A new evolutionary polynomial regression technique to assess the fundamental periods of irregular buildings

2021 ◽  
Author(s):  
Sebastiano Marasco ◽  
Gian Paolo Cimellaro
Keyword(s):  
Polynomial Regression ◽  
Regression Technique ◽  
Evolutionary Polynomial Regression ◽  
Irregular Buildings

scholarly journals Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Ali Ghorbani ◽  
Mostafa Firouzi Niavol
Keyword(s):  
Neural Networks ◽  
Finite Difference ◽  
Polynomial Regression ◽  
Three Dimensional ◽  
Dynamic Loads ◽  
Prototype Model ◽  
Evolutionary Polynomial Regression ◽  
Dimensional Finite Element ◽  
Heavy Loads ◽  
Three Dimensional Finite Element

Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

scholarly journals Comparison of Different Approaches to Predict the Performance of Pumps As Turbines (PATs)

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 1016 ◽  
Author(s):  
Mauro Venturini ◽  
Stefano Alvisi ◽  
Silvio Simani ◽  
Lucrezia Manservigi
Keyword(s):  
Simulation Model ◽  
Ad Hoc ◽  
Polynomial Regression ◽  
Point Of View ◽  
Box Model ◽  
Physical Point ◽  
Evolutionary Polynomial Regression ◽  
Box Models ◽  
Performance Curves ◽  
Physics Based Simulation

This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs). The considered approaches are four, i.e., one physics-based simulation model (“white box” model), two “gray box” models, which integrate theory on turbomachines with specific data correlations, and one “black box” model. More in detail, the modeling approaches are: (1) a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2) a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3) the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4) an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53–5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed.

Development of robust evolutionary polynomial regression network in the estimation of stable alluvial channel dimensions

Geomorphology ◽  
2020 ◽  
Vol 350 ◽  
pp. 106895 ◽  
Author(s):  
Hossein Bonakdari ◽  
Azadeh Gholami ◽  
Ahmed M.A. Sattar ◽  
Bahram Gharabaghi
Keyword(s):  
Polynomial Regression ◽  
Evolutionary Polynomial Regression ◽  
Alluvial Channel

Predicting Landslide Displacements by Multi-objective Evolutionary Polynomial Regression

2014 ◽  
pp. 651-654 ◽  
Author(s):  
Angelo Doglioni ◽  
Giovanni B. Crosta ◽  
Paolo Frattini ◽  
Nicola L. Melidoro ◽  
Vincenzo Simeone
Keyword(s):  
Polynomial Regression ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression

scholarly journals Prediction of side thermal buoyant discharge in the cross flow using multi-objective evolutionary polynomial regression (EPR-MOGA)

2019 ◽  
Vol 21 (6) ◽  
pp. 980-998
Author(s):  
Milad Khosravi ◽  
Mitra Javan
Keyword(s):  
Polynomial Regression ◽  
Velocity Ratio ◽  
Three Dimensional ◽  
Cross Flow ◽  
Temperature Profiles ◽  
Cfd Model ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression ◽  
The Cross ◽  
Thermal Cross Section

Abstract The capability to predict the distribution of pollutants in water bodies is one of the most important issues in the design of jet outfalls. Three-dimensional computational fluid dynamics (CFD) model and multi-objective evolutionary polynomial regression (EPR-MOGA) are used and compared in modeling the temperature field in the side thermal buoyant discharge in the cross flow. The input variables used for training the EPR-MOGA models are spatial coordinates (x, y, z), jet to cross flow velocity ratio (R), depth of the channel (d), and the temperature excess (T0). A previous experimental study is used to verify and compare the performance of the EPR-MOGA and CFD models. The results show that the EPR-MOGA model predicts the thermal cross section of the flow and the spread of pollutants at the surface with a better accuracy than the CFD model. However, the CFD method performs significantly better than EPR-MOGA in predicting temperature profiles. The uncertainty analysis indicated that the EPR-MOGA model had lower mean prediction error and smaller uncertainty band than the CFD model. The relationships achieved by the EPR-MOGA model are very useful to predict temperature profiles, temperature half-thickness, and temperature spread on surface in practice.

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