Search Results (303)

Single-point deformation monitoring models fail to reflect the structural integrity of the concrete gravity dams, and traditional regression methods also have shortcomings in capturing complex nonlinear relationships among variables. To solve these problems, this paper develops a data–physics-driven multi-point hybrid deformation monitoring model based on Bayesian Optimization Algorithm–Light Gradient-Boosting Machine (BOA-LightGBM). Building upon conventional single-point models, spatial coordinates are incorporated as explanatory variables to derive a multi-point deformation monitoring model that accounts for spatial correlations. Subsequently, the finite element method (FEM) is employed to simulate the hydrostatic component at each monitoring point under actual reservoir water levels. Finally, a hybrid model is constructed by integrating the derived mathematical expression, simulated hydrostatic components, and the BOA-LightGBM algorithm. A case study demonstrates that the proposed model effectively incorporates spatial deformation characteristics within dam sections and achieves satisfactory fitting and prediction accuracy compared to traditional single-point monitoring models. With further refinement and extension, the proposed modeling theory and methodology presented in this study can also provide valuable references for safety monitoring of other hydrostatic structures. Full article

Scene Text Recognition (STR) faces significant challenges under complex degradation conditions, such as distortion, occlusion, and semantic ambiguity. Most existing methods rely heavily on language priors for correction, but effectively constructing language rules remains a complex problem. This paper addresses two key challenges: (1) The over-correction behavior of language models, particularly on semantically deficient input, can result in both recognition errors and loss of critical information. (2) Character misalignment in visual features, which affects recognition accuracy. To address these problems, we propose a Deformable-Alignment-based Dual Correction Mechanism (DADCM) for STR. Our method includes the following key components: (1) We propose a visually guided and language-assisted correction strategy. A dynamic confidence threshold is used to control the degree of language model intervention. (2) We designed a visual backbone network called SCRTNet. The net enhances key text regions through a channel attention module (SENet) and applies deformable convolution (DCNv4) in deep layers to better model distorted or curved text. (3) We propose a deformable alignment module (DAM). The module combines Gumbel-Softmax-based anchor sampling and geometry-aware self-attention to improve character alignment. Experiments on multiple benchmark datasets demonstrate the superiority of our approach. Especially on the Union14M-Benchmark, where the recognition accuracy surpasses previous methods by 1.1%, 1.6%, 3.0%, and 1.3% on the Curved, Multi-Oriented, Contextless, and General subsets, respectively. Full article

Since anchoring technology is a key measure to enhance the deformation resistance of engineering structures, it is widely applied in bridges, dams, power transmission lines, and offshore platforms. The displacement of anchor bolts directly affects the deformation resistance of structures, and anchor bolts are frequently arranged at an inclination angle in engineering practice—this inclination angle significantly affects their displacement. However, existing anchor bolt displacement prediction models do not account for the influence of inclination angles. To address this gap, a novel displacement prediction model for inclined anchor bolts based on Mindlin’s solution is proposed in this paper. The validation with three experimental datasets shows that the model’s relative errors are within 5%. Even if minor measurement uncertainties regarding input parameters exist in practical engineering scenarios, the calculated displacement results will not undergo significant deviations. The anchor bolt displacement prediction model proposed in this paper may help scholars better understand the relationship between anchor bolt inclination angle and displacement. Full article

Displacement monitoring data is essential for assessing the structural safety of high-arch dams. Existing models, predominantly based on single-model architectures, often lack the ability to effectively integrate multiple algorithms, leading to limited predictive performance and poor interpretability. This study proposes an ensemble learning framework for dam displacement prediction, combining Hydraulic–Seasonal–Temporal model (HST), Random Forest (RF), and Bidirectional Gated Recurrent Unit (BiGRU) models as base learners. A stacking strategy is employed to enhance predictive accuracy, and the Grey Wolf Optimizer (GWO) is used for hyperparameter optimization. To improve model transparency, the Shapley Additive Explanations (SHAP) algorithm is applied for interpretability analysis. Extensive experiments demonstrate that the proposed ensemble model outperforms individual models, achieving a Root Mean Squared Error (RMSE) of 0.2241 and a Coefficient of Determination (R²) of 0.9993 on the test set. The SHAP analysis further elucidates the contribution of key variables, providing valuable insights into the displacement prediction process and offering a robust technical foundation for arch dam safety monitoring and early risk warning. Full article

As a core structure in coal mine underground reservoirs, the coal pillar dams’ stability is susceptible to the orientation of coal bedding planes. This study examines the deformation characteristics, acoustic emission (AE) evolution, and infrared radiation temperature (IRT) response of coal specimens with varying bedding angles (0°, 30°, 60°, 90°), investigating microscopic failure mechanisms and AE-IRT correlations. The results show that compressive strength and elastic modulus follow a V-shaped trend with increasing bedding angle, initially decreasing before rising. The proportion of low-amplitude events (40–60 dB) increases, while the higher-amplitude (>60 dB) AE signals decrease with the bedding angle. The AE b-values increase with the bedding angles. Mean IRT temperatures exhibit an overall increasing trend with significant fluctuations, and fluctuation amplitudes display an N-shaped pattern. Microscopically, all specimens undergo tensile–shear composite failure, but shear failure contribution varies markedly: 30° specimens show the highest shear proportion, while 60° specimens show the lowest. There is a positive correlation between AE and IRT. The correlation coefficient (γ) is relatively low at 0°, but it is higher at 30°, 60°, and 90°. This research provides a theoretical underpinning for optimizing the design and stability evaluation of coal mine underground reservoirs. Full article

In mountainous canyon regions, BeiDou Navigation Satellite System (BDS)/Global Navigation Satellite System (GNSS) receivers are susceptible to multireflection and tropospheric factors, which frequently reduce the accuracy in monitoring vertical deformation monitoring under short-baseline methods. This limitation hinders the application of BDS/GNSS in high-precision monitoring scenarios in those cases. To address this issue, this study proposes a three-dimensional (3D) deformation measurement method that integrates BDS/GNSS positioning with dihedral corner reflectors (CRs). By incorporating high-precision horizontal positioning results obtained from BDS/GNSS into the radar line-of-sight (LOS) correction process and utilizing ascending and descending Synthetic Aperture Radar (SAR) data for joint monitoring, the method achieves millimeter-level- accuracy in measuring vertical deformation at corner reflector sites. At the same time, it enhances the 3D positioning accuracy of BDS/GNSS to the 1 mm level under short-baseline configurations. Based on monitoring stations deployed at the Jinsha River dam site, the proposed deformation fusion monitoring method was validated using high-resolution SAR imagery from Germany’s TerraSAR-X (TSX) satellite. Simulated horizontal and vertical displacements were introduced at the stations. The results demonstrate that BDS/GNSS achieves better than 1 mm horizontal monitoring accuracy and a vertical accuracy of around 5 mm. Interferometric SAR (InSAR) CRs achieve approximately 2 mm in horizontal accuracy and 1 mm in vertical accuracy. The integrated method yields a 3D deformation monitoring accuracy better than 1 mm. This paper’s results show high potential for achieving high-precision deformation observations by fusing BDS/GNSS and dihedral CRs, offering promising prospects for deformation monitoring in reservoir canyon regions. Full article

An internal geomembrane (GMB)–clay seepage control system is an important form of seepage control structure for rockfill dams. In order to investigate the stress and deformation characteristics of GMB in GMB–clay core-wall rockfill dams (GMCWRD) under different construction and operation conditions, the stress and deformation fields of GMCWRDs were calculated by numerical simulation under a variety of working conditions. The stress and deformation characteristics of the dam and GMB during the impoundment period were investigated, and the influences of the spreading thickness of the clay core-wall and the location of the GMB defects and hydraulic head on the stress and deformation of the GMB were analyzed. The results show that the maximum tensile strain of the GMB upstream of the clay core-wall during the impoundment period occurs at the anchorage of the GMB and the concrete cut-off, with a maximum tensile strain of 2.70%. With the increase in the spreading thickness of the clay core-wall, the maximum tensile stress and strain of the GMB fluctuated. Under the dam construction and foundation conditions in this paper, when the spreading thickness of the clay core-wall was 2 m, the tensile stress and strain of GMB were at the lowest level. As the defect location of the GMB decreases, the phreatic line of the dam gradually increases, and the seepage discharge of the dam and the tensile strain of the GMB gradually increase, with the maximum tensile strain of 3.98%. The maximum deformation of the GMB in each case is much smaller than the maximum elastic deformation range of the selected PVC GMB, and the conclusion of the study provides a certain scientific basis for the design and construction of the seepage control of the core rockfill dam. Full article

Breakwater dams are critical infrastructures that protect the safety of ports. However, these coastal structures are facing the compounding threats of sea level rise, storm surge, and dam subsidence. Heterogeneous deformations in these infrastructures arise from differential construction sequencing, sediment consolidation, and filling materials, yet traditional in situ monitoring remains spatially limited or even unavailable to trace back and continuously monitor deformation evolutions. In contrast, Interferometric Synthetic Aperture Radar (InSAR) offers valuable insights in providing the spatially and temporally covered dam deformation. In this study, we used two Sentinel-1 tracks from 2016 to 2025, and the persistent and distributed scatterers InSAR methods to map the long-term deformation of Xuwei Port, Lianyungang, China. We utilized six sites of leveling measurements to validate the InSAR-derived vertical deformation and indicate Root Mean Square Errors (RMSEs) ranging from −0.9–1.2 cm. We find, for the rock-sand filled section, the deformations show consolidating subsidence ranging from −63.8 cm to −40.6 cm. In contrast, the concrete tubular structure remains stable, with cumulative deformation ranging from −10.6 cm to −5.2 cm. The enclosing reclaimed land undergoes a period of accelerated settlement with subsidence rates of −64.9–−39.3 cm/yr, which are higher than original subsidence rates of −10.1–−9.7 cm/yr. Additionally, we integrated the consolidation model and tide gauge to quantify that the freeboard will decrease to 0.08–0.31 m in the following 100 years with the continuous sea level rise and dam subsidence. This study benefits our understandings of coastal dam and reclaimed land. It highlights InSAR as a valuable tool to evaluate the critical risk between sea level rise and coastal infrastructure subsidence. Full article

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

In deformation monitoring, the severe GNSS multipath caused by reflective surfaces can significantly degrade positioning accuracy. However, traditional multipath mitigation methods often assume strong day-to-day repeatability of residual errors, which is not always valid in complex monitoring environments. We propose a novel GNSS multipath correction approach that leverages multi-day post-fit residual data and principal component analysis to extract stable multipath signals, integrating them into an enhanced spatial repeatability multipath correction model. This method can effectively isolate true multipath errors, even under conditions of weak inter-day repeatability. Experimental results from a dam monitoring network demonstrate that the proposed method reduces the root mean square (RMS) error of single-day kinematic positioning by about 1.8 mm, 2.4 mm, and 6.7 mm in the East, North, and Up components, respectively. For static positioning solutions over 1 h, 2 h, and 4 h sessions, the RMS in East, North, and Up is reduced by approximately 40% on average. After correction, 2 h sessions achieve ~1.1 mm horizontal and ~3.0 mm vertical accuracy, while 4 h sessions reach ~0.9 mm horizontal and ~2.5 mm vertical accuracy. These improvements confirm that the proposed method effectively mitigates multipath effects and meets the high-precision requirements of deformation monitoring. Full article

Hydraulic actions may compromise dam foundation stability. Helical anchors have been used in dam foundation reinforcement projects because of the advantages of large uplift and compression bearing capacity, fast installation, and convenient recovery. However, the research on the anchor plate, which plays a key role in the bearing performance of helical anchors, is insufficient at present. Based on the finite element model of helical anchor, this study reveals the failure mode and influencing factors of the anchor plate and establishes the theoretical model of deformation calculation. The results showed that the helical anchor plate had obvious bending deformation when the dam foundation reinforced with a helical anchor reached large deformation. The helical anchor plate can be simplified to a flat circular disk. The stress distribution of the closed flat disk and the open flat disk was consistent with that of the helical disk. The maximum deformation of the closed flat disk was slightly smaller than that of the helical disk (less than 6%), and the deformation of the open flat disk was consistent with that of the helical disk. The results fill the blank of the design basis of helical anchor plate and provide a reference basis for the engineering design. Full article

Coarse-grained soils serve as essential fill materials in earth–rock dam engineering, where their mechanical properties critically influence dam deformation and stability, directly impacting project safety. Artificial intelligence (AI) techniques are emerging as powerful tools for predicting the mechanical properties of coarse-grained soils. However, AI-based prediction models for these properties face persistent challenges, particularly in parameter tuning—a process requiring substantial computational resources, extensive time, and specialized expertise. To address these limitations, this study proposes a novel prediction model that integrates Adaptive Simulated Annealing (ASA) with an improved Particle Swarm Optimization (PSO) algorithm to optimize the Elman Neural Network (ENN). The methodology encompasses three key aspects: First, the standard PSO algorithm is enhanced by dynamically adjusting its inertial weight and learning factors. The ASA algorithm is then employed to optimize the Adaptive PSO (APSO), effectively mitigating premature convergence and local optima entrapment during training, thereby ensuring convergence to the global optimum. Second, the refined PSO algorithm optimizes the ENN, overcoming its inherent limitations of slow convergence and susceptibility to local minima. Finally, validation through real-world engineering case studies demonstrates that the ASA-PSO-optimized ENN model achieves high accuracy in predicting the mechanical properties of coarse-grained soils. This model provides reliable constitutive parameters for stress–strain analysis in earth–rock dam engineering applications. Full article

Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m³ and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R² between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article