approach for the detection of unknown interface debonding defects in existing CFST members. Full article

Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. In this study, we present an open (design details and software are accessible) and modular system for the collection of EMI data. The instrument proposed allows for the separations between the transmitter to be adjusted and up to four receiving antennas as well as the acquisition frequency (in the range between 3 and 50 kHz) to permit measurements with variable depth of investigation. The sensor provides access to raw data and the software described in this study allows control of the signal processing chain. The design specifications permit apparent conductivity measurements in the range of between 1 mS/m and 1000 mS/m, with a resolution of 1.0 mS/m and with a sampling rate of up to 10 samples per second. The sensor allows for a synchronous acquisition of a time stamp and a location stamp for each data sample. The sensor has a mass of less than 5 kg, is portable and suitable for one-person operation, provides 4 h of operation time on one battery charge, and provides sufficient rigidity for practical field operations. Full article

This study investigates the application of electro-mechanical impedance (EMI) sensing technology to evaluate the hardness of cosmetic creams. Traditional methods, like penetration resistance testing, can be intrusive and disrupt continuous monitoring by impacting internal structures. To overcome this limitation, a piezoelectric sensor is embedded in cosmetic creams to capture EMI signals. This experiment explores varying wax content levels in the creams, establishing correlations between conventional hardness values and EMI signals. The results demonstrate a positive relationship between wax content, hardness values, and the magnitude of EMI resonance peaks. This study emphasizes a robust correlation between established hardness metrics and EMI signals, showcasing the potential of non-destructive testing to drive advancements in cosmetic industry practices. Full article