LCR Meter Research Articles

Quantitative Evaluation of Reinforced Concrete Slab Bridges Using a Novel Health Index and LSTM-Based Deterioration Models

The Health Index (HI) serves as an essential tool for assessing the structural and functional condition of bridges, calculated based on the condition of structural components and the serviceability of the bridge. Its primary purpose is to identify the most deteriorated structures in an asset inventory and prioritize those in most urgent need of repair. However, a frequently cited issue is the lack of accurate and objective data, with the determination of the HI often being heavily reliant on expert opinions and engineering judgment. Furthermore, the HI systems used in most countries are dependent on the current state of bridge components, making it challenging to use as a proactive indicator for factors such as the rate of bridge aging. To address this issue, this study introduces a novel HI as a quantitative evaluation metric for reinforced concrete slab bridges and details the process of deriving the HI based on deterioration models. The deterioration models are derived by preprocessing the deterioration data of reinforced concrete (RC) slab bridges, wherein the relationship between time and deterioration is directly employed for training a long short-term memory model. The HI was validated through a case study involving six RC slab bridges, wherein accuracies of >93% were achieved, confirming that the proposed quantitative evaluation methodology can significantly contribute to maintenance decisions for bridges.

Resistance standards with very small calculable AC-DC difference at frequencies up to 2 MHz for the calibration of precision LCR meters

Abstract We have developed novel impedance standards based on thin-film surface mount device (SMD) resistors. Due to the small dimensions of such resistors, the frequency dependence is very small and can be calculated from quantities that can be either measured or numerically calculated. For nominal resistance values in the 10 kΩ range, as for our application, the DC and AC properties of a single thin-film SMD resistor are not sufficient but can be greatly improved by connecting several thin-film SMD resistors in series. Then the calculated frequency dependence at frequencies up to 2 MHz amounts to only a few parts per million of the DC value, which is about four orders of magnitude smaller than for all conventional calculable AC-DC resistors with a similar nominal DC value. A precision inductance-capacitance-resistance (LCR) meter for frequencies up to 2 MHz, which has a resolution and a reproducibility of a few parts per million but a systematic uncertainty of up to 3000 parts per million, is used to measure two very different SMD-based resistance standards. This enables the verification of the model calculation as well as the calibration of the LCR meter and the correction of its systematic effects, both with a relative uncertainty of a few parts per million in the whole frequency range. This boost in precision enables new applications in this frequency range such as the verification of conventional calculable resistance standards, the calibration of impedance standards, and future measurements of the quantum Hall resistance.We have developed novel impedance standards based on thin-film surface mount device

The effect of synthesis techniques on the gas sensing properties of TiO2/SiC/CoFe2O4 nanocomposites as gas sensor

This study investigates the impact of two distinct methodologies on the structural, morphological, and gas sensing properties of TiO2/SiC/CoFe2O4 (TSC) nanocomposites determined using x-ray diffraction (XRD), scanning electron microscopy (SEM), LCR meter, and gas sensing unit respectively. The TiO2/SiC/CoFe2O4 nanocomposites were synthesized using chemical co-precipitation method (C-TSC) and the solid state method (G-TSC). The Scherrer formula was used to calculate the average grain size of C-TSC and G-TSC, which was estimated to be 8 ± 2 nm and 10 ± 2 nm, respectively. The formation of TSC nanocomposites was confirmed by XRD, SEM, and EDX analysis. The response (%) toward ethanol and NH3 gas was tested as a function of flow rate (ppm) and temperature from room temperature (28 °C) to 300 °C. The response (%) was observed to be increasing with increasing temperature and three intermediate temperatures were found. The response and recovery time were also measured with varying gas concentrations. The long-term stability of devices was tested up to 30 days and less variation in result was found, which confirms stability of sensor. The material synthesized using chemical co-precipitation method (C-TSC) shows better properties than G-TSC.

Investigation of the Dielectric Characteristics of Chromium-substituted Zinc and Cobalt Nanoferrite Systems

Abstract The series of Cr-substituted Zn and Co nanoferrite systems having the chemical composition CrxZnFe2–xO4 and CrxCoFe2–xO4 (0.0 ≤ x ≤0.5) were prepared by sol–gel technique. The present investigation aims to compare the impact of Cr3+ concentration on dielectric characteristics of Zn and Co-nanoferrite systems. The investigation of various dielectric characteristics was conducted using an LCR meter at 1kHz frequency and across the frequency range of 100Hz to 1MHz. The measurements were conducted at room temperature. In the case of both ferrite systems, the plots of frequency Vs dielectric constant (ε′) and AC conductivity (σac) show a common dielectric behavior of spinel ferrites. The plots of frequency Vs dissipation factor (D) of Cr- Cr-substituted zinc and Cobalt nanoferrite samples were observed to be unusual and display a relaxation peak at a particular frequency. In the current study, CrxZnFe2-xO4(x=0.2 and 0.3) and CrxCoFe2–xO4 (x=0.3) ferrite samples were found to exhibit very high values of dielectric constant. So these ferrites may be useful for a wide range of applications in electrical storage devices. According to LCR reports, all the Cr3+ substituted ferrite samples in both ferrite systems exhibit higher dielectric constant and AC conductivity values. Possible mechanisms that may be accountable for the outcomes are thoroughly scrutinized in this paper.

Effect of Sr2+ substitution on structural, morphological, electrical and dielectric properties of Ca2MgSi2O7 ceramic

Despite the excellent properties of ceramic materials for electronic devices, this study systematically investigates the significant effects of strontium (Sr2⁺) substitution on the structural, morphological, electrical, and dielectric behavior of Ca2-xSrxMgSi2O7 (x = 0, 0.2, 0.4, 0.6) ceramics. The composite was prepared via a solid-state route and characterized using techniques such as FESEM-EDAX, BET, XRD, Hall Effect measurements, and an LCR meter, respectively. The surface morphology of the structure was initiated to be smooth, compact, dense, island-shaped and porous. It is noted that the grain size reduced (from 1.10 μm to 0.72 μm) while the surface area enlarged (from 90 m2/g to 122 m2/g) with Sr substitution. XRD study showed that all the ceramics samples, after sintering at 1250 °C for 5 h. Higher amount of Sr, enhanced the crystallinity, as validated by the peak intensification. Correspondingly, the electrical resistivity and dielectric permittivity of Ca2-xSrxMgSi2O7 was decreased with Sr substitution. Particularly, the Ca1.6Sr0.4MgSi2O7 unveiled the lowermost electrical resistivity (76 Ω cm) and dielectric permittivity (εr = 848) but the uppermost dielectric loss (tan δ = 1.06) at 1 kHz. The attained outcomes exhibited the appropriateness of these samples for use in capacitor and antennas design.

Evaluation of Load-Bearing Capacity in Japanese Arch Steel Aqueduct Bridge through Structural Redundancy Assessment

Structural redundancy assessment of steel aqueduct bridges is made by the analysis of a case study using the Musota Aqueduct bridge structure: a simply supported steel arch bridge erected in 1973 and a seven- span continuous steel aqueduct bridge. In this paper, as a case study, after the validation of the model, the structural redundancy of the Musota aqueduct bridge in Wakayama City with respect to its load-carrying capacity after the failure of hanging components due to corrosion was investigated. The conventional procedure for the assessment of redundancy makes use of static nonlinear structural analysis. A three-dimensional finite-element model of this bridge was developed to simulate its behavior. The results from the linear analysis are compared with those from the nonlinear analysis to investigate the appropriateness of the former in the evaluation of redundancy. A detailed nonlinear static finite element study is carried out into the hangers' components of the arch bridge in order to clarify the implications involved in the failure of redundancy. Finally, recommendations for prudent bridge maintenance methods are presented based on findings from the investigation.

Deformation and internal force of multitower suspension bridges: Numerical modeling and analytical analysis

An analytical model for predicting the deformation and internal forces of multitower suspension bridges is proposed in this paper. Equilibrium differential equations and deformation coordination equations are established based on deflection theory and coupling analysis of bridge components, in which the vertical stiffness contribution of girders and the longitudinal stiffness of all towers are considered. The equations for the deformation and internal forces of the bridge components, including towers, hangers, cables, and girders, are derived taking into account the live loads to which the multitower suspension bridge is subjected. The equations capture the mechanical characteristics of the bridge components, and the results can be obtained explicitly with the proposed model, notably improving the calculation efficiency. The applicability and efficiency of the proposed model are verified by two finite element model examples featuring two and three towers. A parametric analysis is performed to investigate the impact of major bridge parameters using the proposed model. The analysis results indicate that the longitudinal stiffness ratio of the mid-tower to the main cable plays an important role in the key design indices, including girder deflection and safety factor of anti-slipping of the saddle. A suitable stiffness range for the mid-tower is obtained by the proposed model.

Nonlinear seismic response analysis of long-span railway cable-stayed bridges crossing strike-slip faults

Active faults along railways in the mountainous regions of western China pose significant challenges to bridge safety. To ensure the safe operation of long-span railway bridges under complex geological conditions, this study investigates the synthesis of artificial ground motions for bridges crossing strike-slip faults and analyzes their nonlinear seismic response. First, we develop a theoretical method for simulating high- and low-frequency seismic motions using a finite fault and an equivalent velocity pulse model. Next, using a specific long-span railway cable-stayed bridge as a case study, we construct a nonlinear finite element model with OpenSees software. Finally, we assess the seismic response of key bridge components considering various crossing angles, seismic amplitudes, fault rupture directivity, and fling-step effects. The results show that the crossing angle significantly influences the seismic response, with longitudinal and transverse responses exhibiting opposite patterns. Additionally, the scaling factor of seismic motion significantly affects bridge response. For bridges crossing strike-slip faults, the longitudinal response exhibits a sudden increase in displacement due to instantaneous velocity pulses, while the transverse response shows notable residual displacement influenced by the fling-step effect. However, the critical section curvatures of bridge towers and piers remain within the elastic range across all crossing angles, indicating that controlling large displacement deformations is crucial for the seismic design of bridges crossing strike-slip faults.

Network analysis of resilience, anxiety and depression in clinical nurses

BackgroundResilience is a protective feature against anxiety and depression disorders. However, the precise relationship and structure of resilience and anxiety and depression remain poorly understood. This study sought to investigate the link among resilience’ components and anxiety as well as depression.Methods1,279 clinical nurses were recruited. 10-item Connor-Davidson Resilience Scale, Generalized Anxiety Disorder 7, and Patient Health Questionnaire 9 were employed to evaluate resilience, anxiety, and depression, respectively. The regularized partial-correlation network was generated utilizing data from cross-sectional survey and the bridge expected influence index was utilized to quantify bridge components.ResultsThe rates of anxiety and depression within clinical nurses were 67.3% and 67.2%, accordingly. Four strongest bridge edges appeared in the resilience-anxiety network, like “Adapt to change”- “Fear that something might happen”, and “Stay focused under pressure”- “Uncontrollable worry”. Two strongest bridge edges appeared in the resilience-depression network, like “Adapt to change”- “Concentration difficulties” and “Stay focused under pressure”- “Fatigue”. “Adapt to change” was recognized as bridging nodes in both the resilience-anxiety network and the resilience-depression network.ConclusionsInterventions targeting the bridge component “Adapt to change” within resilience, may mitigate the intensity of anxiety and depression symptoms among clinical nurses.

Quantitative assessment of cracks in concrete structures using active-learning-integrated transformer and unmanned robotic platform

Quantitative assessment of cracks in concrete bridges is crucial for structural health monitoring and digital twin. However, the training of crack segmentation models relies heavily on annotation resources, and their segmentation capabilities are often unsatisfactory in terms of the accuracy of boundary location of thin cracks encountered in practice. In this paper, an active-learning-integrated crack segmentation transformer (ACS-Former) framework is proposed to maximize segmentation performance with limited annotation resources. The two-branch ACS-Former includes (1) a feature pyramid transformer (FPT) for multi-scale crack segmentation and (2) boundary difficulty-aware active learning (BDAL) to select informative images for labeling and incorporation into FPT training. Additionally, an adhesive climbing robot is proposed for image collection of hard-to-access components of large bridges. The on-site operational feasibility and practicability of the proposed ACS-Former and climbing robot are demonstrated by field experiments performed on in-service bridges, including the quantification of cracks narrower than 0.2 mm, as required by engineering codes.

Seismic response study of multi-span continuous beam bridges near faults considering permanent displacement attenuation effects

The fling-step effect caused by near-fault ground motion induces a step-like permanent displacement of the ground surface, which decays sharply with increasing fault distance, leading to varying degrees of ground motion at each supporting point of the bridge structure. In this context, multi-span long-span bridges exhibit echelon displacement of the main beam and a more complex internal force response compared to ordinary bridges. To investigate the seismic response characteristics of multi-span long-span continuous girder bridges under near-fault ground motion, this study is based on the permanent displacement attenuation model. The main bridge of the Hongyazi Yellow River Bridge in Shizuishan City, consisting of 16 spans, is taken as the research object. Using OpenSees software, a finite element model for the dynamic analysis of the multi-span long-span continuous girder bridge was established, and its structural dynamic characteristics were compared with the natural vibration test of the actual bridge. Based on simulated ground motions that consider the permanent displacement attenuation effect and spatial variability, the seismic response of the key components of the near-fault multi-span long-span continuous beam bridge is examined. The results indicate that under near-fault ground motion considering the attenuation effect of permanent displacement, the main girder of multi-span long-span bridges frequently deviates from the axis in different directions, ultimately resulting in residual deformation of the main girder post-earthquake. The permanent displacement attenuation effect increases the maximum bending moment response at the pier bottom and causes uneven internal force distribution in the bridge. Ignoring this effect can lead to overestimation of bearing deformation.

Damage sensing in multi-functional nanocomposite polymer bonded energetics with embedded multi-walled carbon nanotube sensing networks

Abstract This experimental investigation evaluates the strain and damage sensing abilities of multi-walled carbon nanotube (MWCNT) networks embedded in the binder phase of polymer-bonded energetics (PBEs). PBEs are a special class of particulate composite materials that consist of energetic crystals bound by a polymer matrix, wherein the polymer matrix serves to maintain the composite’s shape and form. The structural health monitoring (SHM) approach presented in this work exploits the piezoresistive properties of the distributed MWCNT networks. Major challenges faced during such implementation include the low binder concentrations of PBEs, the presence of conductive/non-conductive particulate phases, the high degree of heterogeneity in the PBE microstructure, and achieving the optimal MWCNT dispersion. In this study, ammonium perchlorate (AP) crystals as the oxidizer, Aluminum grains as the metallic fuel, and Polydimethylsiloxane (PDMS) as the binder are used as the constituents for fabricating PBEs. To study the effect of each constituent on the MWCNT network’s SHM abilities, various materials systems are comprehensively studied: MWCNT/PDMS materials are first evaluated to study the binder’s electromechanical response, followed by AP/MWCNT/PDMS to assess the impact of AP addition, and finally, AP/AL/MWCNT/PDMS to evaluate the impact of adding conductive aluminum grains. Compression samples (ASTM D695) were fabricated and subjected to monotonic compression. Electrical resistance is recorded in conjunction with the mechanical test via an LCR meter. Gauge factors relating to the change in normalized resistance to applied strain are calculated to quantify the electromechanical response. MWCNT dispersions and mechanical failure modes are analyzed via scanning electron microscopy imaging of the fracture surfaces. Correlations between the electrical behavior in response to the mechanical behavior are presented, and possible mechanisms that influence the electromechanical behavior are discussed. The results presented herein demonstrate the successful ability of MWCNT networks as SHM sensors capable of real-time strain and damage assessment of PBEs.

Study on increasing load capacity of wooden arch bridge by CFRP strengthening: experimental and numerical Verification

The wooden arch corridor bridge is a typical representative of Chinese wooden bridges, holding significant historical research value. Currently, these bridges face issues of severe component deformation and insufficient load-bearing capacity. To address these problems, this study employs CFRP reinforcement on the components of wooden arch corridor bridges to reduce deformation and enhance load-bearing capacity. Experimental research on CFRP reinforcement has yielded the elastic modulus of the bonding interface. Given the lack of an accurate numerical model for wooden arch corridor bridges, this study establishes a precise numerical model by setting parameters based on load test data from wooden arch corridor bridges. The elastic modulus obtained from the experiments is input into the numerical model for analysis. The results indicate that CFRP exhibits excellent reinforcement performance, with the load-bearing capacity of the reinforced damaged components still reaching 75%–85% of their original capacity, while the load-bearing capacity of the reinforced undamaged components increases to 130%–140% of their original capacity. The failure modes of the CFRP-reinforced wooden components suggest that allowing for some gaps in the bonding of CFRP can enhance overall ductility. The application of CFRP to wooden arch corridor bridges also demonstrates favorable reinforcement effects, increasing the load-bearing capacity of the arch surface by approximately 20%, thereby providing a theoretical basis for the reinforcement of wooden arch bridge frameworks.

The Investigation of the Effect of La3+, Eu3+, and Sm3+ Ions on Photoluminescence and Piezoelectric Behavior of RE1.90Y0.10Zr2O7 (RE: Eu, Sm and Y: La, Sm, Eu) Pyrochlore-Based Multifunctional Smart Advanced Materials.

A series of compounds with the general formula RE1.90Y0.10Zr2O7 (where RE includes Eu, Sm and Y includes La, Sm, Eu) were synthesized using a solid-state reaction method. These compounds were analyzed through differential thermal analysis (DTA), thermogravimetric analysis (TG), X-ray powder diffraction (XRD), and scanning electron microscopy (SEM). Additionally, their dielectric constant, loss tangent, piezoelectric charge constant, and Curie temperature were measured using an LCR meter, d33 meter, and TG/DTA. X-ray diffraction results indicated that all samples crystallize in a cubic pyrochlore structure. Photoluminescence studies revealed that Eu3+ ions predominantly contribute to the emission, whether as activators or co-activators. Among the phosphors, Eu1.90La0.10Zr2O7 exhibited a significantly longer afterglow compared to Sm1.90Eu0.10Zr2O7 and Eu1.90Sm0.10Zr2O7. Conversely, Sm1.90Eu0.10Zr2O7 demonstrated luminescence intensity that was 20 times greater than that of Eu1.90La0.10Zr2O7 and Eu1.90Sm0.10Zr2O7. Furthermore, all samples with characteristic Eu3+ emissions also exhibited piezoelectric properties. Curie temperature (Tc) of Eu1.90La0.10Zr2O7, Sm1.90Eu0.10Zr2O7, and Eu1.90Sm0.10Zr2O7 are 770°C, 830°C, and 845°C, respectively. Therefore, Sm3+ ion improves piezoelectric properties and Curie temperature when doping into the Eu2Zr2O7 host crystal.

Multidamage Identification in High-Resolution Concrete Bridge Component Imagery Based on Deep Learning

Multidamage Identification in High-Resolution Concrete Bridge Component Imagery Based on Deep Learning

OPTIMIZATION OF AN INTELLIGENT CONTROLLED BRIDGELESS POSITIVE LUO CONVERTER FOR LOW-CAPACITY ELECTRIC VEHICLES

This paper presents a novel approach to power conversion in electric vehicle (EV) applications. The proposed converter, a Bridgeless Single Stage Positive Luo Converter (BSPLC), is optimized to enhance efficiency and reduce losses in low-capacity EVs. Traditional converters experience higher losses due to their passive components and bridge circuits. By eliminating the bridge components, the converter achieves higher efficiency. An intelligent fuzzy logic controller is employed to provide adaptive control and stabilize output under varying input conditions, improving performance and response time. The converter design is further optimized through parameter tuning and simulation to achieve minimal ripple and maximum power efficiency at 92%. The proposed solution is ideal for low-capacity EVs, as it ensures enhanced power conversion, reduced thermal stress, and improved battery life. The study demonstrates the converter’s capability to meet the growing demands for energy-efficient solutions in modern electric mobility.

Seismic damage assessment of bonded versus unbonded laminated rubber bearings: A deep learning perspective

Laminated Rubber Bearings (LRBs), widely employed in highway bridges worldwide, are vulnerable to shear failure, squeezing or displacing during seismic events, potentially compromising bridge safety. Establishing a reliable approach for assessing the damage of LRBs is crucial for evaluating the seismic performance of bridges and preventing serious damage. To address this concern, this paper proposes a novel approach for LRBs seismic damage assessment based on computer vision (CV) technique. First, quasi-static loading tests are conducted on both bonded and unbonded LRBs to effectively enhance the understanding of their nonlinear properties. Subsequently, a large number of bearing samples are investigated to develop a comprehensive image database for a CV-based damage assessment model. A deep convolutional network (CNN) is designed to segment damage pixels, which are then combined with damage indicators to quantify the impact of various influencing factors on seismic damage using the interpretable machine learning technique, Shapley value. The results demonstrate that the CNN model achieves high-precision pixel segmentation and accurate predictions of seismic damage to LRBs, with RMSE consistently below 0.009 and R2 exceeding 95 %. The primary factor influencing LRB damage is the type of constraint, which interacts with loading characteristics and geometric dimensions to produce various coupled effects. This high-precision CV-based approach shows significant potential for estimating seismic damage states of critical bridge components and building seismic protection.

A parametric approach for creating finite element models from point clouds for steel superstructure components in steel girder bridges

Finite element (FE) analysis is widely used to evaluate the conditions of existing bridges. However, the creation of high-fidelity FE models (FEM) for existing bridges is often a challenging task. In view of the ability of 3D laser scanning to capture detailed geometrical information, this study proposes a direct scan-to-FEM strategy specifically for steel bridge superstructure and hence to facilitate the creation of high-fidelity FE models for existing steel girder bridges. Parametric representations are proposed to address partial occlusions in the point clouds as well as to reinforce the connectivity and alignment between the superstructure elements. Mesh generation algorithms are developed to automatically convert the parametric representations into all-hexahedron FE mesh assembly that can be used directly for FE analysis without any mesh verification or editing. Experimental validations are carried out to evaluate the effectiveness of proposed method in term of both mesh quality and analysis accuracy.

Research on cadmium-injected manganese ferrite made through the sol-gel method, focusing on its structure, magnetic properties, electrical conductivity, and electrochemical behavior

Cadmium was integrated into Manganese ferrite (Mn1-xCdxFe2O4) nanofine particles via the sol-gel method for potential sensing uses. The X-ray diffraction (XRD) revealed that the particles contained a cubic spinel structure and FTIR spectroscopy was employed to identify the metal-oxide (MO) stretching, bending and stretching vibrations. The purity of the samples and the surface feature were scrutinized using an energy dispersive X-ray (EDAX) spectrum and scanning electron microscope (SEM). The magnetic properties were examined as ferromagnetic found through a vibrating sample magnetometer (VSM). An LCR meter gives information on dielectric constants (ε’ & ε’’), loss (tanδ) with AC conductivity (σAC), and cyclic-voltammetry was used to evaluate the electrochemical properties, including voltage/current responses (V/I), Nyquist frequency, voltage/current response to SCR, and phase angle as a function of frequency. From the results the magnetic, dielectric, electrochemical behavior and etc., can be interpreted and discussed extensively.

Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore

Management of ageing masonry arch bridges entails periodic site inspections to identify signs of potential structural degradation. Previous research has focused on detecting surface cracks from images. This paper develops an alternative approach where cracks are identified from point clouds via geometric distortions. An image-based anomaly detection method called PatchCore is customized for 3D applications for this purpose. First, Fast Point Feature Histograms (FPFH) are used to extract geometric features. Then PatchCore is applied on synthetic point clouds with crack labels, generated using 3D finite element modelling (FEM) and graphical modelling. Results show that the proposed method can capture surface and internal cracks in arches. Analyses show that the method is robust against measurement noise, initial damage and masonry surface roughness, and can be applied to other bridge components. Limitations of the method in detecting small changes in curvature and in-plane geometric distortions are highlighted for further improvements.