Displacement Prediction Research Articles

Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

Thermal displacement prediction of high-speed electric spindles based on BWO-BiLSTM

To accurately, efficiently and stably predict the thermal displacement of the spindle, a prediction model based on the beluga whale algorithm (BWO) optimized bi-directional long and short-term memory neural network (BiLSTM) is introduced in this paper. Firstly, the thermal characterization and simulation analysis of the spindle are carried out, and the temperature and thermal displacement change characteristics of the spindle are obtained. Then the thermal deformation experiment of the spindle is carried out, and the temperature and displacement sensors are set up reasonably according to the temperature and thermal displacement change characteristics of the spindle, and the experimental data are collected and analyzed. The adaptive and globally convergent BWO is selected to optimize network parameters of BiLSTM, and the BWO-BiLSTM prediction model is constructed by learning the nonlinear correlation characteristics between spindle temperature and axial thermal displacement. The constructed BWO-BiLSTM prediction model is compared with other prediction models, and it is found through analysis that the prediction results output from the BWO-BiLSTM model have better accuracy and stability. The results of the study can provide a certain theoretical basis and technical support in predicting the spindle thermal displacement, which can help to promote the precision machining production of electric spindles.

Data-driven image mechanics (D2IM): A deep learning approach to predict displacement and strain fields from undeformed X-ray tomography images – Evaluation of bone mechanics

The recent advent of deep learning (DL) has enabled data-driven models to pave the way for the full exploitation of rich image datasets from which physics can be learnt. Here we propose a novel data-driven image mechanics (D2IM) approach that learns from digital volume correlation (DVC) displacement fields of vertebrae, predicting displacement and strain fields from undeformed X-ray computed tomography (XCT) images. D2IM successfully predicted the displacements in all directions, particularly in the cranio-caudal direction of the vertebra, where high correlation (R2=0.94) and generally minimal errors were obtained compared to the measured displacements. The predicted axial strain field in the cranio-caudal direction of the vertebra was also consistent in distribution with the measured one, displaying generally reduced errors in the regions within the vertebral body. The application of D2IM to lower resolution imaging in initial testing provides promising results indicating the future viability of integrating this technology into a clinical setting. This is the first study using experimental full-field measurements on bone structures from DVC to inform DL-based models such as D2IM, which represents a major contribution in the prediction of displacement and strain fields based only on the greyscale content of undeformed XCT images. In future, D2IM will incorporate a range of biological structures and loading scenarios for accurate prediction of physical fields, aiming at clinical translation for improved diagnostics. Data AvailabilityCode for preparing dataset, training D2IM model and visualising/analysing results has been hosted on GitHub: https://github.com/PeterSoar/D2IM_PrototypeThe dataset used for this study can be found on Figshare: https://doi.org/10.6084/m9.figshare.25404220.v1

Cutout effects on the vibration of sandwich auxetic cylindrical shells with an experimental validation

This study investigates the influence of auxetic core layers on the vibrational characteristics of sandwich cylindrical shells with cut-outs. The analysis utilizes the First-order Shear Deformation Theory (FSDT) and analytical techniques. The irregular behavior of auxetic re-entrante honeycomb cells necessitates the exploration of their effects on various systems. Additionally, the proposed method offers advantages over Finite Element Method (FEM). FEM modeling suffers from increased computational time and reduced accuracy with a large number of elements or high aspect ratio elements. These challenges are particularly significant for shells with geometric features of disparate scales, such as large cylinders with tiny cutouts or cutouts with low aspect ratios. However, the present research addresses these challenges by employing a method that integrates five panels with varying dimensions. Consequently, each section can utilize specific Two-Dimensional Generalized Differential Quadrature (2D-GDQ) grid points, tailored to the size of each side, enabling rapid and accurate prediction of displacement and stress parameters, particularly for shorter sides. The proposed method excels at precisely modeling cutouts while preserving their realistic properties. This is achieved by avoiding geometric and material simplifications and by assigning distinct boundary conditions to each region, including critical areas like corners, internal, and external sides. Moreover, Frequency Response Functions (FRFs) obtained from accelerometer and laser signals, alongside vibration-induced sound wave signals captured by electroacoustic microphones, validate the precision of the numerical calculations.

Graph recurrent neural networks-integrated real-time prediction of key displacements for fire-induced collapse early warning of steel frames

This study proposes the FRAME-Net, which can extract spatial-temporal features of discrete structures like planar steel frames, for early warning systems of fire-induced building collapse. FRAME-Net, based on graph neural networks and recurrent neural networks, seamlessly integrates feature extraction techniques, advanced graph convolution operations, and an enhanced graph long short-term memory mechanism, resolving the critical issue that the actual state of burning buildings, e.g., fire scenario, load case, material properties, etc., cannot be identified accurately and rapidly. The temperatures of steel components and easy-to-measure displacements are inputs, and hard-to-measure displacements at the top and interior of steel frames under fire can be predicted in real time. Numerical examples indicate that the trained agent accurately predicts the hard-to-measure displacements for the trained steel frame under completely unknown datasets, showcasing its potential in real-world unknown fire scenarios. Besides, the trained intelligent agent can be directly applied to untrained steel frames with different topological forms without re-training, which significantly reduces computational costs and is beneficial for swift emergency responses. Subsequently, satisfactory results predicted by the trained agent without re-training for the steel frame under the real fire test further underscores the promising generalization capability of the trained agent to adapt to unknown fire scenarios in real-world steel frames. Finally, the feasibility of the proposed framework to facilitate the implementation of the early-warning systems of fire-induced collapse is proved by comparing the predicted and actual remaining building collapse time, thereby assisting in reducing casualties during the rescue process and contributing to smart firefighting advancements.

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.

Maximum displacement prediction model for steel beams with hexagonal web openings under impact loading based on artificial neural networks

The estimation of the maximum displacement of steel beams with hexagonal web openings under impact loads is crucial for the anti-impact design of structures. The dynamic characteristics are complex, and the maximum displacement can be obtained experimentally, although the time cost, and expense of such experiments are high. In this context, a 6-5-1 artificial neural network model based on the Levenberg-Marquardt backpropagation algorithm was developed, incorporating 5-fold cross-validation techniques. Due to limited experimental data, 324 high-precision finite element models were created in bulk using Python-based ABAQUS software scripts to provide additional numerical data, and 26 specimens were designed for drop hammer tests to validate the finite element models. The artificial neural network model on the test data yielded a mean squared error of 0.0268, a mean absolute error of 0.0014, and a coefficient of determination value of 0.9806, with samples having an error of less than 10% comprising 77.16% of the total samples. Using Garson’s algorithm, the contribution of input features was estimated, and a full parameter analysis of the proposed model was conducted. The results indicate that the most significant factor is the impact mass, which accounts for 37.58%. When the impact mass increased from 530 kg to 630 kg, the maximum displacement of Q235 surged by 93.75%. The factor with the least impact is the opening spacing, accounting for 2%, with an average increase in maximum displacement of only 3.45%. Finally, ANN-based equations that can be used as design tools are established.

Modeling of Mode I crack-tip dislocation nucleation in three FCC materials: Ni, Cu and Al

An accurate estimation of the critical stress intensity factor for crack tip dislocation nucleation under Mode I loading is of great importance to determine whether a material is intrinsically ductile or not. Here, shear displacements and energy change at crack tip in FCC nickel, copper and aluminum are investigated during Mode I fracture process using atomistic simulations. In light of our simulation results, a new shear resistance model is formulated by a general Fourier expansion with coefficients identified by the computed energy curve. The new model involving the step formation energy which can be regarded as a new parameter and unstable stable stacking fault energy, reduces to Rice theory if no step exists. The criterion for nucleation is developed based on the idea that crack tip behaviors are controlled by the shear resistance and the maximum point serves as an obstacle to conquer. The predictions of the critical shear displacement corresponding to maximum shear resistance position and the critical nucleation energy show good agreement with simulation results. In addition, the new model can be further utilized to study the effect of complex stress state on Mode I crack tip dislocation nucleation.

Large dump critical zone of interest identification and slope failure prediction using realistic 3D numerical modelling

Dump failure is one of the most catastrophic hazards in the opencast mining industry and involves sudden and violent failures. Therefore, susceptible region identification is essential for slope failure mitigation in the large dump. This study proposed a sustainable approach for large dump critical zone of interest (CZI) identification, stability monitoring and slope failure prediction. UAV close-range photogrammetry survey and structure-from-motion (SfM) approach were used for the dump realistic 3D model reconstruction. 3D numerical modelling was employed for dump stability analysis. In this investigation, the dump critical zone, slope factor of safety (FOS), displacement and slope failure prediction were analysed using the 3D numerical modelling. The results indicate that maximum displacement was found in the critical zone I. However, the dump was stable under static conditions. Dump critical slope failure time is predicted using the inverse velocity method. The results of this investigation are reliable and establish the scope of UAV photogrammetry for realistic 3D modelling. In addition, the slope stability state and failure model were consistent with field observations. The proposed approach can be used for the mine, hill, and roadcut slopes. UAV technology and 3D modelling approaches have substantially reduced data collection time and expense.

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). The present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

Mid-span displacement and damage degree predictions of RC beams under blast loading using machine learning-based models

Civil engineering has extensively employed RC beams in various applications. However, these beams are susceptible to damage and failure under blast loading. Assessing damage of RC beams from blasts requires real measurement data and time-intensive numerical simulation, making rapid evaluation a significant challenge. In this study, machine learning (ML) models were developed to swiftly evaluate the damage degree and predict the mid-span displacement of RC beams, comprising three models for each problem. To compensate for the lack of real experiment data, a total of 1041 data points are generated through numerical simulation using ABAQUS to create the training dataset. Additionally, for displacement prediction models, 39 collected measurement data was integrated. Initially, the ML models were established to predict the peak mid-span displacement of RC beams under explosion, with inputs including twelve RC beam structural parameters and two blast load parameters, and the output being the maximum min-span displacement of the beam. Subsequently, the ML models were developed using the same fourteen parameters as inputs, with the output being the beam’s damage degree and failure mode. These models achieved adequate performance, demonstrated by evaluation metrics such as R2, RMSE, MAE, MAPE, with values of 0.95, 5.468 mm, 3.16 mm, 17.7 %, respectively, for regression problem. For the classification problem, metrics such as accuracy, precision, recall, and f1-score were employed, with an overall value of 0.837 for the best model. The results show that the developed ML models can precisely and rapidly assess the damage degree, failure mode and predict the mid-span displacement of the RC beam under blast load, which provides a good method for the anti-explosive protection design of RC beams. The graphical user interface tool was also established for ease of use.

Seismic performance of sheet pile walls in liquefiable soil using centrifuge tests and an assessment of nonlinear effective stress analysis using PM4Sand

Past earthquakes have revealed that damage to sheet-pile walls under saturated conditions is closely linked to excess pore water pressure buildup in the surrounding soil. Nonlinear effective stress analysis (ESA) is commonly employed to assess the seismic performance of sheet-pile walls in liquefiable soils, incorporating constitutive models for liquefaction simulation. However, ESA results are sensitive to uncertainties in input parameters, model calibration, and modeling techniques. Dynamic centrifuge tests conducted in the Liquefaction Experiments and Analysis Project (LEAP) offer valuable insights into important response mechanisms and validate ESA. Seven centrifuge tests on a cantilevered sheet-pile wall model showed that liquefaction did not occur in the backfill near the wall due to net seaward wall displacement but did occur farther away. In addition, the mechanism of wall displacement was mainly due to the shear deformation of the softened backfill, with the displacement magnitude depending on the relative density of soil, peak ground acceleration of base motion, and wall displacement during gravity loading. Nonlinear ESA was performed for three centrifuge tests using FLAC2D and the PM4Sand constitutive model for soil. Gravity analysis captured static wall displacement and initial stress distribution in the soil. Two calibrations of the PM4Sand model were pursued at the element level: C1 calibration for liquefaction strength and C2 calibration for liquefaction strength and the post-liquefaction shear strain accumulation rate. System-level simulations showed similar liquefaction behavior as observed in the tests for both calibrations. However, the C2 calibration provided closer predictions of wall displacements, while the C1 calibration (default for PM4Sand) resulted in larger and more conservative displacements. Overall, the PM4Sand model performed well with minimal calibration, making it suitable for nonlinear ESA of sheet-pile walls.

Seismically induced deformations of layered slopes with non-unique and non-planar slip surfaces

Earthquake-induced deformation is prevalently used to evaluate the seismic performance of slopes, often assumed to occur along a predefined single slip surface. This paper presents a new limit equilibrium method combining with Newmark sliding block analysis for calculating the seismic displacement of layered slopes with non-unique and non-planar slip surfaces. The determination of the most and subsequent critical slip surfaces during the earthquake shaking is discussed with an emphasis on the coupling effects between the shallow and deep failures. The approach is validated by the results from numerical simulation and the shaking table model test of layered slopes. Comparison analyses are also performed for the predictions of seismic displacement from the developed and previous sliding-block models. It is found that the seismic sliding deformation at the first slip surface significantly affects the evolution and sliding deformation of the subsequent slip surface. For the considered example of layered slope, a second slip surface at deeper location passing through the slope toe is identified, and a coupled variation is observed for the sliding displacement along the two slip surfaces. These features are not well predicted from the available sliding-block models. The developed model can capture the interaction between the non-unique slip surfaces and the geometry effect of the non-planar slip surfaces.

Dynamic prediction model of landslide displacement based on (SSA-VMD)-(CNN-BiLSTM-attention): a case study

Accurately predicting landslide displacement is essential for reducing and managing associated risks. To address the challenges of both under-decomposition and over-decomposition in landslide displacement analysis, as well as the low predictive accuracy of individual models, this paper proposes a novel prediction model based on time series theory. This model integrates a Convolutional Neural Network (CNN) with a Bidirectional Long-Short Term Memory network (BiLSTM) and an attention mechanism to form a comprehensive CNN-BiLSTM-Attention model. It harnesses the feature extraction capabilities of CNN, the bidirectional data mining strength of BiLSTM, and the focus-enhancing properties of the attention mechanism to enhance landslide displacement predictions. Furthermore, this paper proposes utilizing the Variational Mode Decomposition (VMD) method to decompose both landslide displacement and its influencing factors. The VMD algorithm’s parameters are optimized through the Sparrow Search Algorithm (SSA), which effectively minimizes the influence of subjective bias while maintaining the integrity of the decomposition. A fusion of the Maximal Information Coefficient (MIC) and Grey Relational Analysis (GRA) is then employed to identify the critical influencing factors. The selected sequence of factors that conforms to the criteria is used as the input variable for displacement prediction via the CNN-BiLSTM-Attention model. The cumulative displacement prediction is derived by aggregating the results from each sequence. The study reveals that the SSA-VMD-CNN-BiLSTM-Attention model introduced herein achieves superior predictive accuracy for both periodic and random term displacements than individual models. This advancement provides a dependable benchmark for forecasting displacement in similar landslide scenarios.

Slope multi-step excavation displacement prediction surrogate model based on a long short-term memory neural network: for small sample data and multi-feature multi-task learning

ABSTRACT Machine learning-based surrogate models have become the preferred approach for large-scale and frequent simulation tasks due to its significant improvement in computational efficiency. In order to overcome the potential effects of learning with small sample data and the challenges of multi-feature multi-task learning, we developed a novel deep learning long short-term memory (LSTM) model. Taking slope excavation displacement prediction as a case study, we employed the Latin hypercube sampling method to generate a synthetic dataset for training LSTM and other mainstream models. Experimental results demonstrate that the model's prediction accuracy decreases with a reduction in sample size, while support vector regression (SVR), back propagation neural network (BPNN), LSTM and Gaussian process regression (GPR) demonstrate a stronger resistance. It is feasible to utilise excavation features as model inputs to establish a unified multi-step excavation model, but the accuracy of the SVR model decreased by 32.5% after supplementing excavation features. Even when the sample size is less than 50, both LSTM and GPR exhibit excellent performance, achieving model R-squared and RMSE surpassing 0.99 and 0.07 mm. However, when addressing multi-output learning tasks, LSTM stands out as the optimal choice. This study will assist researchers or engineers in swiftly selecting appropriate surrogate models.

Landslide displacement prediction based on time series and long short-term memory networks

Landslide displacement prediction based on time series and long short-term memory networks

Landslide displacement prediction model based on multisource monitoring data fusion

This study presents a displacement prediction model that integrating various monitoring data sources to comprehensive landslide monitoring information utilization. The research focuses on the landslide in Lingwan Village, utilizing Ridge Regression (RR) to integrate diverse monitoring data. The fused displacement data undergo decomposition into trend and periodic terms using the Smoothness Priors Approach (SPA). The Autoregressive Integrated Moving Average (ARIMA) model predicts trend and periodic term displacements separately, which are then combined to forecast the overall landslide surface displacement accurately. The RR model demonstrates a strong correlation coefficient of 0.998, indicating high predictive ability. Trend term displacement prediction outperformed periodic term prediction, especially during the rainy season. The RR-SPA-ARIMA model has an average absolute error of 1.73 mm, a mean bias error of −0.08 mm, and a root mean square error of 1.87 mm. The model demonstrates high prediction accuracy.

Multifractal Characteristics and Displacement Prediction of Deformation on Tunnel Portal Slope of Shallow Buried Tunnel Adjacent to Important Structures

The tunnel portal section is often in extremely weak and fragmented strata, and the deformation of the portal side and slope will affect the stability of the surrounding rock and the tunnel-supporting structure. However, the deformation characteristics and displacement development patterns of slopes in the tunnel portal section are not clear. In this paper, the multifractal characteristics and displacement prediction of the deformation sequence of the tunnel portal slope at of a weak and water-rich shallow buried tunnel adjacent to an important structure are studied in depth. Combined with the deformation characteristics of the tunnel portal slope, a suitable slope monitoring and measurement scheme is designed to analyze the deformation pattern of the tunnel portal slope. Based on the multifractal detrended fluctuation analysis (MF-DFA) method, the multifractal characteristics of the deformation monitoring sequences at each monitoring point of the tunnel portal slope are analyzed. The multifractal characteristics of displacement sequences at different monitoring points of the tunnel portal slope are consistent with the actual monitoring results. Furthermore, the Long Short-Term Memory (LSTM) model is optimized using the Particle Swarm Optimization (PSO) algorithm to predict the deformation of the tunnel portal slope. The results show that the maximum mean square error (MSE) of the horizontal displacement test set prediction results is 0.142, and the coefficient of determination (R2) is higher than 91%. The maximum value of MSE for vertical displacement test set prediction is 0.069, and the R2 are higher than 91%. The study shows that the performance of the PSO-LSTM prediction model can meet the requirements for predicting the displacement of the tunnel portal slope. Based on the MF-DFA method and PSO-LSTM prediction model, the fluctuation characteristics of the displacement value of the tunnel portal section can be accurately identified and the displacement development pattern can be effectively predicted. The conclusions of the study are of great practical significance for the safe construction of the tunnel portal section.

Forecasting and uncertainty analysis of tailings dam system safety based on data mining techniques

Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters.

Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach