Search Results (220)

Suffusion is a primary cause of failure in hydraulic structures, including earth dams; however, the mechanisms underlying suffusion-induced failure and the stability changes remain poorly understood. This study derives and implements a sequentially coupled computational model that considers the effect of seepage–suffusion–stress, aimed at simulating the entire process of suffusion-induced failure in earth dams and evaluating their stability. The accuracy of the proposed approach is validated through comparisons with one-dimensional consolidation theory, suffusion experiments, and triaxial tests on eroded soil. A model of the earth dam at high water levels is developed to simulate the full process of suffusion-induced failure and assess its stability. The results indicate that, under the influence of suffusion, fines are lost most rapidly at the dam toe, followed by the region near the upstream water level. In the later stages of suffusion, the soil near the slip surface undergoes excessive compression, leading to an increase in fine content rather than a decrease. The mechanism of suffusion-induced failure in earth dams involves severe fines loss at the dam toe and near the upstream water level, which leads to significant soil weakening and the formation of a continuous plastic zone extending from the dam toe to the upstream water level. The safety factor of the earth dam, when suffusion effects are not considered, remains nearly constant, making it challenging to accurately assess its stability. The safety factor of the earth dam remains nearly constant when suffusion is neglected, indicating that overlooking suffusion presents substantial safety risks. Furthermore, reducing the permeability coefficient of the earth dam can effectively mitigate suffusion. Full article

Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video tampering, underscoring the need for reliable methods to verify video authenticity. Temporal tampering typically involves manipulating frame order through insertion, deletion, or duplication. This paper proposes a computationally efficient framework that transforms high-dimensional video into compact one-dimensional temporal signals and learns tampering patterns using a shallow one-dimensional convolutional neural network (1D-CNN). Five complementary features are extracted between consecutive frames: frame-difference magnitude, structural similarity drift (SSIM drift), optical-flow mean, forward–backward optical-flow consistency error, and compression-aware temporal prediction error. Per-video robust normalization is applied to emphasize intra-video anomalies. Experiments on a custom dataset derived from D²-City demonstrate strong detection performance in single-attack settings: 95.0% accuracy for frame deletion, 100.0% for frame insertion, and 95.0% for frame duplication. In a four-class setting (non-tampered, insertion, deletion, duplication), the model achieves 96.3% accuracy, with AUCs of 0.994, 1.000, 0.997, and 0.988, respectively. Efficiency analysis confirms near real-time CPU inference (≈12.7–12.9 FPS) with minimal memory overhead. Cross-dataset tests on BDDA and VIRAT reveal domain-shift sensitivity, particularly for deletion and duplication, highlighting the need for domain adaptation and augmentation. Overall, the proposed multi-feature 1D-CNN provides a practical, interpretable, and resource-aware solution for temporal tampering detection in dashcam videos, supporting trustworthy video forensics in IoT-enabled transportation systems. Full article

This paper investigates the thermal shock response of a spacecraft solar panel. The panel is represented as a thin homogeneous plate. The governing equations are derived from the coupled thermoelasticity theory for a homogeneous medium, combining the heat equation with compressibility effects and the Lamé equations for the displacement vector. The aim of the paper is to analyze new properties of a specific formulation of the coupled thermoelasticity problem and to establish a justified simplification. New properties follow from a specific formulation of the thermoelasticity problem for a real physical object (a solar panel). They are subjective properties of this formulation and allow, in particular, to reduce the coupled thermoelasticity problem to a simpler, uncoupled problem, with certain limitations. This simplification is driven by the physics of the thermal shock process and the resulting plate deformation, which allows the thermal problem to be reduced to a one-dimensional formulation. The main result is a simplified thermoelasticity model that reveals several new properties. Notably, in the region where longitudinal displacements are negligible, the coupled problem generates into an uncoupled one. This result can be applied to model disturbances caused by thermal shock on spacecraft. Full article

Changes in climate and ocean pollution has prioritized monitoring of ocean surface behavior. Ocean drifters, which are floating sensors that record position and velocity, help track ocean dynamics. However, environmental events such as oil spills can cause abnormal behavior, making anomaly detection critical. Unsupervised learning, combined with deep learning and advanced data handling, is used to detect unusual behavior more accurately on the NOAA Global Drifter Program dataset, focusing on regions of the West Coast and the Mexican Gulf, for time periods spanning 2010 and 2024. Using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), pseudo-labels of anomalies are generated to train both a one-dimensional Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The results of the two models are then compared with bootstrapping with block shuffling, as well as 10 trials with bar chart summaries. The results show nuance, with models outperforming the other in different contexts. Between the four spatiotemporal domains, a difference in the increasing rate of anomalies is found, showing the relevance of the suggested pipeline. Beyond detection, data reliability and efficiency are addressed: a RAID-inspired recovery method reconstructs missing data, while delta encoding and gzip compression cut storage and transmission costs. This framework enhances anomaly detection, ensures reliable recovery, and reduces energy consumption, thereby providing a sustainable system for timely environmental monitoring. Full article

To study the sulfate corrosion behavior of potassium magnesium phosphate cement (PMPC) paste, the sulfate content, strength, and length of PMPC specimens were measured at different corrosion ages under 5% Na₂SO₄ solution soaking conditions, and the phase composition and microstructure were analyzed. The conclusion is as follows: In PMPC specimens subjected to one-dimensional SO₄²⁻ corrosion, the relation between the diffusion depth of SO₄²⁻ (h) and the SO₄²⁻ concentration (c (h, t)) can be referred by a polynomial very well. The sulfate diffusion coefficient (D) of PMPC specimens was one order of magnitude lower than Portland cement concrete (on the order of 10⁻⁷ mm²/s). The surface SO₄²⁻ concentration c (0, t), the SO₄²⁻ computed corrosion depth h₀₀, and D of FM2 specimen containing 20% fly ash (FA) were all less than those of the FM0 specimen (reference). At 360-day immersion ages, the c (0, 360 d) and h₀₀ in FM2 were obviously smaller than those in FM0, and the D of FM2 was 64.2% of FM0. The strengths of FM2 specimens soaked for 2 days (the benchmark strength) were greater than those of FM0 specimens. At 360-day immersion ages, the residual flexural/compressive strength ratios (360-day strength/benchmark strength) of FM0 and FM2 specimens were all larger than 95%. The volume linear expansion rates (S_n) of PMPC specimens continued to increase with the immersion age, and S_n of FM2 specimen was only 49.5% of that of the FM0 specimen at 360-day immersion ages. The results provide an experimental basis for the application of PMPC-based materials. Full article

Considering the complexity and diversity of water-rich soft soil strata, indoor triaxial shear tests and creep tests were conducted on soft soil to explore its deformation law and creep characteristics. To address the nonlinear characteristics of soft soil creep, a nonlinear pot element was proposed and substituted for the two linear pot elements in the Burgers model, thus establishing an unsteady parametric Burgers model. The one-dimensional creep equation of the unsteady Burgers model was derived, theoretically determining that the unsteady model can describe three stages of creep. Based on this, the creep equation of the unsteady Burgers model was extended to a three-dimensional stress state, and the triaxial compression creep test curves of Ningbo soft soil were fitted and parameters identified. The above model was derived from a three-dimensional finite difference scheme suitable for numerical solution in FLAC3D. A custom constitutive creep model was developed in FLAC3D, and the non-accelerated creep stage and accelerated creep stage of the improved model were analyzed to verify the accuracy and reliability of the constitutive model. The results show that the numerical simulation results and the indoor creep test results are in good agreement in terms of strain increment and the creep change curve, which confirms the effectiveness and applicability of the proposed unsteady Burgers creep constitutive model and its secondary development application. Full article

Global Navigation Satellite System (GNSS) signal acquisition in high-dynamic environments faces significant challenges due to large Doppler frequency offsets and stringent computational constraints. This paper proposes a frequency-domain compressed acquisition algorithm that reformulates the conventional two-dimensional code-phase/Doppler search as a set of independent one-dimensional sparse recovery problems. Doppler uncertainty is modeled as sparsity in a discretized frequency dictionary, and a low-coherence measurement matrix is designed offline via projected gradient descent with a two-stage annealing strategy. The resulting matrix significantly reduces maximum coherence and supports reliable sparse recovery from a small number of compressed measurements. During online operation, the receiver forms compressed observations for all code phases through efficient matrix operations and recovers sparse Doppler spectra using lightweight orthogonal matching pursuit. Simulation results show that the proposed method achieves a several-fold reduction in computational cost compared with classical parallel code-phase search while maintaining high detection probability at low carrier-to-noise density ratios and under large Doppler offsets, providing an effective solution for resource-constrained GNSS receivers in high-dynamic scenarios. Full article

Firstly, based on one-dimensional Terzaghi consolidation theory, we derived and established the analytical solution of excess pore water pressure and average consolidation degree of double-layered foundation, which can reflect the effect of fiber reinforcement. Meanwhile, the one-dimensional consolidation test of a double-layered foundation was carried out by means of a modified WG-type (product series code) consolidation instrument. The accuracy of the theoretical solution was verified by designing different consolidation parameters of the basalt fiber-reinforced solidified lightweight soil (BF-SLS) layer. Secondly, our findings suggest that the settlement rate of the double-layered foundation decreased with the increase in thickness, compression modulus and fiber mixing ratio of the BF-SLS layer. Nevertheless, the average pore pressure dissipation rate changed in the opposite trend. Both increased with increasing permeability coefficient of the BF-SLS layer. Within the thickness ratio range of 0 to 1/2 between the upper and lower layers, the thickness of the BF-SLS layer significantly influenced the consolidation process of the double-layer foundation. At equivalent T_v levels, the difference in consolidation degree exceeded 60%. Finally, a comparison of various simplified methods for calculating the average consolidation degree of double-layer foundations reveals that neither the weighted consolidation coefficient method nor the average index method yields results that are in good agreement with theoretical solutions. The difference between U_s (defined by sedimentation) and U_p (defined by pore pressure) cannot be distinguished. This research can further refine the consolidation theory of “upper hard and lower soft” double-layer foundations. Full article

As the last critical hydrological control station on the Lancang River before it flows out of China, the daily runoff variations at the Yunjinghong Hydrological Station are directly linked to agricultural irrigation, hydropower development, and ecological security in downstream Mekong River riparian countries such as Laos, Myanmar, and Thailand. Aiming at the core issues of the runoff sequence in the Lancang–Mekong Basin, which is characterized by prominent nonlinearity, non-stationarity, and coupling of multi-scale features, this study proposes a synergistic prediction framework of “multi-scale decomposition-model improvement-parameter optimization”. Firstly, Regenerated Phase-Shifted Sine-Assisted Empirical Mode Decomposition (RPSEMD) is adopted to adaptively decompose the daily runoff data. On this basis, a Convolutional Sparse Attention Transformer (CSAT) model is constructed. A one-dimensional convolutional neural network (1D-CNN) module is embedded in the input layer to enhance local feature perception, making up for the deficiency of traditional Transformers in capturing detailed information. Meanwhile, the sparse attention mechanism replaces the multi-head attention, realizing efficient focusing on key time-step correlations and reducing computational costs. Additionally, an Improved Multi-Verse Optimizer (IMVO) is introduced, which optimizes the hyperparameters of CSAT through a spiral update mechanism, exponential Travel Distance Rate (T_DR), and adaptive compression factor, thereby improving the model’s accuracy in capturing short-term abrupt patterns such as flood peaks and drought transition points. Experiments are conducted using measured daily runoff data from 2010 to 2022, and the proposed model is compared with mainstream models such as LSTM, GRU, and standard Transformer. The results show that the RPSEMD-IMVO-CSAT model reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 15.3–28.7% and 18.6–32.4%, respectively, compared with the comparative models. Full article

To address flight safety risks from rotor defects in rotorcraft drones operating in complex low-altitude environments, this study proposes a high-precision diagnostic model based on the Multimodal Data Input and Spatio-Temporal Feature Fusion Network (MDI-STFFNet). The model uses a dual-modality coupling mechanism that integrates vibration and air pressure signals, forming a “single-path temporal, dual-path representational” framework. The one-dimensional vibration signal and the five-channel pressure array are mapped into a texture space via phase space reconstruction and color-coded recurrence plots, followed by extraction of transient spatial features using a pre-trained ResNet-18 model. Parallel LSTM networks capture long-term temporal dependencies, while a parameter-free 1D max-pooling layer compresses redundant pressure data, reducing LSTM parameter growth. The CSW-FM module enables adaptive fusion across modal scales via shared-weight mapping and learnable query vectors that dynamically assign spatiotemporal weights. Experiments on a self-built dataset with seven defect types show that the model achieves 99.01% accuracy, improving by 4.46% and 1.98% over single-modality vibration and pressure inputs. Ablation studies confirm the benefits of spatiotemporal fusion and soft weighting in accuracy and robustness. The model provides a scalable, lightweight solution for UAV power system fault diagnosis under high-noise and varying conditions. Full article

Compressed air energy storage in aquifers presents a promising approach for large-scale energy storage, yet its implementation is complicated by geochemical reactions, such as pyrite oxidation, which can impact reservoir integrity and operational efficiency. This study numerically investigates the evolution of reservoir characteristics and geochemical processes during CAESA operations to address these challenges. Using the TOUGHREACT simulator, we developed one-dimensional and two-dimensional reactive transport models based on the Pittsfield aquifer field test parameters to simulate coupled thermal-hydrological–chemical processes under varying injection rates, temperatures, reservoir depths, and operational cycles. The results demonstrate that higher injection rates induce greater near-well pressure buildup and extended thermal zones, while deeper reservoirs exhibit abrupt declines in pressure and gas saturation due to formation constraints. Geochemical analyses reveal that pyrite oxidation dominates, leading to oxygen depletion, groundwater acidification (pH reduction), and secondary mineral precipitation, such as goethite and hematite. These findings underscore the critical interplay between operational parameters and geochemical reactions, highlighting the need for optimized design to ensure long-term stability and efficiency of aquifer-based energy storage systems. Full article

The timely identification of rock fractures is crucial in deep subterranean engineering. However, it remains necessary to identify reliable warning indicators and establish effective warning levels. This study introduces the Acoustic Emission Transformer for FRActure Prediction (AET-FRAP) multi-input time series forecasting framework, which employs acoustic emission feature parameters. First, Empirical Mode Decomposition (EMD) combined with Fast Fourier Transform (FFT) is employed to identify and filter periodicities among diverse indicators and select input channels with enhanced informative value, with the aim of predicting cumulative energy. Thereafter, the one-dimensional sequence is transformed into a two-dimensional tensor based on its predominant period via spectral analysis. This is coupled with InceptionNeXt—an efficient multiscale convolution and amplitude spectrum-weighted aggregate—to enhance pattern identification across various timeframes. A secondary criterion is created based on the prediction sequence, employing cosine similarity and kurtosis to collaboratively identify abrupt changes. This transforms single-point threshold detection into robust sequence behavior pattern identification, indicating clearly quantifiable trigger criteria. AET-FRAP exhibits improvements in accuracy relative to long short-term memory (LSTM) on uniaxial compression test data, with R² approaching 1 and reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). It accurately delineates energy accumulation spikes in the pre-fracture period and provides advanced warning. The collaborative thresholds effectively reduce noise-induced false alarms, demonstrating significant stability and engineering significance. Full article

This article presents a theoretical study of the scaling properties of the kinetic energy spectrum in compressible turbulence. From the fundamental symmetries and linear transformations of the microscopic action, we derive exact relations between the correlation functions and their generators. These relations put strong constraints on the possible scaling relations in the system as a function of scale. One of the main results of this study is that the action can be split between an incompressible part, which is the same as the usual stochastic Navier–Stokes theory in whatever the value of the Mach number is, and a longitudinal part, whose behavior is to be compared to the three-dimensional Burgers equation, which presents a much richer phase diagram as its usually discussed one-dimensional counterpart. Full article

This study presents a streamlined and accurate approach for modeling the performance of hermetic reciprocating compressors under variable-speed conditions. Traditional compressor models often neglect the influence of motor frequency, leading to considerable deviations at low-speed operation. To address these limitations, a frequency-dependent numerical framework was developed using one-dimensional (1-D) and two-dimensional (2-D) polynomial regressions to represent volumetric efficiency (${\eta }_v$) and isentropic efficiency (${\eta }_{isentr}$) as functions of compression ratio (r) and motor speed frequency (f). The proposed model integrates manufacturer data and thermodynamic property databases to predict compressor behavior across a wide range of operating conditions. Validation using the Bitzer 4HTE-20K CO₂ compressor demonstrated strong agreement with experimental data, maintaining prediction errors within ±10% for both power input and discharge temperature. Moreover, the model enhanced accuracy by up to 19.4% in the low-frequency range below 40 Hz, where conventional models typically fail. The proposed method provides a practical and computationally efficient tool for accurately simulating the performance of hermetic reciprocating compressors that support improved design, optimization, and control of refrigeration and heat pump systems. Full article

In response to the challenges of low recycling rates of waste tires and their underutilization in loess subgrades, this study systematically investigates the compression deformation characteristics of tire particle (4–6 mm)-modified loess through comprehensive laboratory testing. Using one-dimensional compression tests and cyclic loading–unloading tests, the effects of different tire particle contents (0% to 100%) on pore structure evolution, compression parameters—including the compression coefficient, compression modulus, and volumetric compression coefficient—and deformation mechanisms were thoroughly analyzed. The study reveals critical state characteristics and deformation mechanisms of tire-derived aggregate–loess mixtures (TDA-LMs) and establishes a predictive model for their compression behavior. The research results indicate the following: (1) The compression behavior of TDA-LM exhibits a distinct dosage threshold and stress dependence: the critical blending ratio is 30% under stresses below 100 kPa, increasing to 40% at higher stresses (≥100 kPa); (2) Mixtures with medium to low tire content display strain hardening, whereas pure tire specimens show approximately 10% modulus softening within the 200–300 kPa range. Stress- and content-dependent models for the compression modulus and volumetric compression coefficient were developed with high accuracy (R² > 0.96); (3) The dominant deformation mechanism shifts from soil skeleton plastic yielding (at tire contents < 40%) to rubber-dominated elastic deformation (at contents > 50%). Over 85% of cumulative deformation occurs during the initial loading phase, indicating that particle–soil interface restructuring primarily takes place early in the loading process. This study provides a theoretical basis and practical design parameters for the application of waste tires in loess subgrade engineering, supporting the sustainable reuse of solid waste in environmentally friendly geotechnical construction. Full article