Significance Of Input Variables Research Articles

Stacking Ensemble-Based Machine Learning Model for Predicting Deterioration Components of Steel W-Section Beams

The collapse evaluation of the structural systems under seismic loading necessitates identifying and quantifying deterioration components (DCs). In the case of steel w-section beams (SWSB), three distinct types of DCs have been derived. These deterioration components for steel beams comprise the following: pre-capping plastic rotation (θp), post-capping plastic rotation (θpc), and cumulative rotation capacity (Λ). The primary objective of this research is to employ a machine learning (ML) model for accurate determination of these deterioration components. The stacking model is a powerful combination of meta-learners, which is used for better learning and performance of base learners. The base learners consist of AdaBoost, Random Forest (RF), and XGBoost. Among various machine learning algorithms, the stacking model exhibited superior functioning. The evaluation metrics of the stacking model were as follows: R2 = 0.9 and RMSE = 0.003 for θp, R2 = 0.97 and RMSE = 0.012 for θpc, and R2 = 0.98 and RMSE = 0.09 for Λ. The significance of input variables, specifically the web-depth-over-web-thickness ratio (h/tw) and the flange width-to-thickness ratio (bf/2tf), in determining the deterioration components was assessed using the Shapley Additive Explanations model. These parameters emerged as the most crucial factors in the evaluation.

Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods

Shear wave velocity is considered as one of the most important rock physical parameters which can be measured by dipole sonic imager (DSI) tool. This parameter is applied to evaluate porosity and permeability, rock mechanical parameters, lithology, fracture assessment, etc. On the other hand, this data is not available in all wells and hence, an accurate and reliable estimation of this parameter with the least uncertainty is of great importance in reservoir characterization. In this study, regression, multi-layer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP) methods are utilized to estimate the shear wave velocity using well log data. Also, the reported empirical correlations in the literature are also investigated in the studied field. The input data include depth, effective porosity, V p , gamma ray logs (natural and spectral), neutron log, density log and caliper log from the Bangestan Group Formation in one of the fields in southwestern Iran. In this study, all the expressed methods are compared based on the best coefficient of determination (R 2 ), root mean square error (RMSE), mean squared error (MSE), average absolute relative error (AARE), and average relative error (ARE). Among the used methods, MGGP was developed for using the useful features of this method including sensitivity analysis and correlation. Sensitivity analysis is performed on the input data using the MLP-ANN and MGGP method. Also, a correlation is suggested based on the MGGP method which is able to predict the shear wave velocity using the mentioned input parameters. The results show that the MLP-ANN method is more accurate, reliable and efficient compared to other methods studied in this paper. R 2 for the train, validation, and test phase are 0.9973, 0.9901 and 0.9898, respectively. The results of sensitivity analysis imply that compressional wave velocity has the highest impact on the shear wave velocity. Finally, Young Dynamic Modulus and Poisson Dynamic Ratio are computed using both real and predicted shear wave velocities. The results indicate that these two parameters can be calculated with high accuracy using predicted shear wave velocity. • Five methods are implemented for predicting shear wave velocity (V s ) using petrophysical logs. • ANN can predict the V s with high accuracy (R 2 Test = 0.9898). • A sensitivity analysis performed on the significance of input variables on the V s using the results of MGGP. • A correlation is introduced for forecasting the V s . • Investigate the effects of V s prediction by ANN on the geomechanical modelling.

Normalized neighborhood component feature selection and feasible-improved weight allocation for input variable selection

In design optimization, as the number of input variables increases, the convergence rate of optimization tends to decrease, and the number of function calls and design change costs tend to increase. Neighborhood component feature selection (NCFS) was adopted to select significant input variables. However, the parameter determination process of the NCFS incurs a high computational cost and weakens robustness. Therefore, this study proposes a normalized NCFS (nNCFS) by normalizing scales between mean loss and regularization terms via the initial dataset Additionally, in the case of a multi-response system, complex decision-making processes that involve the allocation of weights for multiple responses are required. It is possible to allocate weights by using conventional methods such as the analytic hierarchy process and entropy methods. However, the analytic hierarchy process method is highly influenced by the designer’s subjectivity, and the entropy method is unable to consider a design optimization problem. Accordingly, the feasible-improved weight allocation (FIWA) method is now proposed by considering a design optimization problem objectively. Comparing the NCFS with the nNCFS through mathematical examples, we found that the nNCFS significantly improved the computational cost and robustness. Moreover, the FIWA method selected significant input variables that yielded feasible and improved designs. Then, the nNCFS and the FIWA methods were applied to the design of the body-in-white of a vehicle. The significance of input variables was analyzed using the nNCFS, and feasible and improved designs were obtained on the basis of the significant input variables selected using the FIWA method.

Analysis and Performance Optimization of Variable Compression Ratio Diesel Engine using Canola Oil Based Biodiesel

Rapid development of the global economy demands huge amount of Energy. Transportation sector contributes major part of economy development. Fossil fuels like Petrol and Diesel are used for transportation and heavy duty vehicles. Higher rate of consumption of these naturally available fuels leads to its depletion and deterioration of environment. To solve such issue many researchers are interested in identifying and using alternative fuels for internal combustion engines. Biodiesel is renewable fuel and can be used as alternative fuel for Diesel engines. In present work Canola oil based Biodiesel (COB) is blended in Diesel with different concentration like B20, B40 and B60. Engine input variables like compression ratio (13:1, 15:1, 17:1) and load (4, 8,12kg) are considered to optimize the results. The Performance of Variable Compression Ratio (VCR) Diesel Engine is evaluated using Taguchi method. Analysis of variance is conducted to recognise the significance of input variables on Brake Thermal Efficiency, Brake power Specific Fuel Consumption. Results of optimization showed that load is the most crucial factor which affects the engine performance i.e. Increase in load decrease the Brake Thermal Efficiency, Brake Power and increases the Specific Fuel Consumption.

New formulae for capacity energy-based assessment of liquefaction triggering

Liquefaction of loose saturated soil deposits is a hazardous type of ground failure occurring under earthquake excitations. Therefore, an accurate estimation of liquefaction potential is extremely important in geotechnical engineering. In the current study, a new model is proposed which estimates the level of strain energy needed for liquefaction initiation. A compiled database containing cyclic tests gathered from previously published works was used to develop new models to predict liquefaction potential. M5′ algorithm was used to find the best correlation between parameters. It was shown that not only the derived formulas are acceptably accurate but also they feature a very simple structure in comparison with available formulas in the literature. The proposed equations are accurate, physically sound and uncomplicated. Furthermore, safety factors were given for different levels of risk, which can be useful for engineering practice. In addition, the influence of different predictors on the liquefaction potential was evaluated and also the significance of input variables was assessed via sensitivity analysis. Finally, a new model was introduced for preliminary estimation of liquefaction potential.

Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents

BackgroundFew studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model, multiple logistic regression (MLR) model, and Cox regression model.MethodsThis study compared the MLR, Cox, and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010. An estimation dataset was used to train the model, and a validation dataset was used to evaluate model performance. The sensitivity analysis was also used to assess the relative significance of input variables in the prediction model.ResultsThe ANN model significantly outperformed the MLR and Cox models in predicting 5-year mortality, with higher overall performance indices. The results indicated that the 5-year postoperative mortality of breast cancer patients was significantly associated with age, Charlson comorbidity index (CCI), chemotherapy, radiotherapy, hormone therapy, and breast cancer surgery volumes of hospital and surgeon (all P < 0.05). Breast cancer surgery volume of surgeon was the most influential (sensitive) variable affecting 5-year mortality, followed by breast cancer surgery volume of hospital, age, and CCI.ConclusionsCompared with the conventional MLR and Cox models, the ANN model was more accurate in predicting 5-year mortality of breast cancer patients who underwent surgery. The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.

Optimisation of electrochemical drilling process using Taguchi method and regression analysis

Inconel 718 is a hard to machine nickel-based super alloy. It is extensively used in aerospace industries. It has high-strength and poor thermal diffusion which results in decreased life of tool and difficulty in machining by traditional methods. Unconventional machining processes have evolved over a period to meet such demands and claim to be a suitable alternative to conventional machining. Electrochemical machining (ECM) is an unconventional manufacturing process which is used to machine hard to machine materials. This paper presents an investigation on the application of Taguchi's approach and regression model to optimise the electrochemical drilling process of Inconel 718. The trials are planned using design of experiments proposed by Taguchi. Analysis of variance (ANOVA) is employed to determine the significance of input variables on the desired performance measures. The control of form and orientation errors of machined surfaces needs attention and included as desired performance. Multiple regression models have been developed to predict the desired performance measures in electrochemical drilling of Inconel 718.

Demographic Models for Projecting Population and Migration: Methods for African Historical Analysis

This study presents methods for projecting population and migration over time in cases were empirical data are missing or undependable. The methods are useful for cases in which the researcher has details of population size and structure for a limited period of time (most obviously, the end point), with scattered evidence on other times. It enables estimation of population size, including its structure in age, sex, and status, either forward or backward in time. The program keeps track of all the details. The calculated data can be reported or sampled and compared to empirical findings at various times and places to expected values based on other procedures of estimation. The application of these general methods that is developed here is the projection of African populations backwards in time from 1950, since 1950 is the first date for which consistently strong demographic estimates are available for national-level populations all over the African continent. The models give particular attention to migration through enslavement, which was highly important in Africa from 1650 to 1900. Details include a sensitivity analysis showing relative significance of input variables and techniques for calibrating various dimensions of the projection with each other. These same methods may be applicable to quite different historical situations, as long as the data conform in structure to those considered here.