Performance analysis of empirical models for predicting rock mass deformation modulus using regression and Bayesian methods

Deformation modulus of rock mass is one of the input parameters to most rock engineering designs and constructions.The field tests for determination of deformation modulus are cumbersome,expensive and time-consuming.This has prompted the development of various regression equations to estimate deformation modulus from results of rock mass classifications,with rock mass rating(RMR)being one of the frequently used classifications.The regression equations are of different types ranging from linear to nonlinear functions like power and exponential.Bayesian method has recently been developed to incorporate regression equations into a Bayesian framework to provide better estimates of geotechnical properties.The question of whether Bayesian method improves the estimation of geotechnical properties in all circumstances remains open.Therefore,a comparative study was conducted to assess the performances of regression and Bayesian methods when they are used to characterize deformation modulus from the same set of RMR data obtained from two project sites.The study also investigated the performance of different types of regression equations in estimation of the deformation modulus.Statistics,probability distributions and prediction indicators were used to assess the performances of regression and Bayesian methods and different types of regression equations.It was found that power and exponential types of regression equations provide a better estimate than linear regression equations.In addition,it was discovered that the ability of the Bayesian method to provide better estimates of deformation modulus than regression method depends on the quality and quantity of input data as well as the type of the regression equation.

Preliminary Application of Combinatorial Measurement and Regression Analysis Method to High Precision Instrumental Analysis

With determination micro-Fe by 1, 10-phenanthroline spectrophotometry for example, they are systematically introduced the combinatorial measurement and regression analysis method application about metheodic principle, operation step and data processing in the instrumental analysis, including: calibration curve best linear equation is set up, measurand best linear equation is set up, and calculation of best value of a concentration. The results showed that mean of thrice determination , s = 0 μg/mL, RSD = 0. Results of preliminary application are simply introduced in the basic instrumental analysis for atomic absorption spectrophotometry, ion-selective electrodes, coulometry and polarographic analysis and are contrasted to results of normal measurements.

DDM regression analysis of the in-situ stress field in a non-linear fault zone

A multivariable regression analysis of the in-situ stress field, which considers the non-linear deformation behavior of faults in practical projects, is presented based on a newly developed three-dimensional displacement discontinuity method （DDM） program. The Bar- ton-Bandis model and the Kulhaway model are adopted as the normal and the tangential deformation model of faults, respectively, where the Mohr-Coulomb failure criterion is satisfied. In practical projects, the values of the mechanical parameters of rock and faults are restricted in a bounded range for in-situ test, and the optimal mechanical parameters are obtained from this range by a loop. Comparing with the traditional finite element method （FEM）, the DDM regression results are more accurate.

Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy)

This paper discusses some methodological aspects for the production of susceptibility maps of slope instability developed within the CARG Project (Geological Cartography of Italy at 1:50,000 scale). It describes an example of a susceptibility map in the presence of low susceptibility, using database having zero or negligible cost, with the aim to test some methodologies that can be easily reproducible to get a first estimate of the landslide susceptibility on a wide area. Two statistical approaches have been applied: the non-parametric conditional analysis and the logistic analysis for rare events. The predictive ability obtained from the two methodologies, was evaluated by the success-prediction curves for the conditional analysis, and by the Receiver Operating Characteristic curve (ROC), for the logistic model. The landslide susceptibility maps have been classified into four classes using both the Natural Breaks algorithm and the method proposed by Chung and Fabbri (2003). The paper considers the influence of these two classification methods on the quality of final results.

Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al2O3, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.

Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods

The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields,especially in the food industry.The k-nearest neighbor(k-NN)method of Near-Infrared Reflectance(NIR)analysis is practical,relatively easy to implement,and becoming one of the most popular methods for conducting food quality based on NIR data.The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables,while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables.The objective of this paper is to use the functional Near-Infrared Reflectance(NIR)spectroscopy approach to predict some chemical components with some modern statistical models based on the kernel and k-Nearest Neighbour procedures.In this paper,three NIR spectroscopy datasets are used as examples,namely Cookie dough,sugar,and tecator data.Specifically,we propose three models for this kind of data which are Functional Nonparametric Regression,Functional Robust Regression,and Functional Relative Error Regression,with both kernel and k-NN approaches to compare between them.The experimental result shows the higher efficiency of k-NN predictor over the kernel predictor.The predictive power of the k-NN method was compared with that of the kernel method,and several real data sets were used to determine the predictive power of both methods.

The Method for Optimum Estimation of COVID-19 Variant Type Virus Infection Status Analysis by the Multivariate Analysis Considering the Environmental Variability Impact in Japan

Currently, the estimated value of the effective reproduction number (ERN), which is an index for grasping the COVID-19 infection status, is used for important planning and evaluation of infection prevention measures. Since ERN in the Sequential SIR model fluctuates in multiple dimensions due to changes in the surrounding environment, it is difficult to set the appropriate accuracy of the uncertainty region of the estimated data. The challenge in this study is to build a mathematical model of infectious disease according to the characteristics and data characteristics of the infectious disease and select an appropriate estimation method. Highly accurate quantitative research that analyzes the validity of “how infectious diseases prevail” from an academic point of view is the key to prediction and estimation in appropriate infection situation analysis. In this study, we adopted a statistical multivariate analysis method (T method) that enables evaluation and prediction of important factors related to ERN estimation and analysis of phenomena that change in real time (time series analysis). It was clarified that it is possible to estimate with higher accuracy by applying the T method to the estimated value of ERN by the current SIR mathematical model.

The k Nearest Neighbors Estimator of the M-Regression in Functional Statistics

It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are introduced.The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic.We use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust model.Under some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach.

Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames

Steel frames equipped with buckling restrained braces(BRBs)have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces.Therefore,special attention has been paid to the seismic risk assessment(SRA)of such structures,e.g.,seismic fragility analysis.Conventional approaches,e.g.,nonlinear finite element simulation(NFES),are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures.In this study,amachine learning(ML)-based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions.An optimal artificial neural network model can be trained using calculated damage and intensity measures,a technique which will be used to compute the fragility curves of a steel BRB frame instead of employing NFES.Numerical results show that a highly efficient instantaneous failure probability assessment can be made with the proposed framework for realistic large-scale building structures.

Suspended Sediment Regression Model On Mud Coasts

Two statistical models of the concentration of suspended sediment on mud coasts have been developed by using the stepwise regression method. The statistical analysis in this paper is based on the field data of the mud coast of Lianyungang, China. The regression models provide an essentially complete statistical description of the processes of fine-grained sediment on open mud coasts.

Regression Formulations for the Stress at Hot Spot of Multiplanar Tubular DT Joints

Stress concentration analysis of multiplanar tubular DT joints plays an important role in the fatigue design of offshore platforms. A semi-analytic method for stress analysis under the condition of any loads is briefly introduced in the paper. Nineteen multiplanar tubular DT joints with one of two braces of the same dimension subjected to axial loads and out- of- plane bending moments are computed for parametric stress analysis by using the present method. The influence of geometrical parameters on the stresses of multiplanar tubular DT joints is discussed and compared with corresponding uniplanar T joints. The regression formulae for the stress at hot spot of multiplanar DT joints are found by modification of SCF of corresponding uniplanar T joints. The parametric formulae for the maximum stress by superposition. Finally, the influences of stiffening effect and load-interaction effect on the maximum stress of DT joints are discussed.

Comparative Analysis of Urban-rural Residents' Propensity to Consume in China's Four Regions

According to the data in China Statistical Yearbook from 1992 to 2008,by using regression model,we adopt least square method and generalized least square method to conduct empirical analysis on the relationship between urban-rural residents' income and consumption in China's east,northeast,central region and west.The results show that the urban-rural residents' propensity to consume in China's four regions has prominent characteristics.In terms of region,urban residents' marginal propensity to consume takes on irregular fluctuation,while the rural residents' propensity to consume conforms to law of diminishing of marginal propensity to consume;in terms of time sequence,the rural residents' marginal propensity to consume in China's four regions takes on "multi-U-form" fluctuation trend,and the rural residents' marginal propensity to consume in different regions has certain difference,while the urban residents' marginal propensity to consume takes on low-frequency broad width fluctuation trend;the urban-rural residents' average marginal propensity to consume in China's four regions conforms to the law of diminishing.In order to increase consumption and promote the balanced rapid development of regional economy,in light of the urban-rural difference and characteristics of different regions,we should propound effective measures to promote urban-rural residents' propensity to consume,and formulate and implement regional policy in order to stimulate consumption.

Analysis and Forecast on the Car Market of Our Country

This paper is intended to forecast the demand of the car market and the quantity of car possession in our country in 2000 by means of two statistics methods, i.e. tendency inference and regression analysis, and then its future growth tendency and market demand in our country are analyzed, according to the strategy requirement and the actual facts of our car industry development.

Rolling Gaussian Process Regression with Application to Regime Shifts

Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.

Biomechanical prediction of abdominal aortic aneurysm rupture risk: Sensitivity analysis

Objectives: The purpose of this research is to determine the quantitative relationship between the peak wall stress of abdominal aortic aneurysm (AAA) and its clinical risk factors including its maximum diameter, asymmetry index, wall thickness and abnormal high blood pressure. Methods: The response surface experimental design with one response and four variables was used to design the experimental tests. Thirty experiments were performed through finite element analysis in order to obtain the designed response values. Results: A nonlinear multivariable regression function was developed based on the experimental data. Results demonstrated the inefficiency of traditional 5-cm criterion for estimating the rupture of AAA. The profound effect of wall thickness on the peak wall stress has been observed and validated by the existing publications. Conclusion: The conventional 5-cm criterion for estimating AAA rupture might induce biased prediction, and multiple clinical risk factors need to be considered in realistic clinical settings.

Robust Linear Regression Models:Use of a Stable Distribution for the Response Data

In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality assumption. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, under a Bayesian approach, the use of a latent or auxiliary random variable gives some simplification to obtain any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to a standard linear regression model with an explanatory variable and the other is related to a simulated data set assuming a 23 factorial experiment. Posterior summaries of interest are obtained using MCMC (Markov Chain Monte Carlo) methods and the OpenBugs software.

Regression Analysis of Initial Stress Field Around Faults Based on Fault Throw by Displacement Discontinuity Method

The back analysis of initial stress is usually based on measured stress values, but the measuring of initial stress demands substantial investment. Therefore, amounts of underground engineering have no measured initial stress data, such as tunneling engineering. Focusing on this problem, a new back analysis method which does not need measured initial stress data is developed. The fault is assumed to be caused by initial load, the displacement discontinuity method (DDM) which considered non-linear fault is adopted to establish a numerical model of the engineering site, and the multivariable regression analysis of the initial stress field around the faults is carried out based on the fault throw. The result shows that the initial stress field around the faults is disturbed significantly, stress concentration appears in the tip zone, the regressive fault throw matches the measured values well, and the regressive initial stress field is reliable.

INVERSE REGRESSION METHOD IN DATA STRUCTURE ANALYSIS

In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.

A New Curve Fitting Method for Forming Limit Experimental Data

The forming limit curve （FLC） can be obtained by means of curve fitting the limit strain points of different strain paths. The theory of percent regression analysis is applied to the curve fitting of forming limit experimental data.Forecast intervals of FLC percentiles can be calculated. Thus reliability and confidence level can be considered. The theoretical method to get the limits of limit strain points distributing region is presented, and the FLC position can be adjusted according to practical requirement. Method for establishing FLC with high reliability using small samples is presented at the same time. This method can make full use of the current experimental data and the previous data.Compared with the traditional method that can only use current experimental data, fewer specimens are required in the present method to obtain the same precision and the result is more accurate with the same number of specimens.

The height-width ratio limited value for rubber bearing isolated structure computed by uniform design method

Rubber isolation is the most mature control technology in practical application, and is widely used by short rigid buildings. However, many high isolation buildings have been built around the world in recent years, which do not follow the existing criterions and codes. Many researchers began to research the special problems caused by larger height-width ratio isolation structures. The overturning effect of high height-width ratio structures with rubber bearing is firstly studied. Considering the main factors, such as the height-width ratio of structures, type of site, the designed basic acceleration of ground motion and the decouple factor in horizon, computing experiment is defined with the Uniform Design Method, which is also known as designing isolation structure. The forces of the bearing under edge of structures based on the position of the rubber bearing are calculated. The result indicates that the rubber bearings will lose its functionality under very high tension and compressing force of earthquake motion in horizon and vertical, when the height-width ratio is over a certain value. Thus, based on the calculation result of isolation structures defined in the uniform design method, regression analysis is conducted, and also the rubber edge force regression formula are gotten, which has higher correlation and smaller standard deviation. This formula can be used to roughly calculate whether the pull force occurs at the edge of the building. By the edge bearings of isolation structure minimum force formula, the height-width ratio limited value of the isolation structure is deducted when rubber bearing has minimum force of zero.