Prediction Of Deformation Research Articles

Deep learning model for the deformation prediction of concrete dams under multistep and multifeature inputs based on an improved autoformer

The long-term prediction of deformation in concrete dams is a critical requirement for maintaining their structural integrity over time in practical management scenarios. While most existing models predominantly address short-term and medium-term predictions, there remains a significant challenge in establishing reliable correlations within complex long-term temporal patterns and varying environmental factors. Consequently, there is limited research on multistep prediction. To solve these problems, an Autoformer-based model for multistep and multifeature dam deformation prediction is proposed. In the proposed model, the sequence decomposition of dam monitoring data is used as the basic construction module inside the depth model. Then, Deep Automatic Correlation (Deep-AutoCorrelation) mechanism is employed to obtain the long-term dependence between dam deformation and environmental load. The encoder-decoder structure integrates time series decomposition and the Deep-AutoCorrelation to predict the multistep deformation of the dam. In the multistep deformation prediction of the dam, the improved Autoformer model demonstrates state-of-the-art performance, resulting in a 48% increase in prediction accuracy across five monitoring point datasets when compared to six benchmark models. Experimental results indicate that this method effectively addresses the challenge of predicting long-term deformation of dams subjected to complex environmental loads. It demonstrates robust capabilities in extracting long-term temporal features and avoids the increase of computational complexity caused by deeper time extraction.

Deformation prediction of arch dams by coupling STL decomposition and LSTM neural network

Deformation prediction of arch dams by coupling STL decomposition and LSTM neural network

Modeling Strain Hardening of Metallic Materials with Sigmoidal Function Considering Temperature and Strain Rate Effects.

This study uses a sigmoidal function to describe the plastic strain hardening of metallic materials, considering temperature and strain rate effects. The effectiveness of this approach is evaluated and systematically compared with other hardening laws. Incorporating temperature and strain rate effects into the parameters of this sigmoidal-type hardening law enables a more precise description and prediction of the plastic deformation of materials under different combinations of temperature and strain rate. The temperature effect is coupled using a simplified Arrhenius model, and the strain rate effect is coupled with a modified Johnson-Cook model. The sigmoidal-type hardening law is integrated with an asymmetric yield criterion to address complex behavior, such as anisotropy and strength differential effects. The calibration and validation of the constitutive model involve examining uniaxial tensile/compressive flow curves in various directions and biaxial tensile/compressive flow curves for diverse metallic alloys, proving the proposed model's broad applicability.

A Similarity Clustering Deformation Prediction Model Based on GNSS/Accelerometer Time-Frequency Analysis

Structural monitoring is crucial for assessing structural health, and high-precision deformation prediction can provide early warnings for safety monitoring. To address the issue of low prediction accuracy caused by the non-stationary and nonlinear characteristics of deformation sequences, this paper proposes a similarity clustering (SC) deformation prediction model based on GNSS/accelerometer time-frequency analysis. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the original monitoring data, and the time-frequency characteristic correlations of the deformation data are established. Then, similarity clustering is conducted for the monitoring sub-sequences based on their frequency domain characteristics, and clustered sequences are combined subsequently. Finally, the Long Short-Term Memory (LSTM) model is used to separately predict GNSS displacement and acceleration with clustered time series, and the overall deformation displacement is reconstructed based on the predicted GNSS displacement and acceleration-derived displacement. A shake table simulation experiment was conducted to validate the feasibility and performance of the proposed CEEMDAN-SC-LSTM model. A duration of 5 s displacement prediction is analyzed after 153 s of monitoring data training. The results demonstrate that the root mean square error (RMSE) of predicted displacement is 0.011 m with the proposed model, which achieves an improvement of 64.45% and 61.51% in comparison to the CEEMDAN-LSTM and LSTM models, respectively. The acceleration predictions also show an improvement of 96.49% and 95.58%, respectively, the RMSE of the predicted acceleration-reconstructed displacement is less than 1 mm, with a reconstruction similarity of over 99%. The overall displacement reconstruction similarity can reach over 95%.

A new elastoplastic model for bolt-grouted fractured rock

Complexities in mechanical behaviours of rock masses mainly stem from inherent discontinuities, which calls for advanced bolt-grouting techniques for stability enhancement. Understanding the mechanical properties of bolt-grouted fractured rock mass (BGFR) and developing accurate prediction methods are crucial to optimize the BGFR support strategies. This paper establishes a new elastoplastic (E-P) model based on the orthotropic and the Mohr-Coulomb (M-C) plastic-yielding criteria. The elastic parameters of the model were derived through a meso-mechanical analysis of composite materials mechanics (CMM). Laboratory BGFR specimens were prepared and uniaxial compression test and variable-angle shear test considering different bolt arrangements were carried out to obtain the mechanical parameters of the specimens. Results showed that the anisotropy of BGFR mainly depends on the relative volume content of each component material in a certain direction. Moreover, the mechanical parameters deduced from the theory of composite materials which consider the short fibre effect are shown to be in good agreement with those determined by laboratory experiments, and the variation rules maintained good consistency. Last, a case study of a real tunnel project is provided to highlight the effectiveness, validity and robustness of the developed E-P model in prediction of stresses and deformations.

Shell element-based prediction of process-induced deformation considering the different fabric parameters and stacking sequences of CFRP woven composites

Herein, we present a finite element method (FEM) based curing analysis for predicting the process-induced deformation (PID) of CFRP woven composites using shell-type elements. This method can efficiently consider various fabric parameters and stacking sequences, even for complex structures. For woven composite plates and L-shaped flanges, FEM-based PID prediction using shell elements reduced computation times by 50–59% compared to solid elements, while maintaining acceptable accuracy with relative errors of 0.17–7.35%. Furthermore, the impacts of various fabric parameters and stacking sequences on the PID of woven composites are analyzed in detail. The main finding is that the variation in material properties due to fabric parameters and stacking sequences significantly impacts the PID of woven composite structures. This prediction method can be applied in the design of composite structures and molds, thereby enhancing the dimensional precision and performance of composite structures.

Rapid prediction of substrate deformation in laser deposition repair process for Ti-6.5Al-3.5Mo-1.5Zr-0.3Si alloy based on the inherent strain method

Rapid prediction of substrate deformation in laser deposition repair process for Ti-6.5Al-3.5Mo-1.5Zr-0.3Si alloy based on the inherent strain method

Biophysical profiling of red blood cells from thin-film blood smears using deep learning

Microscopic inspection of thin-film blood smears is widely used to identify red blood cell (RBC) pathologies, including malaria parasitism and hemoglobinopathies, such as sickle cell disease and thalassemia. Emerging research indicates that non-pathologic changes in RBCs can also be detected in images, such as deformability and morphological changes resulting from the storage lesion. In transfusion medicine, cell deformability is a potential biomarker for the quality of donated RBCs. However, a major impediment to the clinical translation of this biomarker is the difficulty associated with performing this measurement. To address this challenge, we developed an approach for biophysical profiling of RBCs based on cell images in thin-film blood smears. We hypothesize that subtle cellular changes are evident in blood smear images, but this information is inaccessible to human expert labellers. To test this hypothesis, we developed a deep learning strategy to analyze Giemsa-stained blood smears to assess the subtle morphologies indicative of RBC deformability and storage-based degradation. Specifically, we prepared thin-film blood smears from 27 RBC samples (9 donors evaluated at 3 storage time points) and imaged them using high-resolution microscopy. Using this dataset, we trained a convolutional neural network to evaluate image-based morphological features related to cell deformability. The prediction of donor deformability is strongly correlated to the microfluidic scores and can be used to categorize images into specific deformability groups with high accuracy. We also used this model to evaluate differences in RBC morphology resulting from cold storage. Together, our results demonstrate that deep learning models can detect subtle cellular morphology differences resulting from deformability and cold storage. This result suggests the potential to assess donor blood quality from thin-film blood smears, which can be acquired ubiquitously in clinical workflows.

Deformation prediction model for hollow thin-walled aluminum alloy structural parts under multiple load-sequence coupling conditions

Deformation prediction model for hollow thin-walled aluminum alloy structural parts under multiple load-sequence coupling conditions

Prediction of Welding Deformation Using the Thermal Elastic-Plastic Finite Element Method by Considering Welding Interpass Temperature.

In this study, we propose a method for predicting welding deformation caused by multi-pass welding using the thermal elastic-plastic finite element method (TEP-FEM) by considering the interpass temperature. This method increases the interpass temperature, which has not been considered in the existing TEP-FEM, from 200 °C to 1000 °C, and simultaneously performs thermal and mechanical analyses. In addition, this method can also evaluate temperature history and the time it takes to weld. By predicting the welding deformation using this method, angular distortion prediction was reduced from 16.75 mm to 10.9 mm compared to the case where the interpass temperature was cooled to room temperature. Additionally, the deformation error was significantly reduced from 6.14% to 2.92% compared to that of the strain as directed boundary method used in a previous study. Additionally, our research demonstrated that interpass temperatures above 800 °C can result in increased deformation errors. In conclusion, it is essential to select an appropriate temperature to minimize deformation error.

Time Series Prediction of Reservoir Bank Slope Deformation Based on Informer and InSAR: A Case Study of Dawanzi Landslide in the Baihetan Reservoir Area, China

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research.

Analysis of deformation and multi-angle tip contact in a combined mechanism of thin-walled membrane forces and inextensible layers in soft actuators

Soft actuators have extensive applications in operations, are generally made of elastomers, and generate large deformation. It makes accurately predicting their deformation behavior challenging. This study considers the combined effects of chamber sidewall expansion and inextensible layer strain mechanisms on the actuator's bending and tip contact force. We separately model the expansion of the chamber sidewall and the strain in the inextensible layer using the expanding membrane and strain beam theory. The theoretical model presented to predict the contact area of the sidewall expanding membrane under multiple bending conditions. The analytical models for three actuator states are established: 1) Free space; 2) Tip contact at multiple angles; and 3) Grasping state. The proposed theoretical model accurately predicts deformation, force characteristics, and the requisite pressure for object grasping, and its applicability extends to similar soft actuators. Comparative analyses with finite element methods (FEM) and experimental results validate the model's computational efficiency, reduced design variables, and accurate prediction of diverse deformations and tip contact forces. Notably, the model outperforms with shorter computation times. The large and small chamber spacing errors are approximately 10 % and 5 %, respectively.

Dam Deformation Prediction Considering the Seasonal Fluctuations Using Ensemble Learning Algorithm

Dam deformation is the most visual and relevant monitoring quantity that reflects the operational condition of a concrete dam. The seasonal variations in the external environment can induce seasonal fluctuations in the deformation of concrete dams. Hence, preprocessing the deformation monitoring series to identify seasonal fluctuations within the series can effectively enhance the accuracy of the predictive model. Firstly, the dam deformation time series are decomposed into the seasonal and non-seasonal components based on the seasonal decomposition technique. The advanced ensemble learning algorithm (Extreme Gradient Boosting model) is used to forecast the seasonal and non-seasonal components independently, as well as employing the Tree-structured Parzen Estimator (TPE) optimization algorithm to tune the model parameters, ensuring the optimal performance of the prediction model. The results of the case study indicate that the predictive performance of the proposed model is intuitively superior to the benchmark models, demonstrated by a higher fitting accuracy and smaller prediction residuals. In the comparison of the objective evaluation metrics RMSE, MAE, and R2, the proposed model outperforms the benchmark models. Additionally, using feature importance measures, it is found that in predicting the seasonal component, the importance of the temperature component increases, while the importance of the water pressure component decreases compared to the prediction of the non-seasonal component. The proposed model, with its elevated predictive accuracy and interpretability, enhances the practicality of the model, offering an effective approach for predicting concrete dam deformation.

Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides.

Bending shape memory properties and multi-scale viscoelastic behaviors of knitted-fabric reinforced polymer composites

Knitted fabrics with easily deformable loop structure have the potential in the development of shape memory polymeric composites with large recovery deformations. The knitted fabric reinforced shape memory epoxy polymer composites (SMPC) were prepared in this work. The effects of loop densities, orientations and bending radii on shape memory properties of SMPC were investigated. The shape fixity ratio and shape recovery ratio of SMPC subjected to U-shaped bending radius of 5 mm are above 98 %. The shape recovery force of SMPC can reach up to 5.9 N. The thermodynamic properties of SMP were also characterized to obtain mechanical parameters and a user-defined material subroutine (UMAT) of shape memory epoxy polymer (SMEP) was written. Based on viscoelastic theory and the multi-scale geometrical structures, the macroscopic homogeneous thermodynamic model and mesoscopic thermodynamic model of knitted fabric reinforced SMPC were established to study the macro-scale stress distribution and meso-scale deformation evolution during shape memory process, respectively. The neutral surface position of SMPC during bending deformation is offset inner. The unique anisotropic loop structure of knitted fabric determines the shape memory behavior of the SMPC. Finally, micro-CT characterizations of knitted fabric reinforced SMPC were conducted to further understand the loop deformation mechanism during shape memory process. This study will provide important theoretical and technical support for large deformation structure design and deformation prediction of smart composites.

DACLnet: A Dual-Attention-Mechanism CNN-LSTM Network for the Accurate Prediction of Nonlinear InSAR Deformation

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data.

Prediction of adjacent single pile deformation induced by tunnel excavation based on the Pasternak model

The construction of metro tunnels in soft ground would cause significant settlement of the ground surface and deformation of buildings and their foundations in the vicinity of the tunnels. The deformation laws of the pile of the neighbouring structures induced by the tunnel construction have not yet been fully identified. This paper analyzed the impact mechanism of tunnel construction on piles based on the Pasternak model, deduced the calculation methods of additional horizontal displacement and additional internal force of piles, and introduced three solution methods. The computational method results have been validated through numerical simulations using coupling of discrete element method and finite difference method and compared to select laboratory data. The results show that the model accuracy is better when the tunnel axis burial depth is less than the pile bottom burial depth. The analysis shows that the tunnel burial depth significantly influences the displacement distribution of the adjacent single pile, while the impact on the peak displacement is insignificant. The thickness of the elastic layer (C) is inversely related to the maximum horizontal displacement and internal force, but it does not impact the distribution patterns of the displacement and internal force. For thickness of the elastic layer (C), the bending moment’s point is constant at 2 m above and below the tunnel axis, whereas the shear force’s point remains constant at the buried tunnel axis’s depth and 4 m above the tunnel axis.

Enhancing Landslide Prediction Through Advanced Transformer-Based Models: Integrating SAR Imagery and Environmental Data

Landslides, a significant natural hazard driven predominantly by rainfall infiltration, pose a continuous threat to the terrain, buildings, and ecosystem of Caiazzo, located in southern Italy (Caserta). This research undertakes a novel approach to predict and understand the complex patterns of landslide deformations in this region. Our study leverages the advanced capabilities of COSMO-SkyMed satellite imagery, integrating a comprehensive dataset that includes factors such as elevation, slope, Topographic Wetness Index, Stream Power Index, geology, flow direction, curvature, Normalized Difference Vegetation Index, and several other relevant indices. We employ Transformer-based models, renowned for their effectiveness in capturing long-range dependencies within sequential data. The Transformer models in our study are adept at analyzing the temporal sequences of environmental factors and their intricate interactions, thereby offering a more nuanced understanding of the temporal patterns leading to landslides. These Transformer models are designed to process the entire sequence of satellite data obtained by the Coherent Pixels - Temporal Phase Coherence within the SUBSOFT package to ensure data integrity and precision, encompassing 132 images in ascending geometry and 143 in descending geometry, spanning from 2013 to 2021. By doing so, they efficiently identify critical patterns and dependencies over time, such as the interplay between rainfall events and subsequent soil and vegetation changes. The models are fine-tuned to our specific dataset, ensuring high precision and accuracy in landslide deformation prediction. Our evaluation metrics, including Mean Absolute Error, Root Mean Square Error, and R^2 score, demonstrate the superior performance of the Transformer-based approach, with significant improvements over the conventional Deep Learning model. The visual correlation of our predictions with actual landslide occurrences further corroborates the effectiveness of this method. This transformative approach not only enhances our understanding and predictive capability for landslides in Caiazzo but also sets a benchmark for landslide prediction in geologically vulnerable regions worldwide.

An approach for the design of dewatering systems: the case of an excavation for the construction of the assembly shaft of a tunnel boring machine

Robust approaches are needed for designing efficient dewatering systems of deep excavations below the water table to avoid unforeseen incidents (e.g., bottom instabilities in deep excavations and flooding, among others). This paper proposes a methodology, which integrates existing experiences, that was adopted to design the dewatering system of an excavation in the city of Barcelona (Spain). The approach consists of combining: (i) detailed geological and hydrogeological characterizations, (ii) numerical modelling for parameter estimation and drawdown predictions, and (iii) analytical assessment for stability evaluation and soil deformation predictions. The idea is that by combining a set of relatively easy to apply methods, it is possible to successfully solve a complex and risky problem. The methodology allows designing efficient dewatering systems, increasing safety and mitigating potential impacts of groundwater pumping. The most significant conclusion is that the most important step of the proposed approach is the hydrogeological characterization because it allows building realistic and representative numerical models to address most of the challenges associated to dewatering.

A Hybrid Prediction Model for Rock Reservoir Bank Slope Deformation Considering Fractured Rock Mass Parameters

Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical simulations stand out due to their well-defined physical and mechanical concepts, and their ability to make effective predictions with limited monitoring data. The predictive accuracy of hybrid models is closely tied to the precise determination of rock mass mechanical parameters in structural numerical simulations. However, rock masses in rock slopes are characterized by intersecting geological structural planes, resulting in reduced strength and the creation of multiple fracture flow channels. These factors contribute to the heterogeneous, anisotropic, and size-dependent properties of the macroscopic deformation parameters of the rock mass, influenced by the coupling of seepage and stress. To improve the predictive accuracy of the hybrid model, this study introduces the theory of equivalent continuous media. It proposes a method for determining the equivalent deformation parameters of fractured rock mass considering the coupling of seepage and stress. This method, based on a discrete fracture network (DFN) model, is integrated into the hybrid prediction model for rock slope deformation. Engineering case studies demonstrate that this approach achieves a high level of prediction accuracy and holds significant practical value.