Modification of the linear viscoelastic deformation prediction model of asphalt mixture

A deformation prediction model for the dynamic creep test is deduced based on the linear viscoelastic（LVE）theory.Then,the defect of the LVE deformation prediction model is analyzed by comparing the prediction of the LVE deformation model with the experimental data.To improve accuracy,a modification of the LVE deformation prediction model is made to simulate the nonlinear property of the deformation of asphalt mixtures,and it is verified by comparing its simulation results with the experimental data.The comparison results show that the LVE deformation prediction model cannot simulate the nonlinear property of the permanent deformation of asphalt mixtures,while the modified deformation prediction model can provide more precise simulations of the whole process of the deformation and the permanent deformation in the dynamic creep test.Thus,the proposed modification greatly improves the accuracy of the LVE deformation prediction model.The modified model can provide a better understanding of the rutting behavior of asphalt pavement.

Deformation prediction and analysis of underground mining during stacking of dry gangue in open-pit based on response surface methodology

Deformation prediction and the analysis of underground goaf are important to the safe and efficient recovery of residual ore when shifting from open-pit mining to underground mining.To address the comprehensive problem of stability in the double mined-out area of the Tong-Lv-Shan(TLS)mine,which employed the dry stacked gangue technology,this paper applies the function fitting theory and a regression analysis method to screen the sensitive interval of four influencing factors based on single-factor experiments and the numerical simulation software FLAC3D.The influencing factors of the TLS mine consist of the column thickness(d),gob area span(D),boundary pillar thickness(h)and height of tailing gangue(H).The fitting degree between the four factors and the displacement of the gob roof(W)is reasonable because the correlation coefficient(R2)is greater than0.9701.After establishing29groups that satisfy the principles of Box-Behnken design(BBD),the dry gangue tailings process was re-simulated for the selected sensitive interval.Using a combination of an analysis of variance(ANOVA),regression equations and a significance analysis,the prediction results of the response surface methodology(RSM)show that the significant degree for the stability of the mined-out area for the factors satisfies the relationship of h>D>d>H.The importance of the four factors cannot be disregarded in a comparison of the prediction results of the engineering test stope in the TLS mine.By comparing the data of monitoring points and function prediction,the proposed method has shown promising results,and the prediction accuracy of RSM model is acceptable.The relative errors of the two test stopes are1.67%and3.85%,respectively,which yield satisfactory reliability and reference values for the mines.

Deformation prediction of tunnel surrounding rock mass using CPSO-SVM model

A new method integrating support vector machine (SVM),particle swarm optimization (PSO) and chaotic mapping (CPSO-SVM) was proposed to predict the deformation of tunnel surrounding rock mass.Since chaotic mapping was featured by certainty,ergodicity and stochastic property,it was employed to improve the convergence rate and resulting precision of PSO.The chaotic PSO was adopted in the optimization of the appropriate SVM parameters,such as kernel function and training parameters,improving substantially the generalization ability of SVM.And finally,the integrating method was applied to predict the convergence deformation of the Xiakeng tunnel in China.The results indicate that the proposed method can describe the relationship of deformation time series well and is proved to be more efficient.

Research of deformation prediction method of soft soil deep foundation pit

In view of the characteristics of soft soil deep foundation pit for the construction and geotechnical characteristics of the special medium,it is difficult to calculate theoreti- cally accurately structural deformation of the foundation pit,so in the course of excavation on the construction of the information is particularly important.The analysis and compari- son of several popular non-linear forecasting methods,combined with the actual projects, set up a grey theoretical prediction model,time series forecasting model,improved neural network model to predict deformation of the foundation pit.The results show that the use of neural network to predict with high accuracy solution,it is the foundation deformation prediction effective way in underground works with good prospects.

Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data

The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.

Application of probability integral method in ground deformation prediction

In order to study the law of mining subsidence and ground movement, to provide the basis of coal mining under building, railway and water, we used the probability integration method to make comprehensive evaluation of ground stability. Take Yingcheng Coal Mine of Jiutai as an example. Mining-induced movement and horizontal movement are analyzed on the basis of the measurement data. The resuhs of prediction can pro- vide reference and basis for prevention of coal mining subsidence and future restoration and treatment.

Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions

Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation pre- diction models seldom consider uncertainties. In this study, a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model. The expectation, one of the cloud characteristic parameters, was obtained using the Verhulst model, and the other two cloud characteristic parameters, entropy and hyper-entropy, were calculated by introducing inertia weight. The hybrid prediction model was used to predict the dam deformation in a hydroelectric project. Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional sta- tistical model. It provides a new approach to predicting dam deformation under uncertain conditions.

A Novel Hybrid Intelligent Prediction Model for Valley Deformation: A Case Study in Xiluodu Reservoir Region, China

The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam,and an accurate prediction of valley deformation(VD)remains a challenging part of risk mitigation.In order to enhance the accuracy of VD prediction,a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising(EEMD-ITD),Differential evolutions—Shuffled frog leaping algorithm(DE-SFLA)and Least squares support vector machine(LSSVM)is proposed.The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD;then,ITD is applied for redundant information denoising on special sub-series,and the denoised deformation is divided into the trend and periodic deformation components.Meanwhile,several relevant triggering factors affecting the VD are considered,from which the input features are extracted by Grey relational analysis(GRA).After that,DE-SFLA-LSSVM is separately performed to predict the trend and periodic deformation with the optimal inputs.Ultimately,the two individual forecast components are reconstructed to obtain the final predicted values.Two VD series monitored in Xiluodu reservoir region are utilized to verify the proposed model.The results demonstrate that:(1)Compared with Discrete wavelet transform(DWT),better denoising performance can be achieved by EEMD-ITD;(2)Using GRA to screen the optimal input features can effectively quantify the deformation response relationship to the triggering factors,and reduce the model complexity;(3)The proposed hybrid model in this study displays superior performance on some compared models(e.g.,LSSVM,Backward Propagation neural network(BPNN),and DE-SFLA-BPNN)in terms of forecast accuracy.

Time effect and prediction of broken rock bulking coefficient on the base of particle discrete element method

Bulking characteristics of gangue are of great significance for the stability of goafs in mining overburden in the caving zones.In this paper,a particle discrete element method with clusters to represent gangue was adopted to explore the bulking coefficient time effect of the broken rock in the caving zone under three-dimensional triaxial compression condition.The phenomena of stress corrosion,deformation,and failure of rock blocks were simulated in the numerical model.Meanwhile,a new criterion of rock fragments damage was put forward.It was found that the broken rock has obvious viscoelastic properties.A new equation based on the Burgers creep model was proposed to predict the bulking coefficient of broken rock.A deformation characteristic parameter of the prediction equation was analyzed,which can be set as a fixed value in the mid-and long-term prediction of the bulking coefficient.There are quadratic function relationships between the deformation characteristic parameter value and Talbot gradation index,axial pressure and confining pressure.

Three-dimensional consolidation deformation analysis of porous layered soft soils considering asymmetric effects

Long-term settlements for underground structures, such as tunnels and pipelines, are generally observed after the completion of construction in soft clay. The soil consolidation characteristic has great influences on the long-term deformation for underground structures. A three-dimensional consolidation analysis method under the asymmetric loads is developed for porous layered soil based on Biot's classical theory. Time-displacement effects can be fully considered in this work and the analytical solutions are obtained by the state space approach in the Cartesian coordinate. The Laplace and double Fourier integral transform are applied to the state variables in order to reduce the partial differential equations into algebraic differential equations and easily obtain the state space solution. Starting from the governing equations of saturated porous soil, the basic relationship of state space variables is established between the ground surface and the arbitrary depth in the integral transform domain. Based on the continuity conditions and boundary conditions of the multi-layered pore soil model, the multi-layered pore half-space solutions are obtained by means of the transfer matrix method and the inverse integral transforms. The accuracy of proposed method is demonstrated with existing classical solutions. The results indicate that the porous homogenous soils as well as the porous non-homogenous layered soils can be considered in this proposed method. When the consolidation time factor is 0.01, the value of immediate consolidation settlement coefficient calculated by the weighted homogenous solution is 27.4% bigger than the one calculated by the non-homogeneity solution. When the consolidation time factor is 0.05, the value of excess pore water pressure for the weighted homogenous solution is 27.2% bigger than the one for the non-homogeneity solution. It is shown that the material non-homogeneity has a great influence on the long-term settlements and the dissipation process of excess pore water pressure.

Predictive analysis of stress regime and possible squeezing deformation for super-long water conveyance tunnels in Pakistan

The prediction of the stress field of deep-buried tunnels is a fundamental problem for scientists and engineers. In this study, the authors put forward a systematic solution for this problem. Databases from the World Stress Map and the Crustal Stress of China, and previous research findings can offer prediction of stress orientations in an engineering area. At the same time, the Andersonian theory can be used to analyze the possible stress orientation of a region. With limited in-situ stress measurements, the Hock-Brown Criterion can be used to estimate the strength of rock mass in an area of interest by utilizing the geotechnical investigation data, and the modified Sheorey＇s model can subsequently be employed to predict the areas＇ stress profile, without stress data, by taking the existing in-situ stress measurements as input parameters. In this paper, a case study was used to demonstrate the application of this systematic solution. The planned Kohala hydropower plant is located on the western edge of Qinghai-Tibet Plateau. Three hydro-fracturing stress measurement campaigns indicated that the stress state of the area is SH - Sh 〉 Sv or SH 〉Sv 〉 Sh. The measured orientation of Sn is NEE （N70.3°-89°E）, and the regional orientation of SH from WSM is NE, which implies that the stress orientation of shallow crust may be affected by landforms. The modified Sheorey model was utilized to predict the stress profile along the water sewage tunnel for the plant. Prediction results show that the maximum and minimum horizontal principal stres- ses of the points with the greatest burial depth were up to 56.70 and 40.14 MPa, respectively, and the stresses of areas with a burial depth of greater than 500 m were higher. Based on the predicted stress data, large deformations of the rock mass surrounding water conveyance tunnels were analyzed. Results showed that the large deformations will occur when the burial depth exceeds 300 m. When the burial depth is beyond 800 m, serious squeezing deformations will occur in the surrounding rock masses, thus requiring more attention in the design and construction. Based on the application efficiency in this case study, this prediction method proposed in this paper functions accurately.

A Characteristic Analysis of the Dynamic Evolution of Preseismic-Coseismic-Postseismic Interferometric Deformation Fields Associated with the M 7.9 Earthquake of Mani,Tibet in 1997

By using the D-InSAR technique, we have acquired the temporal-spatial evolution images of preseismic.cosesimci-postseismic interferometric deformation fields associated with the M 7.9 earthquake of Mani, Tibet on 8 November 1997. The analysis of these images reveals the relationships between the temporal-spatial evolution features of the interferometric deformation fields and locking, rupturing, and elastic restoring of the source rupture plane, which represent the processes of strain accumulation, strain release, and postseismic restoration. The result shows that 10 months prior to the Mani event, a left-lateral shear trend appeared in the seismic area, which was in accordance with the earthquake fault in nature. The quantity of local deformation on the north wall was slightly larger than that on the south wall, and the deformation distribution area of the north wall was relatively large. With the event impending, the deformation of the south wall varied increasingly, and the deformation center shifted eastward. Two and half monthd before the event, the west side of the fault was still locked while the east side began to slide, implying that the whole fault would rupture at any moment. These features can be regarded as short-term precursors to this earthquake. Within the period from 16 April 1996 to two and half months before the earthquake, the most remarkable deformation zones appeared in the north and south walls, which were parallel to and about 40 km apart from the fault, with accumulated local displacements of 344 mm and 251 mm on the north and south walls, respectively. The south wall was the active one with larger displacements. Five months after the earthquake, the distribution feature of interferometric fringes was just opposite to that prior to the event, expressing evident right-lateral shear. The recovered displacements are -179 mm on the north wall and -79 mm on the south wall, close to the east side of the fault. However, in the area of the south wall far from the fault there still existed a trend of sinistral motion. The deformation of the north wall was small but recovered fast in a larger area, while the active south wall began to recover from the east section of the fault toward the WSW.

Deformation resistance prediction of tandem cold rolling based on grey wolf optimization and support vector regression

In the traditional rolling force model of tandem cold rolling mills,the calculation of the deformation resistance of the strip head does not consider the actual size and mechanical properties of the incoming material,which results in a mismatch between the deformation resistance setting and the actual state of the incoming material and thus affects the accuracy of the rolling force during the low-speed rolling process of the strip head.The inverse calculation of deformation resistance was derived to obtain the actual deformation resistance of the strip head in the tandem cold rolling process,and the actual process parameters of the strip in the hot and cold rolling processes were integrated to create the cross-process dataset as the basis to establish the support vector regression(SVR)model.The grey wolf optimization(GWO)algorithm was used to optimize the hyperparameters in the SVR model,and a deformation resistance prediction model based on GWO–SVR was established.Compared with the traditional model,the GWO–SVR model shows different degrees of improvement in each stand,with significant improvement in stands S3–S5.The prediction results of the GWO–SVR model were applied to calculate the head rolling setting of a 1420 mm tandem rolling mill.The head rolling force had a similar degree of improvement in accuracy to the deformation resistance,and the phenomenon of low head rolling force setting from stands S3 to S5 was obviously improved.Meanwhile,the thickness quality and shape quality of the strip head were improved accordingly,and the application results were consistent with expectations.

Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction

Urban subway tunnel construction inevitably disturbs the surrounding rock and causes the deformation of existing subway structures. Dynamic predictions of the tunnel horizontal displacement, tunnel ballast settlement, and tunnel differential settlement are important for ensuring the safety of buildings and tunnels. First, based on the Hangzhou Metro project, we analyzed the influence of construction on the deformation of existing subway structures and the difficulties and key points in monitoring. Then, a deformation prediction model, based on a back propagation(BP) neural network, was established with massive monitoring data. In particular, we analyzed the influence of four structures of the BP neural network on prediction performance, i.e., single input–single hidden layer–single output, multiple inputs–single hidden layer–single output, single input–double hidden layers–single output, and multiple inputs–double hidden layers–single output, and verified them using measured data.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

Method of forecasting seismic energy induced by longwall exploitation based on changes in ground subsidence

A method of forecasting total seismic energy induced by longwall exploitation, based on changes in ground subsidence, is presented in the form of a linear regression model with one with one independent variable. In the method, ground subsidence is described with a cross-section area of a subsidence trough Pw along a line of observations in the direction of an advancing longwall front, approximately along the axis of the longwall area. Total seismic energy is determined on the basis of seismic energy data of tremors induced by exploitation. The presentation consists of a detailed method and evaluation of its predictive ability for the area of longwall exploitation within the region of one of the coal mines in the Upper Silesian Coal Basin. This method can be used for forecasting the total seismic energy released by tremors within the area directly connected with the exploitation, in which the seismic activity induced by this exploitation occurs. The estimation of the parameters of the determined model should each time be carried out with investigations of the correctness of the model. The method cannot be applied when the number of recorded phenomena is small and when there is insufficient data to make it possible to calculate the index Pw.

An improved artificial bee colony-random forest(IABC-RF)model for predicting the tunnel deformation due to an adjacent foundation pit excavation

An improved artificial bee colony-random forest(IABC-RF)model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit.A new search strategy of the artificial bee colony(ABC)algorithm is herein developed and incorporated,with the results showing that a much higher computational efficiency can be achieved with the new model,while high computational accuracy can also be maintained.The improved ABC algorithm is thereafter utilised and combined with the random forest(RF)model,where four important hyper-parameters are optimized,for a tunnel deformation prediction.Results are thoroughly compared with those of other prediction methods based on machine learning(ML),as well as the monitored data on the site.Via the comparisons,the validity and effectiveness of the proposed model are fully demonstrated,and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering.

Analysis of meso-inhomogeneous deformation on a metal material surface under low-cycle fatigue

A polycrystalline Voronoi aggregation with a free surface is applied as the representative volume element（RVE）of the nickel-based GH4169 superalloy.Considering the plastic deformation mechanism at the grain level and the Bauschinger effect,a crystal plasticity model reflecting the nonlinear kinematic hardening of crystal slipping system is applied.The microscopic inhomogeneous deformation during cyclic loading is calculated through numerical simulation of crystal plasticity.The deformation inhomogeneity on the free surface of the RVE under cyclic loading is described respectively by using the following parameters：standard deviation of the longitudinal strain in macro tensile direction,statistical average of first principal strains,and standard deviation of longitudinal displacement.The relationship between the fatigue cycle number and the evolution of inhomogeneous deformation of the material’s free surface is investigated.This research finds that：（1）The inhomogeneous deformation of the material free surface is significantly higher than that of the RVE inside;（2）the increases of the characterization parameters of inhomogeneous deformation on the free surface with cycles reflect the local maximum deformation of the RVE growing during cyclic loading;（3）these parameters can be used as criteria to assess and predict the low-cycle fatigue life rationally.