Search Results (43)

Vision-based structural health monitoring offers a promising alternative to conventional wired sensing systems; however, its adoption is often limited by high hardware costs and computational constraints at sensing nodes. This study presents the design and laboratory validation of a low-cost vision-based system for displacement and strain monitoring using a centralized processing architecture. The proposed system separates image acquisition from computation, where an ESP32-CAM module serves as a lightweight edge node for grayscale image capture and wireless transmission, while computational tasks including displacement tracking, subpixel localization, scale calibration, and strain estimation are performed on a centralized unit. This enables low-cost deployment at USD 60 per node with low power consumption at 1 W. System performance was evaluated through controlled experiments, including a 24 h zero-drift test and quasi-static displacement tests up to 15 μm. Validation against a Linear Variable Differential Transformer (LVDT) shows close agreement, with an absolute error of 2.63 µε and drift within ±2 μm. The system achieves an effective strain range of ±35,000 με. These results demonstrate the potential of low-cost centralized vision-based systems, demonstrating strong potential for practical deployment in structural health monitoring applications. Full article

This study introduces a systematic and optimised methodology for designing Linear Variable Differential Transformer (LVDT) sensors and Voice Coil (VC) actuators, tailored for high-precision applications such as gravitational wave detectors and particle accelerators. Unlike prior studies, which focus primarily on industrial-grade LVDT design frameworks or isolated parameter studies, this work addresses the specific challenges of achieving both enhanced sensor response and actuation force within strict geometric and thermal constraints. Using a custom-developed simulation pipeline based on Finite Element Method Magnetics (FEMM), we evaluate the influence of key design parameters such as coil dimensions, radial gaps, and coil wire diameter on performance metrics such as response and linearity. The novelty of this work lies in its systematic exploration of design trade-offs, such as maximising performance while minimising heat dissipation, and its applicability to high-precision environments. In this work, particular emphasis is placed on the combination of the LVDT and VC functionalities in one unified sensor-and-actuator system designed for gravitational wave detectors. In addition, the methodology and simulation results are validated with experimental measurements of an optimised design, demonstrating a 2.8-fold increase in LVDT response and a 2.5-fold increase in VC actuation force compared to the initial configuration while preserving LVDT linearity and VC force stability. This work represents a significant advance over existing methodologies by offering a structured, scalable design process. Full article

Early-age shrinkage in 3D-printed concrete constitutes a critical applied challenge due to the rapid development of deformations and the absence of conventional reinforcement systems. From a scientific standpoint, a clear knowledge gap exists in materials science concerning the reliable quantification of very small, rapidly evolving strains in fresh and early-age cementitious materials produced by additive manufacturing. This study investigates practical and low-cost alternatives to commercial optical systems for monitoring early-age shrinkage in 3D-printed concrete, a key challenge given the rapid deformation of printed elements and their typical lack of reinforcement. The work focuses on identifying both the most precise method for capturing minor, fast-developing strains and affordable tools suitable for laboratories without access to advanced equipment. Three mixtures with different aggregate types were examined to broaden the applicability of the findings and to evaluate how aggregate selection affects fresh properties, hardened performance, and shrinkage behavior. Shrinkage measurements were carried out using a commercial digital image correlation system, which served as the reference method, along with simplified optical setups based on a smartphone camera and a GoPro device. Additional measurements were performed with laser displacement sensors and Linear Variable Differential Transformer LVDT transducers mounted in a dedicated fixture. Results were compared with the standardized linear shrinkage test to assess precision, stability, and the influence of curing conditions. The findings show that early-age shrinkage must be monitored immediately after printing and under controlled environmental conditions. When the results obtained after 12 h of measurement were compared with the values recorded using the commercial reference system, differences of 19%, 13%, 16%, and 14% were observed for the smartphone-based method, the GoPro system, the laser sensors, and the LVDT transducers, respectively. Full article

Urban railway bridges are critical components of modern transportation networks. Dynamic loads and harsh environments put urban railway bridges at high risk of damage. Conventional vibration-based damage detection approaches often fail to provide sufficient accuracy and robustness under complex urban conditions. To address this limitation, this study introduces a hybrid deep learning framework that integrates a one-dimensional convolutional neural network (1D-CNN) and a recurrent neural network (RNN) for automatic damage detection using Linear Variable Differential Transformer (LVDT) displacement data. Start with the calibration of a finite element model (FEM) of the target bridge, achieved through updating the model parameters to align with field-acquired LVDT data, thereby establishing a robust and reliable baseline representation of the structure’s behaviour. Subsequently, a series of failure and damage scenarios is introduced within the FEM, and the associated dynamic displacement responses are generated to construct a comprehensive synthetic training dataset. These time-series responses serve as input for training a hybrid deep learning architecture, which integrates a one-dimensional convolutional neural network (1D-CNN) for automated feature extraction with a recurrent neural network (RNN) designed to capture the temporal dependencies inherent in the structural response data. Results show rapid convergence and minimal error in single-damage cases, and robust performance in multi-damage conditions on a dataset exceeding 5 million samples; the model attains a mean absolute error of ≈3.2% for damage severity and an average localisation error of <0.7 m. The findings highlight the effectiveness of combining numerical simulation with advanced data-driven approaches to provide a practical, data-efficient, and scalable solution for structural health monitoring in the urban railway context. Full article

Creep through mainly vertical displacement along en echelon ground ruptures within the creeping segment of the West Valley Fault (WVF) in the Luzon Island, Philippines, has been occurring since first documented in the 90s. It is believed to have been triggered by excessive groundwater withdrawal, mainly because of the high rates of slip recorded in the 90s. Near-field displacements measured by locally fabricated linear variable differential transformer (LVDT) and ultrasonic creepmeters are compared with near-field long-term displacements as measured by precise leveling surveys. Though the ultrasonic creepmeter is less accurate in measuring short-term displacement than the LVDT creepmeter, both are reliable in measuring longer-term displacements. Data from creepmeters can reveal association of displacement with seasonal precipitation and correlation between short-term displacement and episodic rainfall. In the case of the WVF’s creeping segment, rainfall episodes and wet seasons do not always result in immediate abrupt displacement changes. Nevertheless, the results of our monitoring with creepmeters underscores the contribution of precipitation in triggering creep, through its effect on the ground and by releasing stored tectonic strain, in the southern region of the WVF’s creeping zone where groundwater withdrawal remains largely unregulated. Continuous monitoring and periodic leveling surveys should continue as creep continues to cause damage and the potential for induced seismicity remains. Full article

Coal-rock dynamic disasters, especially rock bursts, require insight into the spatiotemporal evolution of strain and temperature to clarify failure mechanisms and improve early warning. This study aims to characterize the spatiotemporal evolution of the strain field during brittle rock instability by developing a multi-point Fiber Bragg Grating (FBG) strain–temperature monitoring and inversion method. Multi-directional, multi-location FBG deployment enables real-time reconstruction of strain tensors and temperature at each monitoring point, capturing both surface and internal responses under loading. The strain records resolve four stages—initial smoothing, linear growth, pre-peak nonlinearity, and failure fluctuation—with earlier sensitivity than Linear Variable Differential Transformers (LVDT), enabling finer localization of yielding and microcracking. The FBG sensors capture clear spatial heterogeneity and timing offsets during yielding, supporting instability warning. Temperature results show a slow rise followed by a surge from the end of the elastic stage into the plastic stage, reaching ~1.6 °C before declining; the thermal peak precedes the stress peak by ~0.38 s. Meanwhile, the temperature-field coefficient of variation jumps from <0.15 to >0.25, indicating a transition from diffuse heating to banded localization. Together, these strain–temperature precursors validate the FBG-based method as an effective and reliable approach for early warning of brittle rock instability. Full article

This study explores the potential use of Polyethylene Terephthalate (PET) plastic bottles as void makers in short reinforced concrete columns under pure axial compression. Such a scheme promotes sustainability by decreasing the consumption of concrete and reducing the pollution that comes with the disposal of PET bottles. The experimental component of this study consisted of testing 16 reinforced concrete columns divided into two groups, based on the cross-section dimensions. One group contained eight columns of a length of 900 mm with a net cross-sectional area of about 40,000 mm², while the second group contained eight columns of a length of 1100 mm with a net cross-sectional area of about 62,500 mm². The diameter of the void within the small cross-section group was 100 mm and within the large cross-section group was 265 mm. The experimental program includes pairs of solid and corresponding void specimens with consideration of the size of the longitudinal steel reinforcement, lateral tie spacing, and concrete compressive strength. The tests are conducted using a universal test machine under displacement-controlled loading conditions with the help of strain gauges and Linear Variable differential transformers (LVDTs). The analysis of the test results showed that the columns that were embedded with a small void that occupied about 30% of the core area exhibited reductions of 9% in the ultimate capacity, 14% in initial stiffness, 20% in ductility, and 1% in residual strength. On the other hand, the columns that contained a large void occupying about 60% of the core area demonstrated reductions of 24% in the ultimate capacity, 34% in initial stiffness, and 26% in ductility, although the residual strength was slightly increased by 5%. The reason for the deficiency in the structural response in the latter case is because the void occupied a significant fraction of the concrete core. The theoretical part of this study showed that the ACI 318 code provisions can reasonably predict the uniaxial compressive strength of columns embedded with PET bottles if the void does not occupy more than 30% of the concrete core. This study confirmed that short columns embedded with relatively small voids made from PET bottles and subjected to pure axial compression create a balance between sustainability benefits and a structural performance tradeoff. Full article

To reduce the cast in place work of concrete and realize the industrial production of a bamboo–concrete composite (BCC), innovative connection systems composed of an assembled bamboo–lightweight concrete composite (ABLCC) structure featuring perforated steel plate connectors are presented for use in engineering structures. This study examined the shear performance of connection systems composed of an assembled BCC structure featuring perforated steel plate connectors based on the design and fabrication of three groups of shear connectors with nine different parameters using bamboo scrimber, lightweight concrete, perforated steel plates, and grout. Push-out tests were conducted on these shear connectors. A linear variable differential transformer (LVDT) and digital image correlation (DIC) were utilized for measurements. The test parameters comprised fabrication techniques (assembled and cast-in-place/CIP) and connector size (steel plate thickness). This study investigated mechanical performance indicators, including the failure mode, load–slip relationship, shear stiffness, and shear capacity of the shear connectors. The experimental results showed that the shear connector failure modes involved concrete spalling near the connectors and deformation of the perforated steel plates. The load–slip curves generally included three stages: high slope linear increase, low slope nonlinear increase, and rapid decrease. The shear capacity and stiffness of the assembled shear connectors were 0.84 times and 2.46 times those of the CIP connectors, respectively. The stiffness of the 4 mm steel plate connectors increased by 42% compared to the 2 mm steel plate connectors. Analysis showed that the shear capacity of the BBC primarily consisted of four aspects: the end bearing force of the steel plate, contact friction, and forces due to the influence of tenon columns and the reinforcing impact of through-rebars. This study proposes a simple and suitable formula for obtaining the shear capacity of perforated steel plate connectors in the BCC structure, with the analytical values being in good agreement with the test results. Full article

The linear variable differential transformer is a key component for measuring vibration noise and active vibration isolation. The nonlinear output associated with increased differential displacement in LVDT constrains the measurement range. To extend the measurement range, this paper proposes an advanced Snake Optimization–Tangential Functional Link Artificial Neural Network (ASO-TFLANN) model to extend the linear range of LVDT. First, the Latin hypercube sampling method and the Levy flight method are introduced into the snake optimization (SO) algorithm, which enhances the global search ability and diversity preservation ability of the SO algorithm and effectively solves the common overfitting and local optimal problems in the training process of the gradient descent method. Second, a voltage–displacement test bench is constructed, collecting the input and output data of the LVDT under four different main excitation conditions. Then, the collected input and output data are fed into the ASO-TFLANN model to determine the optimal weight vectors of the tangential functional link Artificial Neural Network (TFLANN). Finally, by comparing with the simulation experiments of several algorithms, it is proven that the ASO proposed in this paper effectively solves the common overfitting and local optimization problems in the training process of the gradient descent method. On this basis, through offline simulation comparison experiments and online tests, it is proven that the method effectively reduces ${ϵ}_{fs}$ while expanding the linear range of LVDT and significantly improves the measurement range, which provides a reliable basis for improving measurement range and accuracy. Full article

Bearings play a crucial role in mitigating loads, maintaining stability, and transferring forces between superstructures and substructures. However, bearing failures caused by external factors can compromise structural safety. Therefore, continuous monitoring of bearing displacement is essential, yet current inspection methods are labor-intensive and unsuitable for long-term management. To address this, researchers have proposed systems such as Linear Variable Differential Transformers (LVDTs) and computer vision-based monitoring methods to track bearing displacement over time. However, reliance on external power sources and complex installation processes has limited their widespread application. This paper proposes an automated monitoring system integrating low-power IoT sensors, computer vision, and cloud computing. The system features an event-driven power mechanism to minimize energy consumption and utilizes vision-based displacement measurement techniques, providing both portability and efficiency. Applied in a real-world setting for nine months, the system successfully enabled the long-term monitoring of bridge bearings. The results demonstrate its effectiveness in overcoming traditional limitations and highlight its potential in supporting automated, data-driven assessments of structural stability. Full article

Long-gauge fiber optic sensors have proven to be valuable tools for structural health monitoring, especially in reinforced concrete (RC) beam structures. While their application in this area has been well-documented, their use in RC columns remains relatively unexplored. This suggests a promising avenue for further research and development. This paper presents a thorough comparison of long-gauge fiber optic sensors and traditional measurement tools when used to monitor RC columns under small eccentric compressive loading. The evaluation focuses on the stability and precision of each sensor type. A monitoring system was developed for laboratory testing to assess the performance of various sensor types under specific conditions. The system incorporated four measurement schemes, utilizing a combination of embedded and surface-mounted long-gauge fiber optic sensors, linear variable differential transformers (LVDTs), and point sensors (strain gauges). Long-gauge fiber optic sensors, securely mounted on the concrete surface near the tensile side, were found to accurately measure both large and small deformations, outperforming LVDTs. Compared to strain gauges and embedded optic sensors, the long-gauge fiber optic sensors demonstrated superior average strain measurement and minimal interference from protective covers. Full article

This paper presents a proposal in which the maximum energy density criterion is used to evaluate the dynamic accuracy of LVDT (Linear variable differential transformer) sensors for applications in the energy industry. The solutions proposed in the paper are based on a mathematical model of the LVDT sensor, represented by its frequency response. The mathematical foundations required for the synthesis of such a model and the formulae and algorithm necessary to determine the maximum energy density for the integral-square error criterion are presented. Numerical and simulation calculations are performed using MathCad 15 and MATLAB R2014a programs. The solutions presented in this paper can constitute a basis for the selection of LVDT sensors for applications in the energy industry, with a view to achieving accurate diagnostic measurements. Full article

The objective of this study is to develop a monitoring algorithm that measures the displacement of concrete structures using light detection and ranging (LiDAR). The suggested method is based on non-contact measurements providing 3D point clouds of the scanning area with high resolution. This overcomes the limitation of traditional contact-type and point-based measurement methods such as linear variable differential transformer (LVDT) and strain gauge. The developed algorithm enables one to track the boundaries of a concrete specimen and measures the vertical or lateral displacement. To demonstrate that displacement in the horizontal and vertical direction can be measured irrespective of the field of view (FOV), two different concrete specimens were constructed where gradually increasing vertical or lateral loads were applied. Then, the displacements were monitored using the set of LVDT and LiDAR for the correlation analysis. The results demonstrated a high accuracy of 98~99% correlation in comparison between LVDT and LiDAR. Full article

This paper presents the optimization of an inductive displacement transducer or linear variable differential transformer (LVDT). The method integrates design software (SolidWorks 2023), simulation tools (COMSOL Multiphysics), and MATLAB. The optimization phase utilizes the non-dominated sorting genetic algorithm (NSGA)-II and -III to fine-tune the geometry configuration by adjusting six inner parameters corresponding to the dimension of the interior components of the LVDT, thus aiming to improve the overall performance of the device. The outcomes of this study reveal a significant achievement in LVDT enhancement. By employing the proposed methodology, the operational range of the LVDT was effectively doubled, extending it from its initial 8 (mm) to 16 (mm). This expansion in the operational range was achieved without compromising measurement accuracy, as all error values for the working range of 0–16 (mm) (NSGA-II with a maximum final relative error of 2.22% and NSGA-III with 2.44%) remained below the imposed 3% limit. This research introduces a new concept in LVDT optimization, capitalizing on the combined power of NSGA-II and NSGA-III algorithms. The integration of these advanced algorithms, along with the interconnection between design, simulation, and programming tools, distinguishes this work from conventional approaches. This study fulfilled its initial objectives and generated quantifiable results. It introduced novel internal configurations that substantially improved the LVDT’s performance. These achievements underscore the validity and potential of the proposed methodology in advancing LVDT technology, with promising implications for a wide range of engineering applications. Full article

Safe drilling and effective fracturing are constant challenges for shale formations. One of the most important influencing factors is the accurate characterization of the deformation and damage caused by inherent lamination and natural fractures. Furthermore, shale formations exhibit fine-scale heterogeneity, which conventional laboratory methods (linear variable differential transformer (LVDT), strain gauges, etc.) cannot distinguish. To overcome these constraints, this research aims to investigate the damage and deformation characteristics of shale samples using three-dimensional digital image correlation (3D-DIC). Under uniaxial and diametrical compression, samples of Wolfcamp, Mancos, and Eagle Ford shale with distinct lamination and natural fractures are evaluated. The 3D-DIC system is utilized for image processing, visualization, and analysis of the shale damage process under varying loads. DIC made quantitative full-field strain maps with load (tension, compression, and shear), showing all the damage process steps and strain localization zones (SLZs). DIC maps are used to quantify damage variables in order to investigate sample damage. Damage variables are used to categorize the damage evolution process of shale specimens into four stages: initial damage, linear elastic, elastic–plastic, and plastic damage. Characterizing shale damage evolution with a strain localization line is more effective because there is more damage there than in the whole sample. Damage variables based on major strain and its standard deviation from the DIC strain map for all tested shale samples follow a similar trend, though diametrical compression variables are greater than uniaxial compression. In both uniaxial and diametrical compression, the Wolfcamp shale was reported to have the highest damage variable, which was measured at 0.37, while the Eagle Ford shale was reported to have the lowest damage variable. This image-based technique is more effective not only for understanding the laminated and naturally fractured rocks but also for predicting the hydraulic fractures that will occur during the stimulation process. Full article