Gene-expression programming for the assessment of surface mean pressure coefficient on building surfaces

Mallick, Monalisa; Mohanta, Abinash; Kumar, Awadhesh; Patra, Kanhu Charan

doi:10.1007/s12273-019-0583-8

Cited by 17 publications

(7 citation statements)

References 39 publications

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“…Yang et al [11] proposed a new spectral model for leaf area index estimation based on GEP. Mallick et al [12] used GEP to evaluate the surface average pressure coefficient of the building surface. Ali et al [13] used GEP to predict the air ratio parameters of mineral tailings.…”

Section: Introductionmentioning

confidence: 99%

A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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Gene expression programming (GEP) uses simple linear coding to solve complex modeling problems. However, the performance is limited by the effectiveness of the selected method of evaluating population individuals, the breadth and depth of the search domain for the solution, and the ability of accuracy of correcting the solution based on historical data. Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. It takes measures to specialize origin data to reserve the elite individuals, reevaluate the target individuals, and process data and solutions via the similar period mode, which avoids the tendency to get stuck in local optimum and the complexity of the precisions of correcting complex modeling problems due to insufficiency scope of the search domain, and subsequently, better convergence results are obtained. If we take the leek price and the sunspot observation data as the sample to compare the new algorithm with the GEP simulation test, the results indicate that the new algorithm possesses more powerful exploration ability and higher precision. Under the same accuracy requirements, the new algorithm can find the individual faster. Additionally, the conclusion can be drawn that the performance of new algorithm is better on the condition that we take another set of sunspot observations as samples, combining the ARIMA algorithm and BP neural network prediction algorithm for simulation and comparison with the new algorithm.

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Section: Introductionmentioning

confidence: 99%

A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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“…Therefore, it is important to examine mean wind pressures on different surfaces of an irregular plan shaped buildings. Moreover, pressure contour on different surfaces of a building may provide an idea of pressure variation in a building (Bairagi and Dalui, 2020; Blocken et al , 2007; Mallick et al , 2020; Liu et al , 2020). It has been emphasized in various studies that the effect of pressure variation is considerably higher in the buildings having irregular plan shape (Chakraborty et al , 2014; Bhattacharyya and Dalui, 2018, 2020; Gomes et al , 2005; Li et al , 2020; Sanyal and Dalui, 2020; Mukherjee et al , 2014).…”

Section: Introductionmentioning

confidence: 99%

Wind flow on and around U-shaped buildings

Günaydın

2021

JEDT

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Purpose This paper presents the numerical examination of wind pressure distributions on U-plan shaped buildings having four different depth ratios (DR) as 0.5, 1, 2 and 4 over wind incidence angle (WIA) of 0°. The purpose of this study is to investigate the effect of irregular building form, DRs, distances from the reentrant corner, wind velocity values on and around wind pressure distributions of the buildings. With this aim, ANSYS Fluent 20.0 Computational Fluid Dynamics (CFD) program is used for the analysis. Design/methodology/approach Four U-shaped buildings having the same height, width and wing length but having different DR in plan were analyzed by the application of CFD package of ANSYS 20. With this purpose, wind pressure distributions on and around U-plan shaped buildings were analyzed for the wind velocity values of 2 and 5 m/s over WIA of 0°. Comprehensive results were obtained from the analyses. Findings While the change in the DR values did not create a significant change in positive pressure coefficients on A and E surfaces, negative pressure values increased as the DR decreased. The negative pressure coefficients observed on the A and E surfaces become higher than the positive pressure coefficients with the decrease in the DR. On contrary to that condition, with the decrease in the DR, G surfaces take higher positive pressure coefficients than the negative pressure coefficients. The reason for this is that the DR decreases and negative pressure values on G surface significantly decrease. The effect of the DR on the pressure coefficients is remarkable on B and D surfaces. The negative pressure coefficients on the B and D surfaces tend to increase as the DR decreases. Research limitations/implications This study focused on DRs and wind velocity values effect on pressure coefficients to limit variables. Different building wing dimensions did not take into account. Originality/value Although there are a number of studies related to wind behavior of irregular plan shaped buildings, irregular building forms have not been extensively investigated parametrically, especially in terms of the effect of DR on wind pressures. This study is therefore designed to fill this gap by analyzing impacts of various parameters like building shape with various DRs, WIA and wind velocity values on wind pressure distributions and velocity distributions on and around the building.

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“…The selected models are recreated in GEP using one or more genetic operators, such as mutation or recombination. Previous studies provide a concise conceptual outline of GEP in their descriptions (Mallick et al 2020). The relationship (Equation ( 23)) illustrates the water surface profile of nonprismatic compound channels varies as a function of the geometric and hydraulic factors.…”

Section: Gep Modelmentioning

confidence: 99%

Assessment of water surface profile in nonprismatic compound channels using machine learning techniques

Kaushik

Kumar

2022

Water Supply

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Accurate prediction of water surface profile in an open channel is the key to solving numerous critical engineering problems. The goal of the current research is to predict the water surface profile of a compound channel with converging floodplains using machine learning approaches, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), in terms of both geometric and flow variables, as past studies were more focused on geometric variables. A novel equation was also proposed using gene expression programming to predict the water surface profile. In order to evaluate the performance and efficacy of these models, statistical indices are used to validate the produced models for the experimental analysis. The findings demonstrate that the suggested ANN model accurately predicted the water surface profile, with coefficient of determination (R2) of 0.999, root mean square error (RMSE) of 0.003, and mean absolute percentage error (MAPE) of 0.107%, respectively, when compared to GEP, SVM, and previously developed methods. The study confirms the application of machine learning approaches in the field of river hydraulics, and forecasting water surface profile of nonprismatic compound channels using a proposed novel equation by gene expression programming made this study unique.

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Gene-expression programming for the assessment of surface mean pressure coefficient on building surfaces

Cited by 17 publications

References 39 publications

A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period

A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period

Wind flow on and around U-shaped buildings

Assessment of water surface profile in nonprismatic compound channels using machine learning techniques

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