Earth Pressure Balance Shield Machine Research Articles

A comprehensive hybrid method for performance evaluation of a modified earth pressure balance shield machine in mixed strata

A comprehensive hybrid method for performance evaluation of a modified earth pressure balance shield machine in mixed strata

Intelligent optimization prediction of screw conveyor speed for earth pressure balance shield machine based on complete ensemble empirical mode decomposition of adaptive noise and deep learning

The excavation stratum of the earth pressure balance (EPB) shield machine is complex and changeable. For ensuring the excavated interface stabilization, the rotational speed of screw conveyor must be precisely controlled to maintain the balance of earth pressure in sealed cabin and to avoid accidents such as ground collapse or upliftment. A data-driven intelligent optimal model is presented by the paper, which organically integrates the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN), sparrow search algorithm (SSA) and long short-term memory (LSTM) network to achieve accurate prediction of screw conveyor rotation speed. Firstly, Pearson correlation analysis method is used to analyze the historical data of shield tunneling, and the tunneling parameters strongly related to the change of screw conveyor rotation speed are obtained as model input. Secondly, the CEEMDAN is used to decompose the obtained parameter data set into several modal components, and then the denoised data is reorganized to capture the changing characteristics of the original data and reduce the complexity of the data. Then, the parameters such as the learning rate of the LSTM network are optimized by using the strong global search ability of SSA to enhance the prediction precision further in the model. Finally, based on decomposed and reorganized data, SSA-LSTM is used to establish a rotation speed intelligent prediction model realize precise controlling of screw conveyor rotation speed. The simulation experiment is carried out by using the construction data of a section of Beijing Metro Line 10, and the results of three evaluation indexes of the model are given: Mean Absolute Error (MAE) is 0.02878, Mean Absolute Percentage Error (MAPE) is 0.00882 and R2 is 0.99909, which shows that the model has good prediction performance. The control effect of the method on the earth pressure balance of the sealed cabin is tested, and the maximum error value is 0.25%, which verifies the engineering applicability of the method.

Geological adaptive intelligent control of earth pressure balance shield machine based on deep reinforcement learning

Scientific and precise control of tunnelling parameters is of utmost importance during the construction of shield machines. Given the complexity of the working environment, manual operation is highly prone to causing safety accidents. Therefore, achieving intelligent control of the shield machine is crucial. Based on this, this paper proposes a geological adaptive intelligent control method of earth pressure balance shield machine using the Deep Deterministic Policy Gradient (DDPG) algorithm as the framework, with Actor-Critic as the basis. Firstly, DDPG agent is constructed to replace the screw conveyor control system as the main body of strategy implementation. Secondly, an environmental model is established by utilizing the mechanism model between the sealed cabin pressure and the screw conveyor speed. The real-time sealed cabin pressure, target pressure, and pressure error serve as the state space, while the screw conveyor speed is used as the action space. A combined reward function is set based on safety and accuracy. Finally, the Actor network interacts with the environment under the supervision of the reward function and Critic network. Successful training is achieved when the cumulative reward value is maximized, resulting in the output of optimal control strategy. In this paper, the method dynamically regulates the screw conveyor speed by interacting with the geological environment, to realize the precise control of the sealed cabin pressure and ensure the dynamic balance between sealed cabin pressure and excavation face pressure. The test results show that this method has a good control effect on the sealed cabin pressure under various geological conditions, and can complete 72 kinds of soil transition tasks. It has strong soil adaptability and can respond well to the dynamic changes of soil conditions. This approach enhances the intelligence of the shield machine, mitigating inaccuracies attributed to human operation, which provides a guarantee of safe shield machine operation, whilst exhibiting valuable engineering applications.

Study on the Distribution Law of External Water Pressure with Limited Discharge during Shield Construction of Soft Rock Tunnel in Western Henan Province

When shield tunneling is carried out in poor geological areas with high water head and low strength, there is a great construction risk, and the external water pressure is one of the key factors affecting the stability and safety of the tunnel during shield tunneling. Taking the Yinguruxin tunnel of Xin’an County as the engineering background, the geological conditions and water-bearing characteristics of the water-rich area in the tunnel excavation path are analyzed by means of drilling and high-density electrical method, and the pumping test is used to evaluate the groundwater linkage in the water-rich area. The 2022 version of Midas GTS NX is used to study the distribution characteristics of external water pressure during tunnel excavation in fault zones, and the influence of different drainage rates on the external water pressure of the tunnel is analyzed. The results show that the rock mass in the unfavorable geological section of the tunnel excavation is broken and rich in water, but the complexity of the stratum leads to uneven water richness in the axis direction of the tunnel. The drainage rate is the key to affecting the external water pressure of the lining. The drainage rate is the key to affecting the external water pressure of the tunnel. The correct drainage rate is an effective measure to reduce the external water pressure of the tunnel. The internal and external water pressure of the tunnel decreases with the increase of the drainage rate. When the drainage rate reaches 66.67% of the water inflow, the external water pressure of the tunnel can be reduced to 0.3 MPa to ensure the safety of the earth pressure balance shield machine in the tunneling process. The conclusion provides a useful reference for the high water pressure control of the tunnel shield tunneling process.

Soil conditioning of clay based on interface adhesion mechanism: Microscopic simulation and laboratory experiment

Clogging frequently occurs in the cutter head, excavation chamber or screw conveyor when an earth pressure balance (EPB) shield machine is tunneling in soft or silty clay ground with high clay mineral content. In this paper, montmorillonite, kaolinite, and illite were selected as research objects, and molecular dynamics simulation and laboratory experiment were adopted. At the microscopic scale, dynamic contact behavior and interfacial mechanical behavior of the interface between clay minerals and water/surfactant solution was simulated and the interfacial adhesion and conditioning mechanism between clay minerals and water/surfactant solution was revealed. Thus, sodium dodecyl benzene sulfonate surfactant was selected as the main composition of the soil conditioner. Then, the adhesion stress before and after soil conditioning and the contact angles between clay minerals and water/surfactant solution were tested and analyzed at the macroscopic scale. The result shows that the contact angle between droplet and clay mineral surface is an important parameter to characterize soil adhesion. The simulation rules of the microscopic contact angle are consistent with the experiment results. Furthermore, the adsorption energy between microscopic substances is dominated by electrostatic force, which can reflect the adhesion stress between macroscopic substances. Soil adhesion stress can be effectively decreased by adding the surfactant to the soil conditioner.

In-Situ Dehydration Test of Shield Spoil in Mudstone and Pebble Soil Composite Stratum

When earth pressure balance (EPB) shield machine is driving in mudstone sand pebble composite stratum, it will produce a large amount of spoil with high water content. The high moisture content of spoil not only increases the difficulty and cost of transportation, but also easily leads to environmental pollution problems. In this paper, based on the shield tunnel project of Chengdu Metro Line 30, the hydraulic characteristic of shield spoil was tested, and on this basis, laboratory tests of shield spoil dehydration were carried out according to the designed precipitation scheme. The results show that: 1) The moisture content of the EPB shield spoil in the mudstone sand pebble composite stratum is as high as 55.2%, and its permeability coefficient is basically the same as that of silt, and the shield residue has the condition of in-situ dehydration. 2) The dehydration amount and residue moisture content of spoil in slope-type spoil pit showed a linear relationship with time, and the dehydration rate is slow and the total precipitation is less. In other schemes, the dehydration amount and water content of spoil increase rapidly in the first 24 hours and slowly in the later period. 3) Considering the total amount and speed of dehydration within 24 hours, the dehydration effect of spoil pit is ranked from high to low: 3D gravel cage wall spoil pit > 3D spoil pit with vacuum pumping > 3D permeable stone spoil pit > 3D spoil pit > Slope-type spoil pit. 4) 3D gravel cage wall spoil pit has high dewatering efficiency, and vacuum pumping can assist the discharge of water. It is recommended to use 3D gravel cage wall spoil pit and vacuum pumping scheme for on-site EPB tunnel spoil dewatering.

Experimental study on workability and permeability of sandy soils conditioned with thickened foam

Water spewing and muck plugging often occur during earth pressure balance (EPB) shield machines tunnelling in water-rich sandy strata, even though the conventional foam has been employed to condition sandy soils. In this study, a novel thickened foaming agent suitable for EPB shield tunnelling in water-rich sandy strata is developed. In contrast to conventional foam-conditioned sands, the thickened foam-conditioned sand has a low permeability due to the consistent filling of soil pores with the thickened foam, and the initial permeability coefficient decreases by approximately two orders of magnitude. It also exhibits a suitable workability, which is attributed to the enhanced capability of the thickened foam to condition sandy soils. In addition, the effect of concentration on the stability of the foam is explained by the Gibbs-Marangoni effect, and conditioning mechanisms for the thickened foam on sands are discussed from the evolution of foam bubbles.

A study on soil conditioning for cohesive soils with high content of clay minerals: Microscopic simulation and laboratory experiment

An earth pressure balance shield machine (EPB) is prone to blockage when tunneling in strata with high clay mineral content. Engineers often improve soil fluidity and reduce soil adhesion through soil conditioning to ensure the safe and efficient tunneling of shields. This paper selected montmorillonite, kaolinite, and illite as research objects and used molecular dynamics simulation and laboratory experiments. The interfacial mechanical behavior between anionic surfactant (sodium dodecyl benzene sulfonate, SDBS) solution and clay minerals was simulated at the microscopic scale and the improvement mechanism of SDBS solution on clay minerals was revealed. Then, the zeta potential of clay minerals and the stirring torque of the soil under different foam injection ratios (FIR) were tested. Accordingly, the conditioning effect of the SDBS solution at the macroscopic scale can be evaluated. The results show that the electrostatic adsorption and repulsion of ions occur on the surface of clay minerals, which makes it difficult for clay particles to aggregate into clusters. The SO3H- groups of the SDBS surfactant are the key factor to change the hydrophilicity of clay minerals. When different soil samples are improved to the same state, montmorillonite requires the largest FIR due to its microstructure.

Autonomous intelligent control of earth pressure balance shield machine based on deep reinforcement learning

In order to reduce the construction risk caused by human operation error and improve the geological adaptive ability of the shield machine, an autonomous intelligent control method is proposed for shield machine within the framework of interaction–judgment–decision based on Deep Deterministic Policy Gradient (DDPG) deep reinforcement learning in this study. Due to the strong nonlinear relationship between the shield machine's tunneling parameters, this research builds a deep reinforcement learning environment using mechanism model of sealed cabin pressure. DDPG agent model of the shield machine is established to replace the shield machine to interact and train with the geological environment. By minimizing the difference between the target pressure setting value and the sealed cabin pressure value, the dynamic balance between the sealed cabin pressure and the pressure on the excavation surface is realized, and the best strategy is obtained. Through real-time interaction with the geological environment, the method in this paper can dynamically adjust the tunneling parameters, accurately control the sealed cabin pressure, and has a strong geological adaptive ability. By realizing the intelligent decision-making of the tunneling parameters, it greatly improves the independent decision-making ability of the shield machine system, reduces the inaccuracy of human operation, and provides an effective guarantee for the efficient and safe operation of the shield machine. This study applies deep reinforcement learning technology to the control field of earth pressure balance shield machine, promotes AI technology, and provides a new idea for the development of AI construction technology in engineering field.

Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

During the tunneling process of shield machine, the accurate determination of tunneling parameters is the guarantee of safe operation of shield machine. Through the real-time construction data analysis and mining of shield machine, the tunneling parameters at the next moment are obtained in real-time. Based on this, this paper proposes an intelligent real-time prediction method for multi-region thrust of EPB shield machine based on Sparrow Search algorithm-Long and Short-term Memory (SSA-LSTM). By correlation analysis of the construction big data, the data features with a great correlation of shield machine thrust are obtained. And it is used as the input of LSTM prediction model to explore the nonlinear relationship between inputs and output. SSA is used to optimize LSTM prediction model to establish a more accurate nonlinear relationship, and then the multi-region thrust of shield machine at the next moment is accurately obtained. The simulation results show that SSA-LSTM model can accurately predict the thrust of each region of shield machine at the next moment, and the prediction performance is better than other models. The method provides a reference basis for shield machine to implement accurate thrust regulation and provides a guarantee for effective control of Earth pressure balance (EPB) in sealed cabin to ensure construction safety, which has engineering application value.

Upper bound analysis of longitudinally inclined EPB shield tunnel face stability in dense sand strata

Earth pressure balance (EPB) shield machine frequently drives along a route with a longitudinal inclination angle (δ) during tunnel construction. The tunnel face stability is a crucial issue for controlling the safety of tunneling. In this paper, the finite difference method (FDM) is used to investigate the face stability of longitudinally inclined shield tunnel in dense sand strata. Based on the numerical results of soil failure pattern in front of tunnel face, a novel two-dimensional (2D) failure mechanism is conceived following the upper bound theorem. It is assumed that the failure zone consists of an upper loosen zone, an arc-shaped shear band, and a triangular rigid body, which can calculate the active limit face support pressure pu. A scale factor for pu is introduced to quantify the three-dimensional (3D) behavior from the 2D calculation, with respect to different analysis approaches, e.g., numerical simulation, limit analysis, and limit equilibrium method. By incorporating the measure of scale factor, the calculation accuracy of the proposed 2D failure mechanism can be improved to provide reasonable prediction for longitudinally inclined tunnel.

A coupled deep learning approach for shield moving performance prediction of underground tunnel construction

Recently, deep learning methods have attracted great attentions for geotechnical engineering, especially for the safety construction of underground tunnels. In this regard, the control and regulation of operational parameters are vital for safety and intelligent tunnel construction. In this study, hybrid data-driven models on the basic of optimization approaches [particle swarm optimization (PSO), multilayer hierarchical heterogeneous PSO] and deep learning methods (long short-term memory and gated recurrent unit neural network) were developed. The optimization approaches were utilized to obtain the optimal parameters of deep learning methods. Apart from that, geological data sets were extended to match the scale of operational data sets using Kriging method, which considered the effects of local geology comprehensively. The developed hybrid models (DHMs) were utilized to estimate the speed of earth pressure balance (EPB) shield machine for a tunnel project in China. Results indicate that errors in estimated results by DHMs are acceptable in geotechnical engineering. Besides, the DHMs exist potentials to cope with time-series data sets from underground tunnel project. After that, Pearson correlation coefficient was used to evaluate the relative importance of influential factors on target variable. Results show that penetration has significant correlation relationship with excavation speed. The geological and operational parameters are equally important for the prediction performance of DHMs. The developed DHMs can provide a reference for regulating shield moving performance and attaining safety construction of underground tunnels.

Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms

Real-time and precise prediction of geological conditions is extremely beneficial to the safe and efficient construction of slurry pressure balanced shield machines (SPBMs), especially for super-large diameter tunnels. Machine learning algorithms provide an effective way to predict geological conditions and have been successful in hard-rock tunnel boring machine (hard-rock TBM) tunnels or earth pressure balance shield machine (EPBM) tunnels. However, the effectiveness of machine learning in predicting geological conditions for super-large diameter SPBM tunnels is still unknown. This study systematically demonstrated the application of machine learning algorithms to the prediction of geological conditions in super-large diameter SPBM tunnels based on field data from the Heyan Road crossing river tunnel. The relationship between SPBM parameters and geological conditions was first evaluated, and subsequently, an adaptive model was proposed to determine the number of input variables. The performance of three algorithms, random forest (RF), AdaBoost, and support vector machine (SVM), in rock mass classification, and seven algorithms, multi-objective random forest (MORF), single-target-RF, regressor chain-RF, single-target-AdaBoost, regressor chain-AdaBoost, single-target-SVM, and regressor chain-SVM, in formation percentage prediction, were compared. The results showed that machine learning algorithms can effectively predict the geological conditions in super-large diameter SPBM tunnels. Specifically, RF had the best performance in rock classification compared with AdaBoost and SVM. MORF outperforms the other six algorithms in terms of formation percentage prediction. Additionally, sensitivity analysis indicates that the newly proposed parameters, including slurry system parameters and cutter type, have a clear positive effect on the performance of geological condition prediction.

Research on Strata Deformation Induced by EPB Tunneling in Round Gravel Stratum and Its Control Technology

Recently, many studies have been conducted on the stratum deformation induced by earth pressure balance (EPB) shield tunneling in soft soil and sand. Movement laws vary largely among different strata. However, at present, relevant research mainly focuses on soft soil and sand, whereas little attention has been paid to the movement law of round gravel stratum with higher instability. In this study, a field monitoring test was carried out on the EPB shield machine when it passes through the round gravel stratum. Based on the analysis of monitoring results under different chamber earth pressures, thrust force, the torque of the cutter, grouting pressure, and grouting volume, the relationships between shield tunneling parameters and their influence on the disturbance of the surrounding soil mass were investigated. It was found that the surface deformation shape of the monitoring section of the south and north lines conforms to the Gaussian curve. The vertical deformation of the stratum at the tunnel axis is the largest. The maximum value is observed when the cutter head reaches the monitoring section. The horizontal deformation reaches a maximum value at the stage of the shield tail pass section. The strata deformation is not only related to the strata properties but also has a strong positive correlation with the shield tunneling parameters. The chamber earth pressure is the main factor affecting the stratum deformation before the arrival of the cutter head, and the grouting volume is the main factor affecting the strata deformation during the stage of the shield tail pass section.

DEM-continuum mechanics coupling simulation of cutting reinforced concrete pile by shield machine

Based on the discrete element method (DEM) - continuum mechanics coupling method, a numerical simulation analysis is carried out to model cutting some existing bridge piles that have invaded Beijing Metro Line 12. Firstly, the macroscopic parameters of the reinforced concrete pile and the soil stratum where the shield tunneling machine traversed are calibrated using uniaxial compression and Brazilian test through a series of numerical tests. In this paper, the procedure of building up a DEM-continuum mechanics coupling model is presented, then the model is applied to simulate the process of cutting the pile foundation using the earth pressure balanced (EPB) shield machine. The influences of different advancing parameters on the displacement of the pile bottom, the thrust force, and the torque of the EPB shield machine are investigated. The simulation-calculated results, such as thrust force and torque of the shield machine, are in good agreement with the theoretical method in previous studies and the corresponding monitored data in the field. Finally, a method based on the thrust of a single cutter is proposed to improve the theoretical model so that it can consider the effects of cutting bridge piles.

Soil disturbance induced by EPB shield tunnelling in multilayered ground with soft sand lying on hard rock: A model test and DEM study

A large scale model test and numerical simulation using three dimensional discrete element method (3D DEM) were carried out in parallel to study the soil disturbance induced by earth pressure balanced (EPB) shield tunnelling in multilayered ground with soft sand lying on hard rock. The model test used a miniature EPB shield machine which could fully reproduce the real tunnel construction of excavation and support. The DEM model simulating the model test was developed to capture the microscopic mechanisms of ground deformation. The surface and subsurface settlement were closely recorded in model test and compared with those of homogeneous ground to clarify the influence of underlying rock on the movement of overlying sand. The volume ratio of rock to sand in excavated muck was closely recorded in DEM simulation to quantitatively analyse the over excavation of soft sand. Results show that both the surface and subsurface settlement trough in multilayered ground are narrower than those in homogeneous ground, while the subsurface settlement in multilayered ground is larger than that in homogeneous ground. The width parameter of settlement trough decreases nonlinearly along the gravitational direction. The over excavation of overlying soft sand is influenced by the ratio of hard rock to tunnel cross section and the rotating speed of shield cutterhead.

Prediction of shield machine posture using the GRU algorithm with adaptive boosting: A case study of Chengdu Subway project

Shield machine deviation from the design tunnel axis (DTA) causes dislocation and damage of the segments and may lead to poor tunnel quality, which is a primary concern in tunnel construction. Therefore, it is necessary to predict the shield machine posture dynamically and assist the operator in adjusting the tunneling parameters in advance. Based on the tunneling data of five earth pressure balance (EPB) shield machines, a novel method for predicting shield machine posture, mainly composed of adaptive boosting (AdaBoost) and gated recurrent unit (GRU) algorithms, is proposed in this paper. In parallel, a data preprocessing algorithm is developed for the original tunneling parameters, including three phases: data extraction, data compilation, and data normalization. The hyperparameters of the model were determined using the grid search and cross-validation technology. The actual deviation case test shows that once the model predicts that the shield machine posture will deviate significantly from DTA, it can issue a warning in advance and assist the machine operators in optimizing tunneling parameters for a better trajectory. Then, the model prediction results were compared with the benchmark algorithms. The results reveal that the GRU algorithm is conductive to capture the trend of the time sequence, and the AdaBoost algorithm is beneficial for improving the fitting ability of the regression model. Finally, we found some association rules of tunneling parameters that affect the posture of shield machine.

Quantitative Evaluation of the Adaptability of the Shield Machine Based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP)

Tunnel engineering based on the shield method is an extremely complex project. During the construction process, many uncertain factors are often faced, which makes it difficult to effectively and quantitatively evaluate the effectiveness of the shield machine. To solve this problem, this paper proposes an adaptability evaluation method for shield machines based on the fuzzy analytic hierarchy process (FAHP) and analytic hierarchy process (AHP). First, the selection and equipment matching of Earth pressure balance shield machines in the shield interval between Tashuiqiao Station and Erxianqiao Station of Chengdu Metro Line 17 are introduced. Then, using APH for screening, the final evaluation indices of the adaptability of the shield machines in this project are determined. Next, referring to the existing research results and fuzzy mathematics method, the membership function of the evaluation indices and adaptability evaluation model of the shield machine are constructed. Finally, the rationality and accuracy of the fuzzy comprehensive evaluation model of shield adaptability are verified through onsite shield construction, and the evaluation results are consistent with the actual situation.

Multi-Step Combined Control Technology for Karst and Fissure Water Inrush Disaster During Shield Tunneling in Spring Areas

Shield tunneling in highly fractured karst water-rich conditions easily results in water inrush disaster or even causes the roof of the karst caves to collapse. Severe water inrush disasters have occurred during the EPB (earth pressure balance) shield machine of the Jinan Metro Line R1 advanced through a karst and fissure groundwater-rich limestone ground in the spring area. To cope with the extreme water inrush risk, a multi-step combined control technology was put forward. First, a detailed geological exploration was carried out by ahead geophysical prospecting using high-density resistivity method, geological radar, etc., and geological borehole drilling was conducted from the ground surface before excavation. As a result, the distribution orientation, size, fissure development degree, and water inflow channel within the surrounding rock of the karst caves were detected. Second, multi-step grouting was performed to reinforce the surrounding rock, including pre-grouting treatment and filling rock blocks to the big karst caves from the ground surface, multiple grouting with a small amount of inert slurry each time inside the tunnel, and secondary circumferential hoop grouting at the shield tail. Third, the tunneling process was optimized, including optimizing the tunneling parameters, making full use of the air-pressurized tunneling technology of the EPB to press bentonite into the fractures around the excavation cabin to seal the fissure water, and using the drainage system of EPB and muck improvement technology to reduce the water inrush disaster. Meanwhile, shield protection slurry technology is applied to cutter inspection and replacement in the pressurized chamber under dynamic water flow environment of the spring terrain. The practice shows that the water inrush on the tunnel face is obviously alleviated after the shield machine advanced into the grouting area. According to statistics, the water inflow on the tunnel face decreases from about 4 m3/h before treatment to less than 0.3 m3/h after the abovementioned control, and the water seepage between the segmental linings reduces to almost zero. The average advance rate increased from 3 m/day without stopping or even zero when the shield machine needs to shut down 2–3 days for drainage to about 6 m/day. In addition, the treatments prevented the shield machine from jamming and the head descending disaster. This study provided a reliable control method for shield tunneling through the karst and fissure water-rich area and played an essential role in protecting the spring water.

Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

The shield machine attitude (SMA) is the most important parameter in the process of tunnel construction. To prevent the shield machine from deviating from the design axis (DTA) of the tunnel, it is of great significance to accurately predict the dynamic characteristics of SMA. We establish eight SMA prediction models based on the data of five earth pressure balance (EPB) shield machines. The algorithms adopted in the models are four machine learning (ML) algorithms (KNN, SVR, RF, AdaBoost) and four deep learning (DL) algorithms (BPNN, CNN, LSTM, GRU). This paper obtains the hyperparameters of the models by utilizing grid search and K-fold cross-validation techniques and uses EVS and RMSE to verify and evaluate the prediction performances of the models. The prediction results reveal that the two best algorithms are the LSTM and GRU with EVS > 0.98 and RMSE < 1.5. Then, integrating ML algorithms and DL algorithms, we design a warning predictor for SMA. Through the historical 5-cycle data, the predictor can give a warning in advance if the SMA deviates significantly from DTA. This study indicates that AI technologies have considerable promise in the field of SMA dynamic prediction.