Search Results (418)

Structural Health Monitoring (SHM) is an essential technique for continuously assessing structural conditions using integrated sensor systems during operation. SHM technologies have evolved to address the increasing demand for efficient maintenance strategies in advanced engineering fields, such as civil infrastructure, aerospace, and transportation. However, developing a miniaturized, cost-effective, and multi-sensor solution based on Wireless Sensor Networks (WSNs) remains a significant challenge, particularly for SHM applications in weight-sensitive aerospace structures. To address this, the present study introduces a novel WSN-based Multi-Sensor System (MSS) that integrates multiple sensing capabilities onto a 3 × 3 cm flexible Printed Circuit Board (PCB). The proposed system combines a Piezoelectric Transducer (PZT) for impact detection; a strain gauge for mechanical deformation monitoring; an accelerometer for capturing dynamic responses; and an environmental sensor measuring temperature, pressure, and humidity. This high level of functional integration, combined with real-time Data Acquisition (DAQ) and precise time synchronization via Bluetooth Low Energy (LE), distinguishes the proposed MSS from conventional SHM systems, which are typically constrained by bulky hardware, single sensing modalities, or dependence on wired communication. Experimental evaluations on composite panels and aluminum specimens demonstrate reliable high-fidelity recording of PZT signals, strain variations, and acceleration responses, matching the performance of commercial instruments. The proposed system offers a low-power, lightweight, and scalable platform, demonstrating strong potential for on-board SHM in aircraft applications. Full article

With the acceleration of industrialization, deformable mechanisms that can adapt to complex environments have gained widespread applications. Joints serve as carriers for transmitting forces and motions between components, and their stiffness significantly influences the static and dynamic characteristics of deformable mechanisms. A variable stiffness joint is crucial for ensuring the safety and reliability of the system, as well as for enhancing environmental adaptability. However, existing variable stiffness joints fail to meet the requirements for miniaturization, lightweight construction, and fast response. This paper proposes a piezoelectric-actuated variable stiffness miniature rotary joint featuring a compact structure, monitorable loading state, and rapid response. Given that the piezoelectric stack expands and contracts when energized, this paper proposes a transmission principle for stiffness adjustment by varying the pressure and friction between active and passive components. This joint utilizes a flexible hinge mechanism for displacement amplification and incorporates a torque sensor based on strain monitoring. A static model is developed based on piezoelectric equations and displacement amplification characteristics, and simulations confirm the feasibility of the stiffness adjustment scheme. The mechanical characteristics of various flexible hinge structures are analyzed, and the effects of piezoelectric actuation capability and external load on stiffness adjustment are examined. The experimental results demonstrate that the joint can adjust stiffness, and the sensor is calibrated using the least squares algorithm to monitor the stress state of the joint in real time. Full article

Background: Charcot-Marie-Tooth (CMT) disease is a hereditary motor and sensory neuropathy that affects foot morphology and gait patterns, potentially leading to abnormal plantar pressure distribution. This systematic review synthesizes the existing literature examining plantar pressure characteristics in CMT patients. Methods: A comprehensive search was conducted across PubMed, Scopus, and Web of Science databases. Risk of bias was assessed using the Newcastle–Ottawa Scale. Results: Six studies comprising 146 patients were included. Four studies employed dynamic baropodometry, and two used in-shoe pressure sensors to evaluate the main plantar pressure parameters. The findings were consistent across different populations and devices, with a characteristic plantar-pressure profile of marked midfoot off-loading with peripheral overload at the forefoot and rearfoot, often accompanied by a lateralized center-of-pressure path and a prolonged pressure–time exposure. These alterations reflect both structural deformities and impaired neuromuscular control. Interventional studies demonstrated a load redistribution of pressure after corrective surgery, though residual lateral overload often persists. Conclusions: Plantar pressure mapping seems to be a valuable tool to identify high-pressure zones of the foot in order to personalize orthotic treatment planning, to objectively monitor disease progression, and to evaluate therapeutic efficacy. Further longitudinal studies with standardized protocols are needed to confirm these results. Full article

On 5 September 2022, a Mw 6.6 earthquake occurred in Luding, Sichuan Province, China. The epicenter of this earthquake was located in the vicinity of Mt. Gongga. The China Earthquake Administration employed the Global Navigation Satellite System (GNSS) to conduct concurrent deformation field monitoring of the main fault associated with the Luding earthquake. The research area surrounding Mt. Gongga exhibits intricate structural and dynamic processes. However, previous studies have lacked a comprehensive three-dimensional analysis of the uplift mechanism of Mt. Gongga. This study utilizes GNSS data to constrain simulations and employs the FLAC3D numerical model to simulate the primary fault movement during the earthquake and the subsequent changes in the uplift of Mt. Gongga. These investigations are supported by seismic analysis, mechanical analysis, and inversion studies, facilitating the formulation of its uplift mechanism. The results indicate the following: (1) The seismic source analysis of the earthquake reveals a steep dip angle of the primary fault plane, with a predominant inclination toward the northeast. (2) Numerical simulations demonstrate a consistent correlation between the horizontal displacement pattern and the arcuate structure of the Sichuan–Yunnan block, promoting the counterclockwise uplift of Mt. Gongga. The vertical displacement pattern indicates that this earthquake accelerated the overall uplift of Mt. Gongga. (3) Mt. Gongga undergoes a multiple coupling uplift mechanism characterized by “clockwise uplift + rotational flexure + asthenospheric upwelling”. Seismic analysis, mechanical analysis and the results of numerical inversion serve as a useful basis for understanding the uplift of Mt. Gongga and for understanding high mountain uplift in orogen-foreland systems in general. Full article

To elucidate evolutionary characteristics of the surrounding rock failure mechanism in a double-roadway layout, this work is grounded on in the research context of the Jinjitan Coal Mine, focusing on the deformation and failure mechanisms of double roadways. This paper addresses the issue of resource wastage resulting from the excessive dimensions of coal pillars in prior periods by employing a research methodology that integrates theoretical analysis, numerical simulation, and field monitoring to systematically examine the movement characteristics of overlying rock in the working face. On that basis, the size of coal pillar is optimized. The advance’s stress transfer law and deformation distribution characteristics of the return air roadway and transport roadway are studied. The cause of the asymmetric deformation of roadway retention is explained. A differentiated design is conducted on the support parameters of double-roadway bolts and cables under strong dynamic pressure conditions. The study indicates that a 16 m coal pillar results in an 8 m elastic zone at its center, balancing stability with optimal resource extraction. In the basic top-sloping double-block conjugate masonry beam structure, the differing stress levels between the top working face’s transport roadway and the lower working face’s return air roadway are primarily due to the varied placements of key blocks. In the return air roadway, floor heave deformation is managed using locking anchor rods, while roof subsidence is controlled with a constant group of large deformation anchor cables. The displacement of surrounding rock increases under the influence of both leading and lagging pressures from the previous working face, although the change is minimal. There is a significant correlation between roadway deformation and support parameters and coal pillar size. With a 16 m coal pillar, differential support of the double roadway lowers the return air roadway deformation by 30%, which improves the mining rate and effectively controls the deformation of the roadway. Full article

During prolonged deployment, deep-sea acoustic submerged buoys may undergo displacement and torsional deformation of their main floating body under turbulent flows, which degrades the quality of acquired sensor data and introduces vibration-induced noise that interferes with acoustic measurements. This paper presents a novel structural design for acoustic buoy main bodies based on hydrodynamic principles. We performed fluid-structure interaction (FSI) simulations to evaluate the dynamic response characteristics of the structure in deep-sea conditions, including computational analysis of velocity and pressure field distributions surrounding the buoy. Leveraging pressure data derived from computational fluid dynamics (CFD) simulations, we developed an innovative vibration noise quantification methodology. This approach employs plane wave excitation with equivalent pressure magnitude to simulate hydrodynamic loading effects while incorporating tripartite coupling mechanisms among fluid, structural, and acoustic domains. The simulated vibration noise profiles establish environmental baseline noise levels for onboard acoustic monitoring instruments, thereby enhancing measurement fidelity. Full article

Cardiovascular disease (CVD) is the leading cause of global mortality, with hypertension affecting over one billion people. Current noninvasive blood pressure (BP) systems, like cuffs, suffer from discomfort and placement errors and lack continuous monitoring. Wearable solutions promise improvements, but technologies like photoplethysmography (PPG) and bioimpedance (BIOZ) face usability and clinical accuracy limitations. PPG is sensitive to skin tone and body mass index (BMI) variability, while BIOZ struggles with electrode contact and reusability. We present a novel, strain gauge-based wearable BP device that directly quantifies pressure via a dual transducer system, compensating for tissue deformation and external forces to enable continuous, accurate BP measurement. The reusable, energy-efficient, and compact design suits long-term daily use. A novel leg press protocol across 10 subjects (systolic: 71.04–241.42 mmHg, diastolic: 53.46–123.84 mmHg) validated its performance under dynamic conditions, achieving mean absolute errors of 2.45 ± 3.99 mmHg (systolic) and 1.59 ± 2.08 mmHg (diastolic). The device showed enhanced robustness compared to the Finapres, with less motion-induced noise. This technology significantly advances current methods by delivering continuous, real-time BP monitoring without reliance on electrodes, independent of skin tone, while maintaining a high accuracy and user comfort. Full article

To address the issue of insufficient prediction accuracy in traditional GM(1,1) models caused by significant nonlinear fluctuations in time-series data for ancient building structural health monitoring, this study proposes a wavelet decomposition-based GM(1,1)-BP neural network coupled prediction model. By constructing a multi-scale fusion framework, we systematically resolve the collaborative optimization between trend prediction and detail modeling. The methodology comprises four main phases: First, wavelet transform is employed to decompose original monitoring sequences into time-frequency components, obtaining low-frequency trends characterizing long-term deformation patterns and high-frequency details reflecting dynamic fluctuations. Second, GM(1,1) models are established for the trend extrapolation of low-frequency components, capitalizing on their advantages in limited-data modeling. Subsequently, BP neural networks are designed for the nonlinear mapping of high-frequency components, leveraging adaptive learning mechanisms to capture detail features induced by environmental disturbances and complex factors. Finally, a wavelet reconstruction fusion algorithm is developed to achieve the collaborative optimization of dual-channel prediction results. The model innovatively introduces a detail information correction mechanism that simultaneously overcomes the limitations of single grey models in modeling nonlinear fluctuations and enhances neural networks’ capability in capturing long-term trend features. Experimental validation demonstrates that the fused model reduces the Root Mean Square Error (RMSE) by 76.5% and 82.6% compared to traditional GM(1,1) and BP models, respectively, with the accuracy grade improving from level IV to level I. This achievement provides a multi-scale analytical approach for the quantitative interpretation of settlement deformation patterns in ancient architecture. The established “decomposition-prediction-fusion” technical framework holds significant application value for the preventive conservation of historical buildings. Full article

Ground-based synthetic aperture radar (GBSAR) has been widely used in the fields of early warning of geologic hazards and deformation monitoring of engineering structures due to its characteristics of high spatial resolution, zero spatial baseline, and short revisit period. However, in the continuous monitoring process of GBSAR, due to the sudden failure of radar equipment, such as power failure, or the influence of alternating work between multiple regions, it often leads to discontinuous data collection, and this problem caused by missing data is collectively called “inspection mode”. The problem of missing data in the inspection mode not only destroys the spatial and temporal continuity of the data but also affects the accuracy of the subsequent deformation analysis. In order to solve this problem, in this paper, we propose a data reconstruction method that combines Sage–Husa Kalman adaptive filtering and the Rauch–Tung–Striebel (RTS) smoothing algorithm. The method is based on the principle of Kalman filtering and solves the problem of “model mismatch” caused by the fixed noise statistics of traditional Kalman filtering by dynamically adjusting the noise covariance to adapt to the non-stationary characteristics of the observed data. Subsequently, the Rauch–Tung–Striebel (RTS) smoothing algorithm is used to process the preliminary filtering results to eliminate the cumulative error during the period of missing data and recover the complete and smooth deformation time series. The experimental and simulation results show that this method successfully restores the spatial and temporal continuity of the inspection data, thus improving the overall accuracy and stability of deformation monitoring. Full article

In InSAR processing, optimizing baselines by selecting appropriate interferometric pairs is crucial for ensuring interferogram quality and improving InSAR monitoring accuracy. However, in multi-temporal InSAR processing, the quality of interferometric pairs is constrained by spatiotemporal baseline parameters and surface scattering characteristics. Traditional selection methods, such as those based on average coherence thresholding, consider only a single factor and do not account for the interactions among multiple factors. This study introduces a principal component analysis (PCA) method to comprehensively analyze four factors: temporal baseline, spatial baseline, NDVI difference, and coherence, scientifically setting weights to achieve precise selection of interferometric pairs. Additionally, the GACOS (Generic Atmospheric Correction Online Service) atmospheric correction product is applied to further enhance data quality. Taking the Haikou Phosphate Mine area in Kunming, Yunnan, as the study area, surface deformation information was extracted using the SBAS-InSAR technique, and the spatiotemporal characteristics of subsidence were analyzed. The research results show the following: (1) compared with other methods, the PCA-based interferometric pair optimization method significantly improves the selection performance. The minimum value decreases to 0.248 rad, while the mean and standard deviation are reduced to 1.589 rad and 0.797 rad, respectively, effectively suppressing error fluctuations and enhancing the stability of the inversion; (2) through comparative analysis of the effective pixel ratio and standard deviation of deformation rates, as well as a comprehensive evaluation of the deformation rate probability density function (PDF) distribution, the PCA optimization method maintains a high effective pixel ratio while enhancing sensitivity to surface deformation changes, indicating its advantage in deformation monitoring in complex terrain areas; (3) the combined analysis of spatial autocorrelation (Moran’s I coefficient) and spatial correlation coefficients (Pearson and Spearman) verified the advantages of the PCA optimization method in maintaining spatial structure and result consistency, supporting its ability to achieve higher accuracy and stability in complex surface deformation monitoring. In summary, the PCA-based baseline optimization method significantly improves the accuracy of SBAS-InSAR in surface subsidence monitoring, fully demonstrating its reliability and stability in complex terrain areas, and providing a solid technical support for dynamic monitoring of surface subsidence in mining areas. Full article

As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the progression of localized damage. The accurate modeling and forecasting of deflection are thus essential for effective bridge health monitoring and intelligent maintenance. To address the limitations of traditional methods in handling multi-source data fusion and nonlinear temporal dependencies, this study proposes an enhanced iTransformer-based prediction model, termed LDAiT (LSTM Differential Attention iTransformer), which integrates Long Short-Term Memory (LSTM) networks and a differential attention mechanism for high-fidelity deflection prediction under complex working conditions. Firstly, a multi-source heterogeneous time series dataset is constructed based on wireless sensor network (WSN) technology, enabling the real-time acquisition and fusion of key structural response parameters such as deflection, strain, and temperature across critical bridge sections. Secondly, LDAiT enhances the modeling capability of long-term dependence through the introduction of LSTM and combines with the differential attention mechanism to improve the precision of response to the local dynamic changes in disturbance. Finally, experimental validation is carried out based on the measured data of Xintian Yellow River Bridge, and the results show that LDAiT outperforms the existing mainstream models in the indexes of R², RMSE, MAE, and MAPE and has good accuracy, stability and generalization ability. The proposed approach offers a novel and effective framework for deflection forecasting in complex bridge systems and holds significant potential for practical deployment in structural health monitoring and intelligent decision-making applications. Full article

As an important connecting channel between cities, roads are one of the main elements in urban development infrastructure. The stability evaluation of the roadbed slope runs through the entire life cycle, especially during the operation stage. However, under extreme weather conditions, especially heavy rainfall, the roadbed slope may become unstable, thus endangering operational safety. Therefore, it is necessary to conduct precise dynamic assessments of slope stability. However, due to site limitations, it is often not possible to obtain accurate mechanical parameters of a slope using traditional survey methods when deformation and failure have already occurred. In this study, building upon our existing parameter inversion model, the improved backpropagation genetic algorithm non-dominated sorting genetic algorithm II model (BPGA-NSGA-II), in-depth research was conducted on the selection of key parameters for the model. This study utilized monitoring data to perform an inversion analysis of the real-time mechanical parameters of the slope. Subsequently, the inverted parameters were applied to dynamically assess the stability of the slope. The calculation results demonstrate that the slope safety factor decreased from an initial value of 1.212 to 0.800, which aligns with actual monitoring data. This research provides a scientifically effective method for the dynamic stability assessment of slopes. Full article

With the acceleration of urbanization, the development of urban rail transit networks has become an essential component of modern urban transportation. The construction of new urban rail transit lines often involves crossing existing operational lines, posing significant safety risks and technical challenges. This paper presents a comprehensive study on the safety risk assessment and control measures for the construction of new double-line shield tunnels crossing beneath existing tunnels in complex strata, using the project of Line 5 of the Nanning Urban Rail Transit crossing beneath the existing Line 2 interval tunnel as a case study. This study employs methods such as status investigation, numerical simulation, and field measurement to analyze the construction risks. Key findings include the successful identification and control of major risk sources through refined risk assessment and comprehensive technical measurement. The maximum settlement of the existing tunnel was effectively controlled at −2.55 mm, well within the deformation monitoring control values. This study demonstrates that optimized shield machine selection, improved lining design, interlayer soil reinforcement, the dynamic adjustment of shield parameters, and the precise measurement of shield posture significantly enhance the efficiency of shield tunneling and construction safety. The results provide a valuable reference for the settlement and deformation control of similar projects. Full article

As hydraulic buildings are constantly subjected to complex interactions with water, particularly variations in hydrostatic pressure and temperature, deformation structural behavior is inherently sensitive to environmental fluctuations. Monitoring dam deformation with high accuracy and robustness is critical for ensuring the long-term safety and operational integrity of hydraulic structures. However, traditional physics-based models often struggle to fully capture the nonlinear and time-dependent deformation responses in hydraulic structures driven by such coupled environmental influences. To address these limitations, this study presents an advanced deep learning (DL)-based deformation monitoring for hydraulic buildings using long-sequence monitoring data of hydrostatic pressure and temperature. Specifically, the Bidirectional Stacked Long Short-Term Memory (Bi-Stacked-LSTM) is proposed to capture intricate temporal dependencies and directional dynamics within long-sequence hydrostatic and thermal time series. Then, hyperparameters, including the number of LSTM layers, neuron counts in each layer, dropout rate, and time steps, are efficiently fine-tuned using the Gaussian Process-based surrogate model optimization (GP-SMO) algorithm. Multiple deformation monitoring points from hydraulic buildings and a variety of advanced machine-learning methods are utilized for analysis. Experimental results indicate that the developed GP-SMO-optimized Bi-Stacked-LSTM dam deformation monitoring model shows better comprehensive representation capability of both past and future deformation-related sequences compared with benchmark methods. By approximating the behavior of the target function, the GP-SMO algorithms allow for the optimization of critical parameters in DL models while minimizing the high computational costs typically associated with direct evaluations. This novel DL-based approach significantly improves the extraction of deformation-relevant features from long-term monitoring data, enabling more accurate modeling of temporal dynamics. As a result, the developed method offers a promising new tool for safety monitoring and intelligent management of large-scale hydraulic structures. Full article

Fused Filament Fabrication (FFF) has become a widely adopted additive manufacturing technology due to its cost-effectiveness, material versatility, and accessibility. However, optimizing process parameters, predicting material behavior, and ensuring structural reliability remain major challenges. This review analyzes state-of-the-art computational methods used in FFF, which are categorized into four main areas: melt flow dynamics, cooling and solidification, thermal–mechanical behavior, and material property characterization. Notably, the integration of Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) has led to improved predictions of key phenomena, such as filament deformation, residual stresses, and temperature gradients. The growing use of fiber-reinforced filaments has further enhanced mechanical performance; however, this also introduces added complexity due to filler orientation effects and interlayer adhesion issues. A critical limitation across existing studies is the lack of standardized experimental validation methods, which hinders model comparability and reproducibility. This review highlights the need for unified testing protocols, more accurate multi-physics simulations, and the integration of AI-based process monitoring to bridge the gap between numerical predictions and real-world performance. Addressing these gaps will be essential to advancing FFF as a precise and scalable manufacturing platform. Full article