Application Of Gene Expression Programming Research Articles

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) of reinforced soil foundations (RSFs) based on settlement criteria, addressing a critical gap in sustainable construction practices. Based on the principles of probability and statistics, the soil uncertainties were mapped using the first-order second-moment (FOSM) approach. The historical data generated via a parametric study on a validated finite element numerical model were used to train and validate the GEP models. Among the ten developed GEP frameworks, the best-performing model, abbreviated as GEP-M9 (R2 = 0.961 and RMSE = 0.049), in the testing phase was used to perform the RA of an RSF. This model’s effectiveness in RA was affirmed through a comprehensive evaluation, including parametric sensitivity analysis and validation against two independent case studies. The reliability index (β) and probability of failure (Pf) were determined across various coefficient of variation (COV) configurations, underscoring the model’s potential in civil engineering risk analysis. The newly developed GEP model has shown considerable potential for analyzing civil engineering construction risk, as shown by the experimental results of varying settlement values.

Application of Gene Expression Programming in Computation of Flow Resistance in Compound Channel with Converging Floodplains

Application of Gene Expression Programming in Computation of Flow Resistance in Compound Channel with Converging Floodplains

Application of gene expression programming in developing correlations for nuclear desalination economic analysis

Application of gene expression programming in developing correlations for nuclear desalination economic analysis

Application of gene expression programming for seasonal rainfall forecasting in Western Australia using potential climate indices

AbstractThis study presents the development of rainfall forecast models using potential climate indices for the Kimberley region of Western Australia, using 100 years of rainfall and climate indices data for four rainfall stations. Three different modeling techniques: multiple linear regression (MLR), autoregressive moving average with exogenous input (ARIMAX), and gene-expression programming (GEP) were applied to develop prediction models. Preliminary analysis suggests that Western Tropical Indian Ocean (WTIO) and Southern Oscillation Index (SOI) have significant impacts on summer rainfall generation for the region. Developed models’ performances were evaluated using Pearson correlation coefficient ($$r$$ r ), root mean square error ($$RMSE$$ RMSE ), mean absolute error $$(MAE)$$ ( M A E ) , Nash–Sutcliffe efficiency $$(NSE)$$ ( N S E ) , and refined Willmot index of agreement ($${d}_{r}$$ d r ). It is found that the GEP model exclusively outperforms the other two alternatives. In the calibration period, the GEP model resulted in a Pearson correlation coefficient (r) values ranging from 0.76 to 0.85, which are significantly higher than that achieved from MLR (0.32 to 0.44) and ARIMAX (0.53 to 0.83) models, while for the validation period, the correlation values for the models ranged from 0.74 to 0.87 for GEP, 0.35 to 0.51 for MLR and 0.59 to 0.77 for ARIMAX models. Considering other statistical error statistics it can be concluded that the GEP model is the best representative seasonal rainfall forecasting model for the region.

Application of gene expression programming to predict the compressive strength of quaternary-blended concrete

Application of gene expression programming to predict the compressive strength of quaternary-blended concrete

Forecasting Electricity Power Generation of Pawan Danavi Wind Farm, Sri Lanka, Using Gene Expression Programming

This paper presents the development of a wind power forecasting model based on gene expression programming (GEP) for one of the major wind farms in Sri Lanka, Pawan Danavi. With the ever-increasing demand for renewable power generation, Sri Lanka has started harnessing electricity from wind power. Though the initial establishment cost of wind farms is high, the analyses clearly showcased the economic sustainability of wind power generation in long term. In this context, forecasting the wind power generation at Sri Lankan wind farms is important in many ways. However, limited research has been carried out in Sri Lanka to predict the wind power generation against the changing climate. Therefore, to overcome this research gap, a model was developed to forecast wind power generation against two climatic factors, viz. on-site wind speed and ambient temperature. The results showcased the robustness and accuracy of the proposed GEP-based forecasting model (with R2 = 0.92, index of agreement = 0.98, and RMSE = 259 kW). Moreover, the results of the study were compared against three different forecasting models and found comparable in terms of the model accuracy. The GEP-based model is advantageous over machine learning techniques due to its capability in deriving a mathematical expression. As an acceptable relationship was found between wind power generation and climatic factors, the proposed model facilitates the future projection of wind power generations with forecasted climatic factors. Though the application of GEP in the field of wind power generation is reported in a few research publications, this is the first research in which GEP is employed to model the power generation with respect to weather indices. The proposed prediction model is advantageous than machine learning models as the relationship between the wind power and the weather indices can be expressed.

Machine Learning for the Development of Data-Driven Turbulence Closures in Coolant Systems

Abstract This work shows the application of Gene Expression Programming to augment RANS turbulence closures for flows though complex geometries. Specifically, an optimized internal cooling channel of a turbine blade, designed for additive manufacturing. One of the challenges in internal cooling design is the heat transfer accuracy of the RANS formulation in comparison to higher fidelity methods, which are still not used in design on account of their computational cost. However, high fidelity data can be extremely valuable for improving current lower fidelity models and this work shows the application of data-driven approaches to develop turbulence closures for an internally ribbed duct. Different approaches are compared and the results of the improved model are illustrated; first on the same geometry, and then for an unseen predictive case. The work shows the potential of using data-driven models for accurate heat transfer predictions even in non-conventional configurations and indicates the ability of closures learnt from complex flow cases to adapt successfully to unseen test cases.

Application of Gene Expression Programming for Performance Analysis of a Regenerative Organic Rankine Cycle with Low-Temperature Heat Source

Application of Gene Expression Programming for Performance Analysis of a Regenerative Organic Rankine Cycle with Low-Temperature Heat Source

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s π theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to R2 = 92.85% and MSE = 1.38 × 10−3 and R2 = 91.93% and MSE = 4.30 × 10−3 are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in RF in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the RF of each cycle. It is found that oil to gas viscosity ratio has a higher impact on RF value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.

Application of Gene Expression Programming to Evaluate Strength Characteristics of Hydrated-Lime-Activated Rice Husk Ash-Treated Expansive Soil

Gene expression programming has been applied in this work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R value or Rvalue) of expansive soil treated with an improved composites of rice husk ash. Pavement foundations suffer failures due to poor design and construction, poor materials handling and utilization, and management lapses. The evolution of sustainable green materials and optimization and soft computing techniques have been deployed to improve on the deficiencies being suffered in the abovementioned areas of design and construction engineering. In this work, expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in an incremental proportion to produce 121 datasets, which were used to predict the behavior of the soil’s strength parameters utilizing the mutative and evolutionary algorithms of GEP. The input parameters were HARHA, liquid limit ( w L ), (plastic limit w P , plasticity index I P , optimum moisture content ( w OMC ), clay activity (AC), and (maximum dry density (δmax) while CBR, UCS, and R value were the output parameters. A multiple linear regression (MLR) was also conducted on the datasets in addition to GEP to serve as a check mechanism. At the end of the computing and iterations, MLR and GEP optimization methods proposed three equations corresponding to the output parameters of the work. The responses validation on the predicted models shows a good correlation above 0.9 and a great performance index. The predicted models’ performance has shown that GEP soft computing has predicted models that can be used in the design of CBR, UCS, and R value for soils being used as foundation materials and being treated with admixtures as a binding component.

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete.

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

Blast-induced ground vibration prediction in granite quarries: An application of gene expression programming, ANFIS, and sine cosine algorithm optimized ANN

Blasting of rocks has intrinsic environmental impacts such as ground vibration, which can interfere with the safety of lives and property. Hence, accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the literature. Therefore, there is need to consider some robust predictive models for accurate prediction results. Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neural network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries. The input parameters into the models are the distance from the point of blasting to the point of measurement (D), the weight of charge per delay (W), rock density (ρ), and the Schmidt rebound hardness (SRH) value while peak particle velocity (PPV) is the targeted output. 100 datasets were used in developing the proposed models. The performance of the proposed models was examined using the coefficient of determination (R2) and error analysis. The R2 values obtained for the GEP, ANFIS, and SCA-ANN models are 0.989, 0.997, and 0.999, respectively, while their errors are close to zero. The proposed models are compared with an empirical model and are found to outperform the empirical model.

Application of gene expression programming for predicting density of binary and ternary mixtures of ionic liquids and molecular solvents

Ionic Liquids (ILs) have received increased attention across a number of disciplines in recent years. This noticeable importance of ILs is attributed to their attractive proprieties. Precise evaluation of the thermophysical properties of ionic liquids and their mixtures with molecular solvents is essential for distinct multidisciplinary applications. In this study, a rigorous white-box intelligent technique, viz. gene expression programming (GEP) was implemented for establishing new correlations for accurate prediction of density of binary and ternary mixtures of ILs and molecular solvents. The newly suggested correlations were developed using a comprehensive experimental database with 1985 real measurements under a variety of operational conditions. The obtained results revealed that the newly established GEP-based correlations can predict the density of binary and ternary mixtures of ILs and molecular solvents with a high degree of integrity. The GEP-based correlations exhibited overall average absolute relative error (AARE) values of 0.5621% and 0.2128% for binary and ternary cases, respectively. Besides, it was found that our proposed explicit correlations followed the expected tendency with respect to the considered variables. Furthermore, the superiority and the reliability of the GEP-based correlations was testified against the best-existing approaches in the literature. Finally, the leverage approach was performed and the statistical validity of the correlations and the experimental data was testified.

Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV, respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

Retraction Note to: Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer

Retraction Note to: Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer

A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming

Regarding the high corrosion resistance of brass in sulfuric acid, its leaching process is the most important step in hydrometallurgical recovery of brass scraps. In this study, the electrochemical dissolution of brass chips in sulfuric acid has been investigated. The electrochemical cell voltage depends on various parameters. Regarding the complexity of electrochemical dissolution, the system voltage could not be easily predicted based on the operational parameters of the cell. So, it is necessary to use modeling techniques to predict cell voltage. In this study, 139 leaching experiments were conducted under different conditions. Using the experimental results and gene expression programming (GEP), parameters such as acid concentration, current density, temperature and anode-cathode distance were entered as the inputs and the voltage of the electrochemical dissolution was predicted as the output. The results showed that GEP-based model was capable of predicting the voltage of electrochemical dissolution of brass alloy with correlation coefficient of 0.929 and root square mean error (RSME) of 0.052. Based on the sensitivity analysis on the input and output parameters, acid concentration and anode-cathode distance were the most and least effective parameters, respectively. The modeling results confirmed that the proposed model is a powerful tool in designing a mathematical equation between the parameters of electrochemical dissolution and the voltage induced by variation of these parameters.

Comparison of the gene expression programming, artificial neural network (ANN), and equivalent Muskingum inflow models in the flood routing of multiple branched rivers

Floods are considered as natural hazards. The parameter estimation of hydrologic methods is time-consuming in the flood routing of multiple inflows river systems. This paper presents the application of gene expression programming (GEP) and artificial neural network (ANN) as alternative approaches to predict the outflow hydrograph in downstream of multiple inflows systems. GEP and ANN models were compared with the equivalent Muskingum inflow model. The Gharesoo River Basin as a multiple inflows river system was applied for the calibration and verification phases of these models. The GEP obtained the formula as a function of the inflow branches based on the fitting data for simulating the outflow hydrograph of the multiple inflows system. GEP and ANN models investigated inflow hydrographs at different time steps. The obtained outflow hydrograph by the GEP model indicated an excellent performance compared with ANN and equivalent Muskingum inflow models in the case involving multiple inflows system.

Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location

Gastric cancer (GC) is the third reason for cancer-related deaths in the world. The late referral of patients to medical centers in an advanced stage can make the treatment procedure more difficult. Accurate diagnosis of risk factors in GC tumor size and tumor location can lead to taking preventive measures or determining a suitable treatment strategy. This study aims to present a general model to identify the correlation of different parameters in a GC tumor place and tumor size. The medical documents of GC patients consist of the dataset of this study. The effect of seven main parameters, namely age, smoking, Helicobacter pylori (H. pylori) infection, job, surgical background, sex, and nodal stage is investigated in GC tumor location and tumor size. By considering all the medical documents, data modeling is conducted using gene expression programming because of the high precision of model output. In the following, three different sensitivity analysis methods (Morris, Distributed Evaluation of Local Sensitivity Analysis (DELSA), and Sobol’–Jansen) are applied to determine the influential factors in the tumor size and location. Results show that in sequence, sex, age, and H. pylori records mostly affect tumor location; the nodal stage, smoking, and surgery record mostly affect tumor size. This method can help in identifying effective parameters and prevention of patients’ death in all types of diseases, even for terminal illnesses.

Application of Gene Expression Programming and Support Vector Regression models to Modeling and Prediction Monthly precipitation

Application of Gene Expression Programming and Support Vector Regression models to Modeling and Prediction Monthly precipitation

Application of gene expression programming in hot metal forming for intelligent manufacturing

Design of the die in hot metal forming operations depends on the required forming load. There are several approaches in the literature for load prediction. Artificial neural networks (ANNs) have been successfully used by a few researches to estimate the forming loads. This paper aims at using the effectiveness of a new evolutionary approach called gene expression programming (GEP) for the estimation of forging load in hot upsetting and hot extrusion processes. Several parameters such as angle (α), L/D ratio (R), friction coefficient (µ), velocity (v) and temperature (T) were used as input parameters. The accuracy of the developed GEP models was also compared with ANN models. This comparison was evidenced by some statistical measurements (R2, RMSE, MAE). The outcomes of the study showed that GEP can be used as an effective tool for representing the complex relationship between the input and output parameters of hot metal forming processes.