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Gotowa bibliografia na temat „GEP EQUATION”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "GEP EQUATION"

Salmasi, Farzin, and John Abraham. "Discharge coefficients for ogee weirs including the effects of a sloping upstream face." Water Supply 20, no. 4 (2020): 1493–508. http://dx.doi.org/10.2166/ws.2020.064.

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Abstract Discharge coefficients (C0) for ogee weirs are essential factors for predicting the discharge-head relationship. The present study investigates three influences on the C0: effect of approach depth, weir upstream face slope, and the actual head, which may differ from the design head. This study uses experimental data with multiple non-linear regression techniques and Gene Expression Programming (GEP) models that are applied to introduce practical equations that can be used for design. Results show that the GEP method is superior to the regression analysis for predicting the discharge coefficient. Performance criteria for GEP are R2 = 0.995, RMSE = 0.021 and MAE = 0.015. Design examples are presented that show that the proposed GEP equation correlates well with the data and eliminates linear interpolation using existing graphs.

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Wang, Leizhi, Qingfang Hu, Yintang Wang, Yong Liu, Lingjie Li, and Tingting Cui. "Regional Calibration of Hargreaves Equation in the Xiliaohe Basin." Journal of Geoscience and Environment Protection 04, no. 07 (2016): 28–36. http://dx.doi.org/10.4236/gep.2016.47004.

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Jiang, Huanjun, Ahmed Salih Mohammed, Reza Andasht Kazeroon, and Payam Sarir. "Use of the Gene-Expression Programming Equation and FEM for the High-Strength CFST Columns." Applied Sciences 11, no. 21 (2021): 10468. http://dx.doi.org/10.3390/app112110468.

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The ultimate strength of composite columns is a significant factor for engineers and, therefore, finding a trustworthy and quick method to predict it with a good accuracy is very important. In the previous studies, the gene expression programming (GEP), as a new methodology, was trained and tested for a number of concrete-filled steel tube (CFST) samples and a GEP-based equation was proposed to estimate the ultimate bearing capacity of the CFST columns. In this study, however, the equation is considered to be validated for its results, and to ensure it is clearly capable of predicting the ultimate bearing capacity of the columns with high-strength concrete. Therefore, 32 samples with high-strength concrete were considered and they were modelled using the finite element method (FEM). The ultimate bearing capacity was obtained by FEM, and was compared with the results achieved from the GEP equation, and both were compared to the respective experimental results. It was evident from the results that the majority of values obtained from GEP were closer to the real experimental data than those obtained from FEM. This demonstrates the accuracy of the predictive equation obtained from GEP for these types of CFST column.

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Onen, Fevzi. "GEP PREDICTION OF SCOUR AROUND A SIDE WEIR IN CURVED CHANNEL." JOURNAL OF ENVIRONMENTAL ENGINEERING AND LANDSCAPE MANAGEMENT 22, no. 3 (2014): 161–70. http://dx.doi.org/10.3846/16486897.2013.865632.

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Side-weirs have been widely used in hydraulic and environmental engineering applications. Side-weir is known as a lateral intake structure, which are significant parts of the distribution channel in irrigation, land drainage, and urban sewerage system, by flow diversion device. Local scour involves the removal of material around piers, abutments, side-weir, spurs, and embankments. Clearwater scour depth based on five dimensional parameters: approach flow velocity (V1/Vc), water head ratio (h1–p)/h1, side-weir length (L/r), side-weir crest height (b/p) and angle of bend θ. The aim of this study is to develop a new formulation for prediction of clear-water scour of side-weir intersection along curved channel using Gene Expression Programming (GEP) which is an algorithm based on genetic algorithms (GA) and genetic programming (GP). In addition, the explicit formulations of the developed GEP models are presented. Also equations are obtained using multiple linear regressions (MLR) and multiple nonlinear regressions (MNRL). The performance of GEP is found more influential than multiple linear regression equation for predicting the clearwater scour depth at side-weir intersection along curved channel. Multiple nonlinear regression equation was quite close to GEP, which serve much simpler model with explicit formulation.

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Azaiez, Naima. "Improved Modelling of Soil Loss in El Badalah Basin: Comparing the Performance of the Universal Soil Loss Equation, Revised Universal Soil Loss Equation and Modified Universal Soil Loss Equation Models by Using the Magnetic and Gravimetric Prospection Outcomes." Journal of Geoscience and Environment Protection 09, no. 04 (2021): 50–73. http://dx.doi.org/10.4236/gep.2021.94005.

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Ashteyat, Ahmed, Yasmeen T. Obaidat, Yasmin Z. Murad, and Rami Haddad. "COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 26, no. 2 (2020): 189–99. http://dx.doi.org/10.3846/jcem.2020.11931.

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The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.

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., Hoang Nguyen, Nam Xuan Bui ., Hieu Quang Tran ., and Giang Huong Thi Le. "A novel soft computing model for predicting blast - induced ground vibration in open - pit mines using gene expression programming." Journal of Mining and Earth Sciences 61, no. 5 (2020): 107–16. http://dx.doi.org/10.46326/jmes.ktlt2020.09.

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The efforts of this study are to develop and propose a state - of - the - art model for predicting blast - induced ground vibration in open - pit mines with high accuracy anf ability based on the gene expression programming (GEP) technique. 25 blasts were conducted in the Tan Dong Hiep quarry mines with a total of 83 blasting events that were collected for this study. The GEP method was then applied to develop a non - linear equation for predicting blast - induced ground vibration based on a variety of influential parameters. A traditional empirical equation, namely Sadovski, was also applied to compare with the proposed GEP model. The results indicated that the GEP model can predict blast - induced ground vibration in open - pit mines better than the Sadovski model with an RMSE of 0.986 and R2 of 0.867. Meanwhile, the traditional empirical model (Sadovski) only provided an accuracy with an RMSE of 1.850 và R2 of 0.767.

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Hajihassani, Mohsen, Shahrum Shah Abdullah, Panagiotis G. Asteris, and Danial Jahed Armaghani. "A Gene Expression Programming Model for Predicting Tunnel Convergence." Applied Sciences 9, no. 21 (2019): 4650. http://dx.doi.org/10.3390/app9214650.

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Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson’s ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model’s output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner.

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Al-Aboodi, Ali H. "ESTIMATION OF MONTHLY MEAN REFERENCE EVAPOTRANSPIRATION USING GENE EXPRESSION PROGRAMMING." Kufa Journal of Engineering 8, no. 1 (2017): 37–50. http://dx.doi.org/10.30572/2018/kje/811189.

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Evapotranspiration is a main component of the water cycle and is important in crop growth. Monthly mean reference evapotranspiration (ETo) is estimated using gene expression programming (GEP) in Basrah City, south of Iraq. Various climatic data, such as air temperature, relative humidity, and wind speed are used as inputs of GEP model to estimate the values of reference evapotranspiration (ETo) given by the FAO-56 (Penman-Monteith equation). Nine input combinations tested with GEP are coded as model No. (1-9). Root relative squared error (RRSE) is taken as fitness function in each of GEP models. GEP models with three climatic input variables (temperature, relative humidity, and wind speed) take the highest level in the performance. The GEP technique was successfully employed to estimate ETo in the study area. The explicit formulas obtained can be used as powerful models for estimating the mean monthly ETo in the irrigation practices with limited climatic data.

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Mutakela, Patrick S., Joyce P. Lepetu, Gofaone Rammotokara, Melusi Rampart, and Sarah Assem Ibrahim. "Biomass Prediction Equation for <i>Colophospermum mopane</i> (Mopane) in Botswana." Journal of Geoscience and Environment Protection 11, no. 06 (2023): 1–22. http://dx.doi.org/10.4236/gep.2023.116001.

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