Empirical Formula Method Research Articles

Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

Chloride corrosion is a key factor affecting the life of marine concrete, and surface chloride concentration is the main parameter for analyzing its durability. In this paper, we first introduce six erosion mechanism models for surface chloride ion concentration, reveal the convection effect in the diffusion behavior of chloride ions, and then introduce the corrosion mechanisms that occur in different marine exposure environments. On this basis, the analysis is carried out using empirical formulations and machine learning methods, which provides a clearer understanding of the research characteristics and differences between empirical formulas and emerging machine learning techniques. This paper summarizes the time-varying model and multifactor coupling model on the basis of empirical analysis. It is found that the exponential function and the reciprocal function are more consistent with the distribution law of chloride ion concentration, the multifactor model containing the time-varying law is the most effective, and the Chen model is the most reliable. Machine learning, as an emerging method, has been widely used in concrete durability research. It can make up for the shortcomings of the empirical formula method and solve the multifactor coupling problem of surface chloride ion concentration with strong prediction ability. In addition, the difficulty of data acquisition is also a major problem that restricts the development of machine learning and incorporating concrete maintenance conditions into machine learning is a future development direction. Through this study, researchers can systematically understand the characteristics and differences of different research methods and their respective models and choose appropriate techniques to explore the durability of concrete structures. Moreover, intelligent computing will certainly occupy an increasingly important position in marine concrete research.

Physical model test study on strength characteristics of gravel pile composite foundation

In gravel pile composite foundation stability design, the shear strength of composite foundation is critically important. However, due to the size limitation of test equipment, it is difficult to obtain its shear strength in field. This study designs a laboratory shear model test to obtain the shear strength of gravel pile composite foundation, where a total of four group tests were carried out, including two nature soils and two gravel pile area replacement ratios. The shear strength parameters obtained by the shear model tests are compared with the results of empirical formula methods such as the Chinese Standard Method and Priebe Method to verify the accuracy of these two methods. It is demonstrated that for the Chinese Standard Method, the empirical formula's results are smaller than the shear model test results when the pile-soil stress ratio b is 1. When the pile-soil stress ratio b is 1.5, the results are close to each other. For the Priebe Method, the empirical formula results are all larger than the shear model test results, and it is especially unsuitable for the mud soil.

Investigation of the Tunnel Water Inflow Prediction Method Based on the MODFLOW-DRAIN Module

The precise prediction of water inflow in tunnels is a key aspect of civil engineering. It is directly related to the progress of construction, the stability of caverns and the safety of construction personnel. Currently widely used calculation methods include the empirical formula method, the analytical method and the numerical simulation method. In situations where the geological conditions are complex and the boundary conditions are irregular, numerical simulation methods have clear advantages. However, there are still discussions about the rationality, accuracy and applicability of the definition of tunnel boundary conditions in numerical simulation methods. Based on the Groundwater Model System (GMS10.7) software, we investigate the feasibility of using the DRAIN module to describe tunnel boundaries and calculate water inflow. By comparing it with traditional empirical formulas, the feasibility of the method is verified, and the error is about 8%. In addition, this method is applied to predict the water inflow in the construction of an underground pumping station under complex geological conditions and the prediction is compared with the measured water inflow, and the error is about 20%. This article explains the physical meaning and the value method of the relevant parameters of the DRAIN module, providing basic support for the prediction of water inflow in tunnels.

Water-richness evaluation method and application of clastic rock aquifer in mining seam roof

Clastic rock aquifer of the coal seam roof often constitutes the direct water-filling aquifer of the coal seam and its water-richness is closely related to the risk of roof water inrush. Therefore, the evaluation of the water-richness of clastic rock aquifer is the basic work of coal seam roof water disaster prevention. This article took the 4th coal seam in Huafeng mine field as an example. It combined the empirical formula method and generalized regression neural network (GRNN) to calculate the development height of water-conducting fracture zone, determined the vertical spatial range of water-richness evaluation. Depth of the sandstone floor, brittle rock ratio, lithological structure index, fault strength index, and fault intersections and endpoints density were selected as the main controlling factors. A combination weighting method based on the analytic hierarchy process (AHP), rough set theory (RS), and minimum deviation method (MD) was proposed to determine the weight of the main controlling factors. Introduced the theory of unascertained measures and confidence recognition criteria to construct an evaluation model for the water-richness of clastic rock aquifers, the study area was divided into three zones: relatively weak water-richness zones, medium water-richness zones, and relatively strong water-richness zones. By comparing with the water inrush points and the water inflow of workfaces, the evaluation model's water yield zoning was consistent with the actual situation, and the prediction effect was good. This provided a new idea for the evaluation of the water-richness of the clastic rock aquifer on the roof of the mining coal seam.

A review of existing methods for predicting leachate production from municipal solid waste landfills.

Landfilling is one of the predominant methods of municipal solid waste (MSW) disposal worldwide, while the generation of leachate, a kind of toxic wastewater, is among the primary factors behind landfill instability and environmental contamination problems. Precise prediction of leachate production is crucial to landfill safety evaluation and design. This paper presents a comprehensive review of methods for predicting leachate production from MSW landfills. Firstly, compositional characteristics of MSW and leachate generation mechanism are analysed. Factors influencing leachate production are summarised based on the generation mechanism, including the components of MSW, climatic conditions, landfill structure, and environmental factors. Then, we classified the existing methods for predicting leachate production into four categories: water balance formula, water balance model, empirical formula, and artificial intelligence model methods. Advantages, disadvantages, and applicability of different leachate production prediction methods are compared and analysed. Furthermore, limitations in the existing leachate production prediction methods for MSW landfills and scope for future research are discussed.

Research on fatigue curve fitting methods based on the notch stress approach

PurposeThis study investigates the fitting techniques for notch fatigue curves, seeking a more reliable method to predict the lifespan of welded structures.Design/methodology/approachBuilding on the fatigue test results of butt and cruciform joints, this research delves into the selection of fitting methods for the notch fatigue curve of welded joints. Both empirical formula and finite element methods (FEMs) were employed to assess the notch stress concentration factor at the toe and root of the two types of welded joints. Considering the mean stress correction and weld misalignment coefficients, the notch fatigue life curves were established using both direct and indirect methods.FindingsAn engineering example was employed to discern the differences between the direct and indirect approaches. The findings highlight the enhanced reliability of the indirect method for fitting the fatigue life curve.Originality/valueWhile the notch stress approach is extensively adopted due to its accurate prediction of component fatigue life, most scholars have overlooked the importance of its curve fitting methods. Existing literature scantily addresses the establishment of these curves. This paper offers a focused examination of fatigue curve fitting techniques, delivering valuable perspectives on method selection.

Identification of reservoir types in deep carbonates based on mixed-kernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces. Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency, which cannot accurately reflect the nonlinear relationship between reservoir types and logging data. Recently, the kernel Fisher discriminant analysis (KFD), a kernel-based machine learning technique, attracts attention in many fields because of its strong nonlinear processing ability. However, the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well, especially for highly complex data cases. To address this issue, in this study, a mixed kernel Fisher discriminant analysis (MKFD) model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin, China. The MKFD model was trained and tested with 453 datasets from 7 coring wells, utilizing GR, CAL, DEN, AC, CNL and RT logs as input variables. The particle swarm optimization (PSO) was adopted for hyper-parameter optimization of MKFD model. To evaluate the model performance, prediction results of MKFD were compared with those of basic-kernel based KFD, RF and SVM models. Subsequently, the built MKFD model was applied in a blind well test, and a variable importance analysis was conducted. The comparison and blind test results demonstrated that MKFD outperformed traditional KFD, RF and SVM in the identification of reservoir types, which provided higher accuracy and stronger generalization. The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Examination of Determinants and Predictive Modeling of Artificially Frozen Soil Strength Utilizing the XGBoost Algorithm

A freezing method is usually employed in the construction of metro links. Unconfined compressive strength (UCS) is a pivotal mechanical parameter in freezing design. Due to the limitations of indoor experiments and the complexity of influencing factors, the applicability of empirical strength formulas is poor. This study predicts the strength of frozen soil with different particle size distributions based on the highly integrated XGBoost algorithm. Compared with other empirical formula methods, the accuracy is high. Through the analysis of Pearson’s correlation coefficient results, further analysis is needed on the nonlinear correlation between the temperature, the strain rate, and the unconfined compressive strength of frozen soil. The results indicated a strong negative correlation between temperature and unconfined compressive strength; the strength initially increased at a faster rate, slowed down during the intermediate phase, and again increased at a faster rate toward the end. There was a positive correlation between the strain rate and the unconfined compressive strength, with the strength exhibiting varying sensitivities to different sizes of strain rates. When the strain rate was relatively small, the strength increased slightly; as the strain rate increased, the strength increased more significantly. Different soils showed similar trends, but differences in the particle size distribution resulted in variations in the final strength. This study can provide a scientific basis for predicting the strength of soil bodies in the freeze–thaw construction of subway connection tunnels.

Resource differences in the populations of Protosalanx chinensis between Dalong Lake and Amuta Lake in Daqing, Northeast China

The population structure of Protosalanx chinensis is affected by a variety of factors, including water environment, intraspecific differentiation, interspecific competition and commercial fishing. To investigate the growth performance and the current status of resource utilization of P. chinensis in two important fish farming grounds (Dalong Lake and Amuta Lake) in Daqing, Northeast China, we collected 1513 P. chinensis samples from April to December 2018 for measurement and recording the basic biological characteristics. We estimated the parameters including population growth, mortality and exploitation using Von Bertalanffy growth formula, Beverton-Holt model, Pauly's empirical formula and length-based Bayesian biomass estimation method. The results showed that the asymptotic body lengths of Dalong Lake (n=660) and Amuta Lake (n=853) populations were 183.75 and 169.10 mm, respectively. The growth performance indices were both 4.85, and the body mass growth equations were Wt=29.29×(1-e-2.1(t+0.07))3.2977 and Wt=28.72×(1-e-2.5(t+0.07))3.4168, respectively. The ages at inflection were 0.49 a and 0.43 a, and the condition factors were 5.30×10-4±1.38×10-4 and 4.00×10-4±1.61×10-4. The exploitation rates were 0.60 and 0.61, and the relative catch per unit of supplementation were 0.069 and 0.075, respectively. The current differences in population resources between Dalong Lake and Amuta Lake in Daqing were mainly manifested in the parameters related to growth characteristics. The main reason for such differences was changes in the survival conditions of P. chinensis populations after the period of dietary transition induced by the density differences.

Research on prediction and analysis of supercritical water heat transfer coefficient based on support vector machine

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

Determining the present-day in-situ stresses of tight-oil sandstones by conventional logs: An approach in the Triassic Yanchang Formation, southern Ordos Basin

The present-day in-situ stresses affect the drilling design, well pattern deployment, well completion modification, hydraulic fracturing and water injection of tight-oil sandstones. The measurement data of these stresses are commonly unavailable because of their high costs and limited core samples, therefore employing conventional logs for these stress determination is imperative for tight-oil sandstones. Firstly, the suitable calculation models for the present-day in-situ stress calculation by conventional logs were selected according to the geological characteristics of the sixth member of the Yanchang Formation (Chang 6) in Heshui area of the southern Ordos Basin, China. Then, the dynamic rock mechanical parameters were determined by conventional logs, and corrected by the static rock mechanical parameters obtained from the triaxial rock mechanical tests. Moreover, the pore fluid pressure was determined by the empirical formula method. Finally, the maximum and minimum horizontal compressive stresses (σH and σh), and the vertical stress (σv) of six wells were calculated according to the selected models of these stresses, respectively. The present-day in-situ stresses, determined by the proposed method in the paper, were verified by those obtained from acoustic emission tests and finite-element numerical simulations with the relative errors of less than 10%. The results show that the magnitudes of σH, σh and the horizontal differential stress (σH−h) in the study area mainly range from 32 to 43 MPa, 23 to 37 MPa and 5 to 8 MPa, respectively. The magnitude of the three-dimensional present-day in-situ stress increases with the increase of depth. The average gradients of σH, σv and σh are 0.018, 0.014 and 0.015 MPa/m, respectively, that is σH>σv>σh. In this stress state, the hydraulic fractures, with a trend of little expansion towards multiple directions, are commonly developed at a small angle intersecting with the direction of σH in the study area.

Modeling and characteristic analysis of viscous resistance in annulus gap of coalbed gas well drainage pump

The viscous resistance caused by the relative motion of plunger and pump barrel is an important factor in the dynamic analysis of the drainage and production mechanism during coalbed methane exploitation. Existing studies mostly focus on deep well (1000–3000 m) large-displacement pump (usually 80–300 m3/d), and the interstitial medium is mainly crude oil. Regression fitting is mainly carried out based on empirical formula or experimental simulation method, without considering the characteristics of shallower coalbed gas well (400–800 m) small-displacement pump (<10 m3/d). The variation of the clearance between plunger and barrel and the variation of viscous resistance between plunger and barrel are not revealed. Based on the Navier-Stokes equation and Newton's internal friction law, a mathematical model of the annular viscous resistance between the plunger and the pump barrel is established, and an analytical method is used to describe the influence of stroke times, annular clearance, and differential pressure between the upper and lower plungers on the viscous resistance. The results show that the plunger stroke is directly proportional to the variation range of viscous resistance, and low stroke is beneficial to reduce the viscous resistance. The effect of differential pressure between upper and lower plunger of rod pump on viscous resistance of small displacement coalbed gas well is significantly greater than that of plunger running speed. The annular clearance is directly proportional to the differential pressure between the upper and lower parts of the plunger, the inertia force of the plunger, and the shear force of the moving fluid. The annular viscous resistance model between plunger and pump barrel is helpful to improve the calculation accuracy of suspending point load of coalbed gas well pumping unit, analyze the working condition of coalbed gas drainage gas production process, and provide basis for the optimization design of coalbed gas well rod pump clearance.

Prediction method of blast load on underground structure surface based on neural network

The dynamic load in the soil directly leads to the damage of underground structures upon explosions. In this study, a method to predict blast load on underground structure surface based on the neural network was developed to study the load distribution under close-in detonation. First, taking the underground utility tunnel as the experimental structure, 52 groups of field blast tests were conducted on the surface load mechanism, and the surface load data samples were obtained. Second, the key influencing parameters of the reflected blast load were obtained through the dimensional analysis method, and the backpropagation neural network model was constructed based on the test data using the Levenberg–Marquardt algorithm to train and optimize the neural network. Finally, the accuracy of load prediction results was compared and evaluated among the neural network, empirical formula, and nonlinear regression analysis (NRA) methods. It is found that the input parameters of combined variables can further improve the prediction accuracy of the neural network compared with the input parameters of single physical variables. Compared with the empirical formula method and the NRA method, the neural network model with input parameters of combined variables provided the most accurate prediction. The load distribution under typical conditions calculated by the neural network showed that the explosive setting parameters impact the uneven shape of blast load on the structure surface. The increase in explosive equivalent and depth reduces the nonuniformity of load distribution, while the decrease in explosion distance increases the nonuniformity of load distribution.

Stochastic kinematic analysis of rock slope failure angle based on multi algorithm optimization, a case study of the proposed bridge project

ABSTRACT Due to the randomness and complexity of the discontinuity distribution, it is always complicated to analyse the three-dimensional stability problem of discontinuity-controlled rock slope in the field of engineering geology. Specifically, when restricted by poor sampling conditions, the discontinuity data obtained are very limited. To solve this problem, we propose a stochastic kinematic method to estimate the slope failure angle based on limited data. Taking the high-steep rock slope on the right bank of a proposed bridge as an example, the stochastic kinematic method based on multiple algorithms (Monte Carlo simulation, Latin hypercube, and Bayesian estimation) is used to analyse the failure angles of potentially unstable blocks in two modes of plane and wedge failure. After statistical optimisation, the slope failure angles are determined to be 65.69° (plane failure) and 58.65° (wedge failure); this approach is more objective than the empirical formula method and can be used as the basis for judging the slope stability. The method proposed in this study fully considers the randomness of discontinuity distribution and combination, and can use limited sample data to realise failure angle analysis and stability judgement. The result is important as a reference of significance for engineering construction and disaster prevention.

Thermodynamic analysis of the difference in adsorption characteristics of CH4 and CO2 on continental shale

In order to explain the difference in adsorption characteristics of CH4 and CO2 on continental shale from the perspective of thermodynamics, the isothermal adsorption experiments of CH4 and CO2 adsorbed by shale in Yanchang Formation in Ordos Basin were carried out, and the excess adsorption capacity was corrected to absolute adsorption capacity. Then the Clausius-Clapeyron equation was used to analyze the isosteric heat of adsorption of CH4 and CO2 on shale. The results show that, for calculating the absolute adsorption capacity, Ozawa empirical formula or Van der Waals approximation method should be used to calculate the adsorption phase density. The absolute adsorption capacity should be selected as the basic data for calculating the isosteric heat of adsorption. The reason is that the excess isosteric heat of adsorption has a negative value in the low adsorption capacity stage, which is contradictory to the fact that the adsorption process is exothermic, and is significantly higher than the absolute isosteric heat of adsorption. There is a good linear positive correlation between the isosteric heat of adsorption and the adsorption amount of CH4 and CO2 adsorbed by continental shale, and the isosteric heat of adsorption of CH4 is greater than that of CO2. The absolute initial isosteric heat of adsorption of CH4 and CO2 adsorbed by shale is 52.04 kJ/mol and 27.71 kJ/mol, indicating that the adsorption force of CH4 on Yanchang Formation shale is stronger than that of CO2.

Development process and height of the mining-induced water fractured zone over the longwall goaf

Abstract Providing accurate prediction methods for mining-induced water fractured zones over the longwall goaf has become increasingly significant because of the grand economic and technological challenges of deeper extraction. Taking the No. 839 coalface in the Qingdong mining area as a case study, a comprehensive study of the water-flowing fractured zone (WFFZ) formation process and peak height were carried out by the radial basis function (RBF) neural network model, parallel electrical monitoring (PEM) technology, numerical simulation model and the empirical formula method. The results show that the destruction does not occur simply layer-by-layer from the bottom up. Rather, the fractured areas appear first in soft layers above the goaf. With the advance of footage, the destruction gradually extends from soft layers to hard layers, and the WFFZ is formed when the fractured areas fit closely together. Also, the change process of the WFFZ shape is closely related to rock mass heterogeneity. In addition, the results of the comparison suggest that the RBF neural network model had more accuracy in predicting the height of the WFFZ than the numerical simulation model and empirical formula method.

Investigation of the Water Damage Resistance and Storability of a SEBS-Modified Cold-Patching Asphalt Mixture.

At present, achieving good storability and water damage resistance remains challenging for cold-patching asphalt mixtures (CAMs). To address this issue, this study selects styrene-ethylene-butadiene-styrene copolymer (SEBS) and diesel as a modifier and diluent, respectively, to improve the water stability and storability of CAMs. The diesel oil content is determined through the Brookfield rotational viscosity test, and the modifier content is obtained through the Marshall stability test. With the empirical formula method, paper trail test, and modified Marshall test, mixed designs of CAMs modified with and without SEBS are established to determine the best cold-patching asphalt content. On this basis, the modification effect of SEBS is verified by comparing the test results of the modified and unmodified CAMs, and the water stability and Marshall stability tests are conducted before and after CAM storage, respectively. Results show that the optimum contents of SEBS and diesel oil are 7.5% and 40% of the base asphalt weight, respectively, and the best modified asphalt content is 4.6% of the mineral material weight in CAM. The Marshall residual stability and freeze-thaw splitting strength ratio of the 7.5% SEBS-modified CAM are increased by 20.1% and 15.7%, respectively, relative to the unmodified CAM, and the storage performance requirement of at least two months can be guaranteed.

Evaluation on Development Height of Water-Conduted Fractures on Overburden Roof Based on Nonlinear Algorithm

Based on the support vector machine theory, the particle swarm algorithm is used to optimize parameters, combined with an analytic network process in order to feature dimensionality reduction of the original data, and a nonlinear algorithm model combining statistical analysis and machine learning analysis is established. Taking the relevant data of overlying rock from two zones in Yingpanhao Coal Mine as an example, the main factors such as rock tectonic development, coal seam dip, mining height, mining operation method and stope width are ranked according to the weights of their contribution to the development height of the two zones in order to determine the main control factors, such as stope width and mining height. Using MATLAB as the experimental platform, 16 sets of two-dimensional mine data similar to the geology of the study area were divided into training and test sets for prediction and simulation, comparing the optimal solutions of various optimization-seeking algorithms to obtain quantitative prediction results based on the nonlinear algorithm model. The kriging interpolation process was carried out by ArcGIS to realize qualitative visualization, and effective classification is carried out according to the natural breakpoint method to obtain six development height divisions of the water-conducting fracture zone in the study area. The results show that the prediction model of the development height of two zones in the coal roof based on the non-linear algorithm has better accuracy and generalization ability. Predicted by the test set, the model’s prediction result MAE is less than 10%, and the accuracy is better than the traditional empirical formula method, which makes up for its lack of rough calculation accuracy and solves the problem of not being able to locate the height of roof crack development at specific borehole locations, which has significance in guiding the prediction and prevention of roof water damage and subsidence disasters.

Intelligent Simulation of Water Temperature Stratification in the Reservoir.

In order to fully make use of limited water resources, humans have built many water conservancy projects. The projects produce many economic benefits, but they also change the natural environment. For example, the phenomenon of water temperature stratification often occurs in deep reservoirs. Thus, effective ways are needed to predict the water temperature stratification in a reservoir to control its discharge water temperature. Empirical formula methods have low computational accuracy if few factors are considered. Mathematical model methods rely on large amounts of accurate hydrological data and cost long calculation times. The purpose of the research was to simulate water temperature stratification in a reservoir by constructing an intelligent simulation model (ISM-RWTS) with five inputs and one output, determined on the basis of artificial neural networks (ANN). A 3D numerical model (3DNM) was also constructed to provide training samples for the ISM-RWTS and be used to test its simulation effect. The ISM-RWTS was applied to the Tankeng Reservoir, located in the Zhejiang province of China, and performed well, with an average error of 0.72 °C. Additionally, the Intelligent Computation Model of Reservoir Water Temperature Stratification (ICM-RWTS) was also discussed in this paper. The results indicated that the intelligent method was a powerful tool to estimate the water temperature stratification in a deep reservoir. Finally, it was concluded that the advantages of the intelligent method lay in its simplicity of use, its lower demand for hydrological data, its well generalized performance, and its flexibility for considering different input and output parameters.

Deep HystereticNet to predict hysteretic performance of RC columns against cyclic loading

This article attempts to reproduce hysteretic performance of HRB600 bar reinforced concrete columns under cyclic loading by adopting multivariate deep learning methods. Bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent units network (BiGRU) have been modified for temporal sequence related strength prediction with the consideration of multivariate parameters. To this end, a database with a 79,244 (row) × 12 (column) matrix is prepared for training, validation, and testing purposes. The inputs to the model include time intervals, loading displacement, concrete strength, axial compression ratio, longitudinal reinforcement tensile grade, longitudinal reinforcement diameter, longitudinal reinforcement ratio, stirrup tensile strength, stirrup diameter, spacing, and stirrup reinforcement ratio while the target is the cyclic load. By comparing the energy dissipation, accumulated energy dissipation, stiffness degradation and backbone curve of the hysteretic performance, the capacity of the adopted methods is compared with that of the finite element analysis. The comparison shows that the adopted method can recovery the hysteretic curves very well and the GRU method exhibits the most accurate prediction performance because of the highest R2 and the lowest MAE, MSE, and RMSE. The originality and innovation are mainly reflected in the use of time series data-driven deep learning model to replace the existing empirical formula and finite element method to reconstruct the nonlinear hysteretic behavior, so as to predict the seismic performance of the structure.