Deformation Prediction Model Research Articles

Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation

ABSTRACT Applying digital twin technology to the tunnel excavation process enables intelligent tunnel construction, which is critical for ensuring construction safety and advancing intelligent building methods. However, the complex construction environment, involving numerous environmental factors and dynamic changes, poses challenges for traditional deformation prediction and analysis methods. These methods fail to systematically integrate the elements of the excavation scene to achieve accurate prediction. Therefore, this paper proposes a data-knowledge hybrid driven intelligent prediction method of tunnel excavation profile geometric deformation. A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. The experimental results demonstrate that the method proposed in this paper can significantly integrate and analyse the deformation elements of the tunnel excavation scene. It achieves intelligent prediction of the tunnel excavation profile geometric deformation, with reductions of 57.46%, 76.00%, and 53.12% in MAE, MSE, and RMSE, respectively, compared with the traditional prediction model. This method effectively enhances the deformation prediction accuracy of the tunnel excavation profile twin model.

Data-driven deformation prediction model for super high arch dams based on a hybrid deep learning approach and feature selection

Data-driven deformation prediction model for super high arch dams based on a hybrid deep learning approach and feature selection

A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM

This paper presents a deformation prediction model for concrete dams that integrates a reptile search algorithm (RSA), a Variational Mode Decomposition (VMD) algorithm, and a long short-term memory network model with attention mechanism (AttLSTM). This model utilizes the RSA to optimize the parameters K and α of the VMD algorithm. It combines the variance of the modified mode with the sample entropy of these data as the objective function, effectively converting monitoring data into a stable signal while retaining essential characteristic variation. Data are reformatted into a three-dimensional structure and partitioned into training and testing sets. The AttLSTM network was applied to forecast deformation, and results were validated using practical engineering cases. The performance of the proposed model was compared against that of four other models: LSTM, VMD-LSTM, attention LSTM, and VMD-AttLSTM models. Analysis of the five evaluation criteria revealed that the RSA can better optimize the parameters of the VMD algorithm. Consequently, the proposed model demonstrates superior noise reduction capabilities and improved prediction accuracy.

Investigation of indoor and field tests on asphalt pavement with inverted asphalt layers based on the vertical vibration compaction method

An inverted asphalt pavement is created by reversing the sequence of the lower and middle layers in a conventional asphalt pavement. The lower layer is composed of material with larger particle size and lower asphalt content, which improves its ability to withstand deformation caused by rutting. On the other hand, the middle surface has a higher asphalt content, specifically designed to resist fatigue cracking. This paper examines the mechanical response of two pavement structures and investigates the potential of two measures, inverted asphalt pavement and asphalt mixture design by vertical vibration compaction method (VVCM), in reducing stresses and stress levels in asphalt pavements. Additionally, a large thickness rutting and fatigue test method was developed to study the rutting resistance and fatigue life of the pavement structures, and to construct rutting deformation and fatigue life prediction models. Finally, test sections were paved to verify the feasibility of the inverted pavement and VVCM materials. The findings show that inverted pavement and VVCM materials have a minimal impact on pavement stress, but can reduce pavement shear and tensile stress levels by up to 18% to 25%. Furthermore, inverted pavement and VVCM materials have positive effects on improving the rutting resistance and fatigue life of asphalt pavements.

A novel surface deformation prediction method based on AWC-LSTM model

Severe surface deformation can damage the ecological environment, trigger geological disasters, and threaten human life and property. Reliable surface deformation prediction is conducive to reducing potential risks and mitigating disaster losses. Currently, machine learning-based surface deformation prediction models have shown significant improvements in prediction performance. However, most prediction models do not sufficiently consider the characteristics of surface deformation, exhibit subjectivity in parameter settings, and inadequately capture local features in time series data. We introduce the AWC-LSTM model to predict surface deformation. Initially, leveraging the strengths of the autoregressive integrated moving average (ARIMA) model in handling linear signals, the obtained surface deformation information is decomposed to linear and nonlinear parts, and the linear part is predicted. Secondly, by incorporating convolutional neural network (CNN) layers into the long short term memory (LSTM) model, the ability to learn local features is enhanced and the whale optimization algorithm (WOA) is introduced to determine the optimal hyperparameters of the model, thereby predicting nonlinear deformation. The proposed AWC-LSTM model was validated using the Shilawusu coal mine and Beijing as case studies. The outcomes indicate that the deformation predictions for the Shilawusu coal mine and Beijing exhibit a high degree of consistency with the monitored data, with root mean square errors (RMSE) not exceeding 3 mm. This underscores the model’s reliability and applicability across different areas. Comparisons with existing prediction models indicate that the AWC-LSTM model achieves higher predictive accuracy, with an average improvement in accuracy ranging from 28.38 % to 80.59 % over other models.

Analytical Method for Predicting Tunnel Deformation Caused by Overlying Excavation Considering the Dewatering

ABSTRACTPredicting the tunnel deformation caused by overlying excavation is a crucial catch in current rail transit construction. Most theoretical studies overlook the effect of dewatering on tunnel response. This study utilized Mindlin's stress solution and an improved incremental method to calculate the additional load resulting from excavation. The load increment due to dewatering was determined based on the effective stress principle and a modified Dupuit infiltration curve. Treating the tunnel as a series of segment rings on a Vlazov foundation, the energy variance method and a collaborative deformation model were employed to develop a tunnel deformation prediction model. The proposed model's validity was confirmed by examining two traceable cases. Based on this, further discussion addressed the impact of dewatering depth, tunnel burial depth, tunnel‐pit relative distance, and intersection angle on existing tunnel deformation. Finally, combining the proposed model with multiple regression analysis, a semi‐empirical method was established to determine the tunnel's maximum vertical displacement. The study found that dewatering extends the tunnel force range, and the tunnel subsidence was positively correlated with groundwater level drop depth. The increase in the tunnel burial depth and distance from the excavation center led to a longer soil unloading transfer path, resulting in reduced disturbance to the tunnel. A smaller intersection angle between the excavation's short edge and the tunnel led to less disturbance of the underlying tunnel, which should be avoided in practical engineering. The developed semi‐empirical formulae have sufficient engineering accuracy, guiding the reinforcement of the underlying tunnel.

Research on deformation prediction of CFETR multi-purpose overloaded robot based on real-time structural simulator

The CFETR Multi-Purpose Overload Robot (CMOR) is a key subsystem of the China Fusion Engineering Test Reactor (CFETR), which can perform maintenance tasks of the internal components. However, the large slenderness ratio of the structure results in low control accuracy of the CMOR end-effector. This paper proposes a CMOR deformation prediction method based on the real-time structural simulator. The overall deformation model of CMOR is analyzed based on the single joint deformation mechanism. Based on the principle of layered control, the control framework of the CMOR structural simulator is constructed, and the deformation data of CMOR in different positions are calculated based on the finite element method. A hybrid neural network containing a multilayer perceptron, transformer, and attention mechanism is designed to train the CMOR deformation prediction model. The training results show that the deformation prediction model converges quickly and fits the deformation of the CMOR structure well with small prediction errors. Finally, the real-time structure simulator is developed based on the deformation prediction model, and the deformation of CMOR is reconstructed with the update frequency of 2 Hz and the absolute error at any point within ±10 mm, which verifies the correctness of the CMOR structural deformation prediction method.

Research on online deformation monitoring of thin-walled parts driven by digital twin

Thin-walled parts are widely used in aerospace. Because thin-walled parts have thin walls, weak stiffness, and are easy to deform and other characteristics, it is easy to produce deformation in the processing, affecting the quality and accuracy of the workpiece processing. Therefore, accurate and efficient online real-time monitoring of the deformation of thin-walled parts has become a vital issue in process control. The visualization, real-time monitoring, and iterative optimization of digital twin technology can effectively solve the problem of online monitoring of thin-walled parts processing. Therefore, this paper proposes a digital twin driven deformation monitoring method for thin-walled parts, which realizes the effective monitoring of deformation during the processing of thin-walled parts. Firstly, the digital twin architecture for online deformation monitoring of thin-walled parts is established, and the critical technologies of the digital twin for online deformation monitoring of thin-walled parts are proposed. Secondly, the Support Vector Regression (SVR) finite element surrogate model is established to realize the rapid simulation of thin-walled parts cutting. At the same time, a One-Dimensional Convolutional Neural Network (1DCNN) combined with the Self-Attention Mechanism (SAM) deformation prediction model was established based on sensor data. The real-time monitoring and accurate prediction of the deformation of thin-walled components are realized through the parallel guidance of the deep learning and surrogate model. Finally, an experimental analysis was carried out by machining thin-walled plates of aluminum alloy 7075, and the fit of the monitoring models was above 95 %, thus verifying the validity of the proposed method.

Development and verification of rutting prediction model based on variable frequency load test

ABSTRACT An innovative rutting test method was developed based on the existing rutting prediction model to improve the rutting prediction model used in the asphalt pavements. The loading mould was improved based on test requirements and equipment characteristics, and small-scale and full-scale variable frequency load rutting tests were conducted. Nonlinear multiple regression analysis was conducted on the test results using numerical analysis software, converting driving speed into loading frequency and incorporating it into the rutting prediction model. This resulted in a permanent deformation prediction model for asphalt mixtures, incorporating factors such as temperature, pressure, loading cycles, loading frequency, and pavement thickness. Fourteen highways were selected as validation sections. The validation results showed that the average error of the prediction model established in this study was 15.48%, compared to 24.42% and 27.94% for the other two models. For different grades of highways, the load frequency parameter can distinguish the rutting conditions under different driving speeds, making the rutting prediction model established in this study more accurate compared to measured values.

A novel method for predicting carburizing and quenching deformation of the gear with the mandrel based on carburizing expansion strain and dynamic thermal boundary conditions of quenching

During the carburizing quenching process, the interaction of multiple physical fields leads to significant nonlinearity in the mechanical properties and physical characteristics of the material, making prediction of carburizing quenching deformation particularly challenging. Based on the phase transformation thermo-elastic-plastic constitutive equation, this paper innovatively incorporated the deformation of interstitial solid solution induced by carburization, and integrated experimental data on material properties for 8620RH, a multi-field model for carburizing quenching was constructed. The accuracy of the proposed deformation prediction model was validated through experiments on cylindrical samples and the calculation results from specialized heat treatment software Dante. Based on the deformation prediction model, considering the flow effect of quenching medium in gear quenching process, and introducing dynamic heat boundary conditions during quenching, a finite element model for predicting carburizing and quenching deformation of the gear with the mandrel was constructed to study the deformation distribution of the gear. The accuracy of the deformation prediction model was verified through the carburizing quenching experiment of the gear with the mandrel. The results show that compared with the classical model which does not consider the carburizing expansion strain and the dynamic thermal boundary conditions, the proposed model demonstrates higher predictive accuracy in torsional deformation and expansion deformation. The research work provides a novel model and method for improving the accuracy of predicting carburizing and quenching deformation in gears.

A multipoint prediction model for the deformation of concrete dams considering climatic features of high-altitude regions

High-altitude regions are characterized by natural climatic features such as low temperatures and high radiation. Concrete dams built in these regions face more complex operating conditions. Traditional single-output deformation prediction models ignore the correlations between different monitoring points, making it difficult to assess the overall deformation behavior of concrete dams. The black-box nature of some machine learning models can lead to a lack of interpretability, affecting the practical application. Therefore, a multipoint prediction model (MPM) is proposed to analyze the deformation behavior of concrete dams in high-altitude regions. Firstly, the deformation monitoring points are divided based on the climatic features of high-altitude regions. Subsequently, the MPM is constructed using the multi-output CatBoost model and Quasi-Monte Carlo algorithm to achieve deformation prediction across different partitioned monitoring points. Finally, the elucidation of the MPM optimization procedure in a transparent manner alongside the quantification of the influence exerted by input variables on output outcomes notably augments the interpretive capacity of the MPM. The proposed model is validated using monitoring data from a concrete dam in a high-altitude region. The disparities in chaotic and entropy variation features among deformation monitoring points in different zones corroborate the appropriateness of the zoning in this study. Comparisons between the single-output CatBoost model and MPM verify the feasibility and efficiency of multipoint predictions. Comparisons with other multi-output prediction models validate the superior predictive performance of the MPM. Additionally, the proposed model's generalization performance is validated using data from another concrete arch dam in high-altitude regions.

Hybrid deep learning approach for rock tunnel deformation prediction based on spatio-temporal patterns

The ability to predict tunnel deformation holds great significance for ensuring the reliability, safety, and sustainability of tunnel structures. However, existing deformation prediction models often simplify or overlook the impact of spatial characteristics on deformation by treating it as a time series prediction issue. This study utilizes monitoring data from the Grand Canyon Tunnel and introduces an effective data-driven method for predicting tunnel deformation based on the spatio-temporal characteristics of the historical deformation of adjacent sections. The proposed model, a combination of graph attention network (GAT) and bidirectional long and short-term memory network (Bi-LSTM), is equipped with robust spatio-temporal predictive capabilities. Additionally, the study explores other possible spatial connections and the scalability of the model. The results indicate that the proposed model outperforms other deep learning models, achieving favorable root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values of 0.34 mm, 0.23 mm, and 0.94, respectively. The graph structure based on intuitive spatial connections proves more suitable for meeting the challenges of predicting deformation. Integrating GAT-LSTM with transfer learning technology, remains stable performance when extended to other tunnels with limited data.

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%.

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

Surface Subsidence Modelling Induced by Formation of Cavities in Underground Coal Gasification

Underground coal gasification (UCG) is an efficient method for the conversion of deep coal resources into energy. The scope of this work is to model the subsidence of four gasification cavities with a size of 30 m × 30 m × 15 m, separated by 15 m wide pillars. Two scenarios of gasification sequence are modelled, one with the gasification of cavities 1 and 2 followed by 3 and 4, and the other one with the sequence of cavities 1 and 3, followed by 2 and 4. The results show that the final surface subsidence after gasification of four cavities is 9.8 mm and the gasification sequence has an impact only on the subsidence at the intermediate stage but has no impact on the final subsidence after all four cavities are formed, when only the elasticity regime is considered. Additionally, the maximum surface subsidence for the studied cavities of different sizes ranges from 0.016 mm to 7.14 mm, and the relationship between the subsidence and the cavity volume is approximately linear. Finally, a prediction model of surface subsidence deformation is built up using the elastic plate theory, and the formula of surface deformation at a random point is given. The maximum difference between measured and calculated deformation is 4.6%, demonstrating that the proposed method can be used to predict the ground subsidence induced by UCG.

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.

Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble

Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian white noise as a random factor and uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to decompose the data set finely. Each modal component is evaluated by sample entropy (SE) analysis so that the data set can be reconstructed according to the sample entropy value to retain key information. In addition, the model uses partial autocorrelation function (PACF) to determine the correlation between intrinsic modal function (IMF) and historical data. Then, the global search whale optimization algorithm (GSWOA) is used to accurately determine the parameters of kernel extreme learning machine (KELM), which forms the basis of the dam deformation prediction model based on multi-scale adaptive kernel function. The case analysis shows that CEEMDAN-SE-PACF can effectively extract signal features and identify significant components and trends so as to better understand the internal deformation trend of the dam. In terms of algorithm optimization, compared with the WOA algorithm and other algorithms, the results of the GSWOA algorithm are significantly better than other algorithms and have the optimal convergence. In terms of prediction performance, CEEMDAN-SE-PACF-GSWOA-KELM is superior to the CEEMDAN-WOA-KELM, GSWOA-KELM, CEEMDAN-KELM, and KELM models, showing higher accuracy and stronger stability. This improvement is manifested in the decrease of root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) and the improvement of the R square (R2) value close to 1. These research results provide a new method for dam safety monitoring and evaluation.

Temperature-dependent creep damage mechanism and prediction model of fiber-reinforced phenolic resin composites

Carbon fiber-reinforced phenolic resin (CF/PF) and glass fiber-reinforced phenolic resin (GF/PF) composites are crucial for thermal insulation and load-bearing capabilities in the aerospace industry. Understanding their creep behavior at various temperatures is essential. This study investigates the impact of temperature on the bending creep properties of CF/PF and GF/PF composites, focusing on deformation response, damage mechanisms, and prediction models. Results reveal that the decrease in creep properties at elevated temperatures is mainly attributed to the increased viscoplastic strain. At room temperature, CF/PF composite demonstrates exceptional creep properties. CF/PF composite exhibits heightened sensitivity to elevated temperatures, undergoing creep rupture at 210 °C and 240 °C with failure strains of 0.48 % and 0.49 %, respectively. High-temperature damage mechanisms have been analyzed in depth using a combination of thermal stress finite element simulation and microscopic characterization. Thermal stress is directly related to the difference in thermal expansion between the fibers and the resin. The influence of fiber distribution and geometry results in the interface being subjected to both normal and shear stresses. The accumulation of interfacial damage increases the plastic deformation and degrades creep performance. Additionally, an advanced temperature-dependent Burgers model has been developed and effectively predicted the creep curves under different temperatures and loads. This model demonstrated great potential as a general predictive model.

Intelligent prediction model of tunnelling-induced building deformation based on genetic programming and its application

Intelligent prediction model of tunnelling-induced building deformation based on genetic programming and its application

Deformation Prediction Model of Existing Tunnel Structures with Equivalent Layered Method–Peck Coupled under Multiple Factors

The existing tunnel structure, the new underpass tunnel structure and the rock strata in the area of influence of the crossover tunnel are interacting systems that are affected by various factors, such as dynamic and static excavation loads and dynamic and static train loads. The existing theoretical models for the deformation prediction of existing tunnels lack the synergistic analysis of dynamic and static loads on both existing and new tunnels. Based on the theory of the current layer method and Peck’s empirical formula, this paper considers the stiffness of existing tunnels, the stiffness of new tunnels, the loads of excavation methods and the loads of existing tunnels. The results show that a theoretical model for the prediction of the deformation of double-lane highway tunnels underneath existing railroad tunnels with the coupling of the current layer method and Peck under multiple factors is constructed; a modified Peck settlement formula for the base plate of the existing tunnels is put forward; and, through numerical calculations and monitoring data for validation and optimization, it is proved that the theoretical model is applicable to the excavation of tunnels underneath mountainous areas mined by the blasting method.