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Assessing interlayer fatigue performance is crucial for the durability and structural integrity of asphalt pavements. This study introduces supervised machine learning techniques to estimate interface fatigue life (IFL) and interface shear stiffness (ISS) between asphalt layers. Using 84 lab-prepared specimens tested under varying conditions-temperature, normal pressure, loading frequency and shear stress, with a constant tack coat type and application rate-predictive models were trained and validated. The influence of each factor on fatigue indices was evaluated before employing genetic expression programming (GEP) and artificial neural networks (ANNs) for prediction. Model efficacy was quantified using statistical metrics, highlighting the robustness of ANN models (R² = 0.980, RMSE = 0.826, MAE = 0.636 for ISS and R² = 0.972, RMSE = 0.498, MAE = 0.383 for IFL) over GEP models (R² = 0.921, RMSE = 1.402, MAE = 1.079 for ISS and R² = 0.913, RMSE = 0.623, MAE = 0.479 for IFL). Sensitivity analysis confirmed alignment with experimental data, identifying temperature as the most critical factor and frequency as the least influential on interface fatigue performance. These findings could lead to more reliable and efficient designs of interlayer bonding in asphalt pavement layers. [ABSTRACT FROM AUTHOR]

Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.

This study presents a hybrid data-driven machine learning framework utilizing Bayesian regularization-based artificial neural network (ANN) and genetic expression programming (GEP) to develop a robust prestressing loss mathematical model. The proposed framework utilizes a surveyed database of experimentally measured losses of 113 prestressed concrete girders. In addition, this study utilizes the experimental database to evaluate the prediction accuracy of the adopted procedures in design provisions (PCI and AASHTO LRFD). The study shows that the currently adopted procedures result in either inconsistent accuracy or an overestimation of the prestressed losses. On the other hand, the proposed model demonstrated higher accuracy and consistent prediction with respect to all variables. The proposed model resulted in an average predicted-to-measured ratio of 1.03 and a standard deviation of 0.20. The study provides a parametric study to quantify the contribution of each of the included variables that revealed that girder height h, area of prestressing reinforcement Aps, and moment due to dead load Mg are the most influential parameters in the estimation. Furthermore, a statistical evaluation of the different prestress loss estimation methods utilizing Bayesian parameter estimation demonstrated the robustness of the proposed method compared to the existing approaches. [ABSTRACT FROM AUTHOR]

This study focuses on the blade design and optimization of Dual Darrieus Wind turbines (DDWTs). Based on Genetic Expression Programming (GEP) model, a power prediction formula C P = f (Δ , β , θ) , containing radius difference Δ , chord ratio β , and offset angle θ , is proposed in this study. Hence, the power coefficient of various blade layouts can be directly calculated, corresponding to their optimum TSR. Meanwhile, orthogonal array L 9 (34) and Modified Additive Model (MAM) are combined to ensure the representative sample and consider the interaction effects. Additionally, each configuration in dataset requires to execute Computational Fluid Dynamics (CFD) analyses under various Tip Speed Ratios (TSRs) to figure out the highest power coefficient. The optimum layout of double-layer blades, derived from Taguchi Method and C P = f (Δ , β , θ) , features the parameter combination as radius difference Δ of 0.25 m and chord ratio β of 3, which can improve the power output by 7.5%. The power prediction formula established by GEP is competent to approximate C P value of double-layer blades precisely which is verified by the correlation index R2 = 0.993 and supplementary CFD cases. The proposed methodology indicates that DDWTs deserve to be further researched and developed. [ABSTRACT FROM AUTHOR]

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is50) and P-wave velocity (Vp) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PIat was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main objective of this research was to evaluate the possible impact of climate change on groundwater levels in the Tasuj Plain, Iran. To accomplish this, the values of precipitation for a near future period was projected through four general circulation models (GCM). Then, the groundwater level variations through the genetic expression programming (GEP) model were evaluated. The projection results indicated that the average annual precipitation with 33.55 mm in the base period would decline to 20.51, 20.11, and 19.14 mm under three representative concentration pathway (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP8.5, respectively. The values of the determination coefficient range from 0.92 to 0.99; the root mean square error between 0.12 and 0.61, and mean absolute error from 0.08 to 0.54 showed that in the forecasting of groundwater level through the GEP model, all models have acceptable results. The evaluation of groundwater level simulation demonstrated that the developed model through the precipitation and previous groundwater level performs better. Prediction of groundwater level for a future period based on climate change scenarios indicated that the average groundwater level in Tasuj Plain at the beginning of the period would experience a gradual reduction and would then increase very slightly to the end of the period. Overall, it was found that the declining precipitation under climate change has no significant impacts on the groundwater level in the Tasuj Plain, and other parameters like anthropogenic activities could be the primary reason for groundwater depletion. [ABSTRACT FROM AUTHOR]

Abstract: In the present investigation, gene expression programming (GEP) is used to predict thermal conductivity of nanofluids consisting of Al2O3 and CuO nanoparticles suspended in water. The obtained new model is a function of temperature, volume fraction, and diameter of the nanoparticles. To predict the thermal conductivity, experimental data from literatures were partitioned into two sets: a train (800 numbers) and a test (200 numbers) data sets. The model was designed by the train set, and the results were compared with the test data set. The estimated heat conductivity was compared with experimental data and several most cited relations in the literature. Moreover, the new thermal conductivity model was tested in the simulation of benchmark case study of nanofluid free convection inside the square cavity. The predicted Nusselt number was compared with available experimental data at five Rayleigh numbers. The findings illustrated that the GEP can estimate and model the thermal conductivity of nanofluid very efficiently, and it can be used successfully for simulation of engineering problems.Graphical Abstract: [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Effort to predict the mechanical properties of cement mortar containing nano silica and micro silica. • 640 data collected from literature to evaluate the existing model in prediction. • New ANN and GEP were conducted to predict the mechanical properties of cement mortar. • Considering porosity as input and normalized input parameters led to more accurate prediction. Abstract Artificial neural networks (ANNs) and genetic expression programming (GEP) have recently been used to model the properties of cement mortar containing nano silica (NS) or micro silica (MS) to minimize the experimental work. Appropriate ANN and GEP models were proposed in this paper to predict the simultaneous effect of NS and MS on cement mortar properties because available models are not suitable to predict simultaneous effect of NS and MS. For this purpose, ANN and GEP models were trained on 640 different mixture proportions considered from literature and the prediction results were compared to available models. Moreover, in order to validate the proposed ANN and GEP models, a total of 480 compressive (50 × 50 × 50 mm) as well as 96 flexural (40 × 40 × 160 mm) specimens were constructed while considering cement strength class of 42.5 MPa and also, mechanical properties were tested. The higher accuracy results observed when in proposed models for predicting of compressive strength of cement mortar used normalized input parameters. Moreover, flexural strength of cement mortar estimated by the new ANN and GEP models is validated by experimental effort and the results show high accuracy compared to previous models. Finally, a comparative evaluation was also performed on two conditions of with and without considering porosity as an input parameter in the new suggested ANN and GEP models. [ABSTRACT FROM AUTHOR]

This study predicts the mechanical properties of cement mortars. It focuses specifically on cement strength classes of 32.5, 42.5, and 52.5 MPa, respectively. To this end, a total of 270 specimens using 54 mix designs were constructed and subjected to freezing and thawing cycles (FTCs) at −18 and 18 °C. Then, mechanical properties such as compressive and flexural strength and porosity of the specimens after 0, 50, 100, 150, and 200 cycles were obtained. Afterward, genetic expression programming (GEP) was used to predict the obtained results and to represent nonlinear relationships between the mechanical properties, freezing and thawing cycle, and cement strength class. The experimental data was used for training and testing of two GEP approach models. The results show that the strength of mortar reduces with increase in FTC. On other hand, a close correlation was observed between the predicted and experimental values when cement strength class and number of cycle were considered as independent input parameters. This fact indicates the significance of the aforementioned parameters along with the other parameters such as water to cement ratio, sand to cement ratio, and weight of cement on the accuracy of the prediction. [ABSTRACT FROM AUTHOR]

Bu çalışmada AZ91 magnezyum alaşımının farklı parametreler altında işlenmesi ile oluşan kesme kuvvetlerinin deneysel tabanlı teorik bir model ile tahmin edilmesi sunulmuştur. Modelleme için gerekli deneyler kuru işleme ortamında ve işleme devri ilerleme hızı ve 4 farklı matkap ucunun tam faktöriyel deney tasarımı kullanarak gerçekleştirilmiştir. Deneyler sonucunca elde edilen veriler Genetic Expression yazılımı ile modellenerek kesme kuvveti tahmini için formulasyon oluşturulmuştur. Bu formulasyon kullanılarak deneyde kullanılan parametrelerin kesme kuvveti üzerindeki etkileri detaylı olarak analiz edilmiştir

This paper is presented a new theoretical model basis on experimental to predict the thrust force on AZ91magnesium alloy in drilling process depend on the various machining parameters. The experiments for modeling were conducted in dry cutting conditions and designed as full factorial using the spindle speed and feed rate with four different kinds of drill bits. The results were modeled with Genetic Expression Programming and the thrust force formulation was obtained. Considering the formulation, the factors effects were analyzed on thrust force for AZ91.

The accurate estimation of soil dispersivity ( α) is required for characterizing the transport of contaminants in soil. The in situ measurement of α is costly and time-consuming. Hence, in this study, three soft computing methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and gene expression programming (GEP), are used to estimate α from more readily measurable physical soil variables, including travel distance from source of pollutant ( L), mean grain size ( D ), soil bulk density ( ρ ), and contaminant velocity ( V ). Based on three statistical metrics [i.e., mean absolute error, root-mean-square error (RMSE), and coefficient of determination ( R )], it is found that all approaches (ANN, ANFIS, and GEP) can accurately estimate α. Results also show that the ANN model (with RMSE = 0.00050 m and R = 0.977) performs better than the ANFIS model (with RMSE = 0.00062 m and R = 0.956), and the estimates from GEP are almost as accurate as those from ANFIS. The performance of ANN, ANFIS, and GEP models is also compared with the traditional multiple linear regression (MLR) method. The comparison indicates that all of the soft computing methods outperform the MLR model. Finally, the sensitivity analysis shows that the travel distance from source of pollution ( L) and bulk density ( ρ ) have, respectively, the most and the least effect on the soil dispersivity. [ABSTRACT FROM AUTHOR]

Highly nonlinear flow behavior of cement-based grout mixtures has always become an important issue for experimenters during jet grouting applications. In this viewpoint, an investigation has been addressed in this paper on the applicability of a recent soft computing prediction tool, genetic expression programming (GEP), to the prediction of rheological characteristics (i.e., shear stress, viscosity) of the grout mixtures with various stabilizers (clay, sand, lime) for jet grouting purposes. The experimental data (shear stress versus shear rate with respect to stabilizer dosages) of grout mixtures obtained from rheometer tests have been collected from previous study conducted in a wide range of stabilizer dosage rates (0-100 %, by dry weight of binder). For predicting the shear stress and viscosity as the output variables during the train and testing stages, the input variables in the GEP models included shear rate and stabilizer dosage primarily. As a consequence of GEP modeling compared with measured data, this study reveals satisfactory GEP formulations in a good accuracy ( R ≥ 0.86) for predictions of shear stress and viscosity regarding the stabilizer additions. The GEP formulas are also found adequate for modeling the flow behavior of the shear stress-shear rate, alternatively to conventional nonlinear regression and rheological models (Herschel-Bulkley, Robertson-Stiff). For assistance of preliminary evaluations, the derived GEP formulas could be potentially considered in practice for estimations of pumping pressure (shear stress), pumping rate (shear rate) and viscosity of jet grout mixtures. [ABSTRACT FROM AUTHOR]

Sediment transport is a complex phenomenon due to the nonlinearity and uncertainties of the process. The present study applies Gene Expression Programming (GEP) to develop bedload transport models in sewer pipes. In this regard, two types of bedload were considered: loose bed (deposition state) and rigid bed (limit of deposition state). In order to develop the models, two scenarios with different input combinations were considered: Scenario 1 considers only hydraulic characteristics and Scenario 2 considers both hydraulic and sediment characteristics as inputs for modeling bedload discharge. The results proved the capability of GEP in prediction of sediment transport and it was found that for prediction of bedload transport in sewer pipes Scenario 2 performed more successfully than Scenario 1. According to the outcome of sensitivity analysis, F rm (Modified Froude number) and d 50 /y (relative particle size) for rigid boundary and F rm for loose boundary had key roles in the modeling. The outcome of the GEP models also was compared with existing empirical equations and it was found the GEP models yielded better results. It was also found that pipe diameter affected the transport capacity of the sewer pipe. Increasing pipe diameter caused an increase in model efficiency. A pipe with a diameter of 305 mm yielded to the best results. [ABSTRACT FROM AUTHOR]

• Compile experimental test results on circular concrete-filled tube members under shear loading. • Shed light on the current Artificial Intelligence approach and its application in structural engineering. • Developing AI-based models/equations to predict the shear strength of circular concrete-filled tube members. The complex shear behavior of circular concrete-filled tube (CCFT) members has been a challenge for an adequate design equation. Collapses due to shear failure are primarily seen in shear links, pile foundations, and coupling beams in composite shear walls. The current design provisions are based on limited experimental data, leading to very conservative expressions of shear strength. The recent advances in Artificial Intelligence (AI) technologies provided an opportunity to establish design models directly from the data with no need to postulate a mathematical expression. This study utilized three AI techniques alongside 141 experimental test results from the literature to overcome the complex behavior of the CCFT members by proposing reliable design equations/models. Namely, Gaussian Processing Regression (GPR), Gene Expression Programming (GEP) and Nonlinear Regression (NR) analysis. The predictor variables include axial loading, materials properties, section slenderness ratio and shear span ratio. This paper sheds light on the current data-based techniques in solving complex structural problems by addressing the noted AI methods and their application in predicting the shear capacity of CCFT members. It is concluded that the data-driven proposed model demonstrates remarkable accuracy in predicting shear capacity compared to the current design equations and can be used for routine design practice. The statistical validation results show that among the proposed methods, GPR showed the highest efficiency in predicting the shear capacity of CCFT with an average error of 0.5%, whereas for GEP and NR, average errors are 1.26% and 1.09%, respectively. [ABSTRACT FROM AUTHOR]

This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R 2 ), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model. [ABSTRACT FROM AUTHOR]

Existing impulse noise reduction techniques perform well at low noise densities; however, their performance drops sharply at higher noise densities. In this paper, we propose a two-stage scheme to surmount this problem. In the proposed approach, first stage consists of impulse detection unit followed by the filtering operation in the second stage. We have employed a genetic expression programming-based classifier for the detection of impulse noise-corrupted pixels. To reduce the blurring effect caused due to filtering operation on the noise-free pixels, we filter the detected noisy pixels only by using a modified median filter. Better peak signal-to-noise ratio, structural similarity index measure, and visual output imply the efficacy of the proposed scheme for noise reduction at higher noise densities. [ABSTRACT FROM AUTHOR]

This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction of Angle of Shearing Resistance(ϕ) of soil. MARS is an adaptive, non-parametric regression approach. Percentages of fine-grained (FG), coarse-grained (CG), liquid limit (LL), and bulk density (BD) have been used as input variables of MARS. The developed MARS gives an equation for prediction ofϕof soil. The results of MARS have been compared with Genetic Expression Programming (GEP), Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) models. These results demonstrate that the developed MARS can be used as a robust model for determination ofϕof soil. [ABSTRACT FROM PUBLISHER]

Summary The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol + kg −1 and correlation coefficient ( R 2 ) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol + kg −1 and R 2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively. [ABSTRACT FROM AUTHOR]

Improving river flow forecasts for longer lead times by incorporating numerical weather predictions (NWP) into streamflow forecasting systems has attracted hydrologists in recent years. The process turns considerably complex and resource hungry when ensembles of NWP forecasts instead of any single NWP output are used to feed the flow forecasting models in order to capture the uncertainties in hydrological forecasting. This paper presents, for the first time, a comparison of three statistical stratification techniques for simplifying the input precipitation ensemble forecasts driving a river flow forecasting system. A data-driven flow forecasting model developed for the Waikato River in New Zealand using genetic expression programming (GEP) was forced by the 10-days-ahead ensemble precipitation forecasts issued by three meteorological .centers in different parts of the world including the United Kingdom, Canada, and China. The three precipitation ensembles, comprising 51, 21, and 15 members respectively, were reduced to a smaller ensemble consisting of only 5 members by static, dynamic, and cluster stratification. The 10-days-ahead river flow forecasts resulting from the full and reduced precipitation ensembles were compared against the corresponding flows observed during the whole year 2012. The three stratification mechanisms vary in complexity, and two of them are novel in their application to streamflow forecasting. The forecasts were compared for four different attributes including accuracy, reliability, resolution skill, and the maximum flow limit, each targeting a different aspect of the forecast performance. The results indicate that, in general, the flow forecasts driven by all three smaller ensembles were comparable with their full counterpart for all three forecasting centers. The cluster-stratified approach outperformed all others for most of the tested forecast attributes. The performance of static-stratified ensembles was not too far from the cluster-stratified ensembles despite it being significantly simple compared to the latter. The dynamic-stratified ensembles were least in competing with the full ensembles except for the maximum flow limit. [ABSTRACT FROM AUTHOR]

In order to understand the treatment of a marginal soil well, the underlying input-output relationship on the strength and elastic responses due to nonlinearity has always been a great importance in the stabilized mixtures for an optimal design. This paper employs a relatively new soft computing approach, genetic expression programming (GEP), to formulations for unconfined compressive strength (UCS) and elasticity modulus (Es) of clay stabilized with bottom ash, using a database obtained from the laboratory tests conducted in the study. The predictor variables included in the formulations are bottom ash dosage, dry unit weight, relative compaction, brittleness index and energy absorption capacity. The results demonstrate that the GEP-based formulas of UCS and Es are significantly able to predict the measured values to high degree of accuracy against the nonlinear behavior of soil (p<0.05, R>0.847). The GEP approach is found to have a better correlation performance as compared with the nonlinear regression as well. The sensitivity analysis for the parameter importance shows that the dominant influence on the predictions of UCS and Es is exerted by the variables of bottom ash dosage and energy absorption capacity. This study reveals that the GEP is a potential tool for establishing the functions and identifying the key variables for predicting the strength and elastic responses of the clay treated with bottom ash. Including a waste material in the proposed formulas can also serve to the environment for the development of recycling and sustainability. [ABSTRACT FROM AUTHOR]

The primary purpose of a weir is to measure discharge. Moreover, it can be used as a tool to increase aeration efficiency in rivers, fish hatcheries, and water treatment plants. A free overfall jet from a weir plunging into downstream water causes entrainment of the air bubbles if the free overfall jet velocity exceeds a certain critical value and hence aeration occurs. In recent years, different soft computing systems have been successfully employed for the solution of complex problems. The aim of this study is to develop models to estimate air entrainment and aeration efficiencies of weirs using soft computing systems. For this aim, genetic expression programming, a recently developed artificial intelligence technique, is used. Genetic Expression Programming (GEP) is a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators. GEP is preferred since it generates a mathematical function which fits to given experimental data. The results indicate that there are good agreements between the measured values and the values obtained by the genetic expression programming models. These good agreements confirm the validity of the developed genetic expression programming models. [ABSTRACT FROM AUTHOR]

Prediction of acid gases solubilities in ionic liquids (ILs), have recently emerged as promising mediums for refining of natural gas, using powerful paradigms is of great importance from technical and economical point of view. In this respect, this study aims at appraising the effectiveness of one of the new generation soft computing methodologies called gene-expression programming (GEP) for estimating the hydrogen sulfide (H2S) solubility in ionic liquids (ILs). A total data set of 465 experimental data belonging to 11 ionic liquids, which gathered from literatures, were used to develop a general correlation. The temperature and pressure accompanied with acentric factors and critical temperature and pressure of ILs were used as independent input variables, while H2S solubility as dependent output variables. The modeling results showed the coefficient of determination (R2) of 0.9902 and 0.0438% mean absolute relative error (MARE) for the predicted solubilities from the corresponding experimental values. Therefore, the model is comprehensive and accurate enough to be used to predict the H2S solubility in various ILs. In addition, the GEP-model predictions were compared with the outputs of two well-known engineering approaches named Soave-Redlich-Kwong (SRK) and Peng-Robinson (PR). Results showed that the proposed evolutionary-based method was more accurate than the widely used aforementioned thermodynamic models. [ABSTRACT FROM AUTHOR]

In this study, Al 6061 based metal matrix composites reinforced with Al2O3 particles were produced with vortex method by using reinforcing agent in three different weight ratio (10%, 15% and 20%) and two different particle sizes (32 and 66 μm). produced cylindrical specimens were machined on CNC lathe with various cutting speeds and feed rates by using cementite carbide cutting tips and resultant surface roughness values were determined. Measured surface roughness values were used to develop a mathematical model by utilizing genetic programming, which is one of the genetic algorithm based methods, for estimation of surface roughness with respect to material properties and cutting parameters. Predicted results by using developed mathematical model were compared with experimental results. It is observed that predictions of the model is robust enough. [ABSTRACT FROM AUTHOR]

• The most important parameter in tool wear was computed the cutting speed. • In tool wear, the cutting speed was followed by feed rate, cutting length and depth of cut, respectively. • Tool wear could be calculated by both measuring and formulating. • Mathematical expression was generated by genetic expression programming. • The tool wear equations obtained depending on the cutting parameters were shown with different mathematical operations. Titanium alloys are materials difficult to machine due to their excellent characteristics. In the machining process of these alloy materials, it is critical to determine the important coefficient cutting parameters such as depth of cut (a p), cutting speed (V c), feed rate (f r), and cutting length (L c) on tool wear (VB), and to simplify them by creating a mathematical expression. The effects of cutting parameters on VB were explored experimentally, in this study, by turning the Ti-6Al-4V alloy at various cutting parameters. Multiple linear regression (MLR) and Genetic Expression Programming (GEP) methods were used to examine the acquired data. The mathematical expression derived from GEP was simplified. Increasing the cutting parameter values resulted in an increase in VB. The analyses revealed that the parameter of V c had a considerable impact on VB. The best performance (R2) from MLR analyzes was determined as 0.705. This performance was computed as 0.896 in stepwise MLR (SMLR) and 0.96 in GEP. The performance value was found to be 0.911 by simplifying the mathematical expression derived from GEP. It was observed that the analysis of parameter values of a p , V c , f r and L c on estimation performance of VB with the proposed GEP method was appropriate. [ABSTRACT FROM AUTHOR]

Recorded time series of relative humidity (RH) are modeled by using genetic expression programming (GEP) and artificial neural networks (ANNs) models. The data are noisy and contain missing datapoints. RH is modeled as a function of three meteorological variables: temperature, wind speed, and pressure. Various model structures of both of these models are investigated with the aim of testing the robustness of the predicted values in the presence of noise and missing data. Due to the presence of noise, a sophisticated treatment of missing data was not justifiable, and therefore, the strategy adopted was just to carry the datapoints backward, although this may induce bias in the time dimension and contaminate the predicted results. The results of this study indicate that through a careful selection of model structures both GEP and ANN can produce adequately reliable prediction of RH values 1 year into the future. The paper provides evidence that this model structure is feasible when the dependent variables include both the present and past values. [ABSTRACT FROM AUTHOR]

Since the determination from experimental tests are expensive and time consuming, the site conditions in strong ground motion equations are mostly expressed by geologically qualitative descriptions of soils at the recording stations. The analytical solution for the site description has not been sufficiently studied due to highly nonlinear behavior of soil. Advances in field of artificial intelligence (AI) offer new insights to solve the problems in the most complex systems utilizing different algorithms and models. This paper primarily aims to predict average shear wave velocity ( $$\text{ V}_\mathrm{S30}$$) as a soil property at the earthquake recording stations by applying AI methods, which are composed of artificial neural network (ANN) and genetic expression programming (GEP). The application is performed for the 60-accelerograph station sites located in California, USA. The predictor variables of $$\text{ V}_\mathrm{S30}$$ in AI models, which are properly organized from strong ground motion data, are magnitude, site-to-source distance, peak ground acceleration and spectral accelerations at different site periods. $$\text{ V}_\mathrm{S30}$$ values as output variable are collected from the surface wave testings conducted in the sites. The results indicates that for the considered highly nonlinear problem in this paper, the developed ANN and GEP models perform good predictions in terms of error and correlation. It can be concluded that the AI methods are relatively promising for prediction of $$\text{ V}_\mathrm{S30}$$. The findings from this paper can be helpful to improve the site descriptions at the current database of the study region. [ABSTRACT FROM AUTHOR]

The Ethernet passive optical network is being regarded as the most promising for next-generation optical access solutions in the access networks. In time division multiplexing, passive optical network technology (TDM-PON) and the dynamic bandwidth allocation (DBA) play a crucial key role to achieve efficient bandwidth allocation and fairness among subscribers. Therefore, the traffic prediction in DBA during the waiting time must be put into the account. In this paper, we propose a new prediction approach with an evolutionary algorithm genetic expression programming (GEP) prediction incorporated with Limited IPACT referred as GLI-DBA to tackle the queue variation during waiting times as well as to reduce the high-priority packet delay. Simulation results show that the GEP prediction in DBA can reduce the expedited forwarding (EF) packet delay, shorten the EF queue length, enhance the quality of services and maintain the fairness among the optical network units (ONUs). We conducted and evaluated the detail simulation in two different scenarios with distinctive traffic proportion. Simulation results show that the GLI-DBA has EF packet delay improvement up to 30 % over dynamic bandwidth allocation for multiple of services (DBAM). It also shows that our proposed prediction scheme performs better than the DBAM when the number of ONUs increases. [ABSTRACT FROM AUTHOR]

Abstract: Peak ground acceleration (PGA) has still been considered one of the important factors that plays significant role on the earthquake-induced damage of structures. Thus, prediction of the PGA and selection of appropriate ground motion models often have become a valuable topic for seismic hazard assessments. This paper presents an application in order to predict the PGA by a relatively new prediction tool, genetic expression programming (GEP) and the conventional method of regression, and then aims to select the appropriate ground motion models derived by the GEP and regression methods. The selection was carried out by comparing the prediction performances through a ranking process using a new goodness-of-fit measure, likelihood estimation (LH), recently proposed by Scherbaum et al. (2004, on the use of response spectral reference data for the selection and ranking of ground-motion models for seismic hazard analysis in regions of moderate seismicity: the case of rock motion. Bulletin of the Seismological Society of America 94, 1–22). The Turkish earthquake data properly organized were used for deriving the candidate ground motion models of PGA attenuation equations. The validations of the LH method as well as the GEP and regression models were performed by the key attenuation characteristics, the attenuation equations for Turkey and the case records of Turkish strong ground motion data. The results indicate that majority of the PGA candidate models (GEP and regressions) that are ranked as good qualifications (class A and B) by the LH method generally pass the model validations, but the ones ranked as lower levels (class C) fail. This finding indicates that the LH method can be beneficial for the comparisons of the candidate ground motion models, and the selection of appropriate models for attenuation studies. Even though the GEP-based models that were found to be appropriate by the LH method need some improvements to obtain simple functional forms of PGA predictive relationships, their PGA predictions were reasonably consistent with the model validations. Thus, this study suggests that the GEP approach can be employed for predicting the PGAs in seismic hazard studies at least as a supplement to conventionally derived predictive relationships from the regressions. [Copyright &y& Elsevier]

In the present work, the wear of cutting tools in the machining of 7075Al alloy composites reinforced with 10 and 25 wt-% and 16 mm average size Al2O3 particles was investigated. Machining tests were performed on both as cast and heat treated composites by turning process using a physical vapour deposition coated carbide tool CP500 at different cutting conditions. A new model was developed to predict the tool life by genetic expression programming. The training and validation data sets were obtained from the well established machining test results. The weight fraction of particle Pw, cutting speed V, feedrate f and heat treatment Ht were used as independent input variables, while tool life T was used as a dependent output variable. Different models for tool life were predicted on the basis of the training data set using genetic programming, and the accuracy of the best model was proved with validation data set. The test results showed that the genetic expression programming model has produced correlation coefficient R values of ~0·958 for the training data and 0·952 for the validation data. The predicted tool life results were compared with experimental results and found to be in good agreement with the experimentally observed ones. [ABSTRACT FROM AUTHOR]

Machining of particle-reinforced metal matrix composites has been considerably difficult due to the extremely abrasive nature of the reinforcements that causes rapid tool wear and high machining cost. Abrasive water jet (AWJ) machining has proven to be a viable technique to machine such materials compared to conventional machining processes. The present study is focused on the surface roughness of AWJ cut surfaces and genetic expression programming (GEP) was proposed to predict surface roughness in AWJ machining of 7075 Al alloy composites reinforced with AlO particles. In the development predictive models, characteristics of materials such as size and weight fraction of reinforcement particles, and depth of cut were considered as model variables. The training and testing data sets were obtained from the well-established machining test results. The weight fraction of particle, size of particle, and depth of cut were used as independent input variables, while arithmetic mean of surface roughness, maximum roughness of profile height, and mean spacing of profile irregularity as dependent output variables. Different models for the output variables were predicted on the basis of training data set using GEP and accuracy of the best model was proved with testing data set. The test results showed that output variables increased with increasing input variables. The predicted results were compared with experimental results and found to be in good agreement with the experimentally observed ones. [ABSTRACT FROM AUTHOR]

Abstract: Liquefaction of soils induced by the earthquake is one of the major complex problems for the geotechnical engineering. It is generally determined from in situ tests and laboratory test of which application is very difficult, expensive and time consuming. They also require extreme cautions and labor. Hence the development of new models for the prediction of liquefaction potential of soils provides fairly significant facility to design the constructions. This study presents the potential of Genetic Expression Programming (GEP) and Adaptive Neuro-Fuzzy (ANFIS) computing paradigm to forecast the safety factor for liquefaction of soils. To develop the models, a total of 569 data set collected from the literature were used. Five parameters such as standard penetration test ((N 1)60), percentage of finest content (FC), effective overburden stresses (σ′), cyclic stress ratios (CSR) and angle of shearing resistance (ϕ′) were used as input parameters. The performance of models was comprehensively judged using several statistical verification tools. The results revealed that GEP and ANFIS models are fairly promising approach for the prediction of the soil liquefaction potential and capable of representing the complex relationship between seismic properties of soils and their liquefaction potential. [ABSTRACT FROM AUTHOR]

Abstract: Precise determination of the effective angle of shearing resistance (ϕ′) value is a major concern and an essential criterion in the design process of the geotechnical structures, such as foundations, embankments, roads, slopes, excavation and liner systems for the solid waste. The experimental determination of ϕ′ is often very difficult, expensive and requires extreme cautions and labor. Therefore many statistical and numerical modeling techniques have been suggested for the ϕ′ value. However they can only consider no more than one parameter, in a simplified manner and do not provide consistent accurate prediction of the ϕ′ value. This study explores the potential of Genetic Expression Programming, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the prediction of ϕ′ value of soils. The data from consolidated-drained triaxial tests (CID) conducted in this study and the different project in Turkey and literature were used for training and testing of the models. Four basic physical properties of soils that cover the percentage of fine grained (FG), the percentage of coarse grained (CG), liquid limit (LL) and bulk density (BD) were presented to the models as input parameters. The performance of models was comprehensively evaluated some statistical criteria. The results revealed that GEP model is fairly promising approach for the prediction of angle of shearing resistance of soils. The statistical performance evaluations showed that the GEP model significantly outperforms the ANN and ANFIS models in the sense of training performances and prediction accuracies. [Copyright &y& Elsevier]

Abstract: The method of electrical discharge machining (EDM), one of the processing methods based on non-traditional manufacturing procedures, is gaining increased popularity, since it does not require cutting tools and allows machining involving hard, brittle, thin and complex geometry. By using different EDM parameters (current, pulse on-time, pulse off-time, arc voltage), the R a (μm) roughness value as a result of application of a number of copper electrode-hardened powder metals (cold work tool steel) to a work piece has been investigated, in this study. At the same time, roughness values obtained from the experiments that have been modeled by using the genetic expression programming (GEP) method and a mathematical relationship has been suggested between the GEP model and surface roughness and parameters affecting it. Moreover, EDM has been used by applying copper, copper–tungsten (W–Cu) and graphite electrodes to the same material with experimental parameters designed in accordance with the Taguchi method. Results obtained from this study have been compared among each other and similar studies in the literature. [Copyright &y& Elsevier]

Abstract: CNC milling has become one of the most competent, productive and flexible manufacturing methods, for complicated or sculptured surfaces. In order to design, optimize, built up to sophisticated, multi-axis milling centers, their expected manufacturing output is at least beneficial. Therefore data, such as the surface roughness, cutting parameters and dynamic cutting behavior are very helpful, especially when they are computationally produced, by artificial intelligent techniques. Predicting of surface roughness is very difficult using mathematical equations. In this study gene expression programming method is used for predicting surface roughness of milling surface with related to cutting parameters. Cutting speed, feed and depth of cut of end milling operations are collected for predicting surface roughness. End of the study a linear equation is predicted for surface roughness related to experimental study. [Copyright &y& Elsevier]