Gene Expression Programming Method Research Articles

Optimized punching shear design in steel fiber-reinforced slabs: Machine learning vs. evolutionary prediction models

This research paper focuses on utilizing Artificial Neural Networks (ANN), Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR), and Gene Expression Programming (GEP) to predict the punching shear strength of Steel Fibre-Reinforced Concrete (SFRC) slabs.In order to formulate predictions, research and analysis were carried out making use of a dataset, this dataset included several parameters that impact on punching shear strength, including SFRC slabs longitudinally and transversely, using ANN, GEP, and MOGA-EPR methods. The developed models exhibited very good performance, as the soft computing techniques (GEP and MOGA-EPR) achieved R² values of 0.91 to 0.93, while the ANN technique was higher at 0.95. Furthermore, two case studies were incorporated to carry out cost analyses of the models in real-world applications. It was shown that the efficiency of the Machine Learning (ML) models in reducing the costs of materials is relatively high, as they were capable of better predictions than the standard methods employed by the codes.

Prediction and sensitivity analysis of embankment dam settlement under earthquake loading using gene expression programming

ABSTRACT A correct understanding of dams’ dynamic behaviour can help improve structures’ design and safety. The settlement of earth dams caused by earthquake shaking can induce instability and destruction. Using white-box machine learning techniques, including gene expression programming (GEP), a numerical relationship has been tried to estimate the settlement of earth dams under different types of earthquake loading, so that this relationship can be used in the design of earth dams. The main input parameters used in this research include the ratio of the yield acceleration of the dam to the maximum horizontal acceleration of the earthquake (ay/amax), the ratio of the fundamental period of the dam to the predominant period of the earthquake (Td/Tp), the magnitude of the earthquake (Mw) and the settlement ratio (S/H) earth dam as the output parameter. The R, MAE, and RMSE values for the proposed relationship in the research were equal to 0.85, 0.058, and 0.127, respectively. The measurement of statistical parameters shows the acceptable accuracy of the GEP method in predicting S/H. However, developing a database containing other effective parameters can be considered for future research.

QSAR Studies on a Series of Pyrazole Azabicyclo [3.2.1] Octane Sulfonamides N-acylethanolamine-hydrolyzing Acid Amidase Inhibitors

Background: Inflammation is a common and intractable disease for humans. Current antiinflammatory drugs have a lot of side effects, which cause irreversible damage to the body. Objective: We predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds. Methods: We established a quantitative structure-activity relationship (QSAR) model by gene expression programming to predict the IC50 values of natural compounds. The NAAA inhibitor, as a cysteine enzyme, plays an important role in the therapy of pain, anti-inflammatory effects and application of other diseases. A total of 36 NAAA inhibitors were optimized by the heuristic method in the CODESSA program to build a linear model. The 27 compounds and 9 compounds were in train and test sets. On this basis, we selected three descriptors and used them to build nonlinear models in gene expression programming. Results: The best model in the gene expression programming method was found, the square of correlation coefficients of R2 and mean square error for the training set were 0.79 and 0.14, testing set was 0.78 and 0.20, respectively. Conclusion: From this method, the activity of molecules could be predicted, and the best method was found. Therefore, this model has a stronger predictive ability to develop NAAA inhibitors.

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks.

Hyper-tuning gene expression programming to develop interpretable prediction models for the strength of corncob ash-modified geopolymer concrete

Geopolymer concrete has been offered as an alternative to traditional concrete due to the environmental consequences of cement manufacture. It is also unable to make adjustments if the concrete's strength after casting does not meet the required strength. Because of this, it is highly beneficial to make strength predictions before pouring concrete. This study proposes applying the gene expression programming (GEP) method to forecast the mechanical strength of slag and corncob ash-based geopolymer concrete (SCA-GPC). The SHapley Additive exPlanations (SHAP) approach was used to identify the significance of parameters used for modeling. Best-fitted simulations and exceptional prediction performance were produced by the GEP (R2 = 0.96, MAE = 1.876 MPa, MAPE = 6.00%, RMSE = 2.417 MPa, and NSE = 0.954), (R2 = 0.94, MAE = 0.178 MPa, MAPE = 3.50%, RMSE = 0.236 MPa, and NSE = 0.942), and (R2 = 0.94, MAE = 0.126 MPa, MAPE = 3.60%, RMSE = 0.154 MPa, and NSE = 0.935) for compressive, flexural, and split tensile strengths models of SCA-GPC, respectively. Error and efficiency-based statistical checks confirmed the accuracy of the created models. The SHAP research also found that the mechanical strength of SCA-GPC was mostly controlled by slag quantity, curing age, water quantity, and fine aggregate quantity in the mix proportion. This research showed that the mechanical strength of SCA-GPC could be effectively predicted using the GEP method. The implementation of such methods will significantly improve the process of ensuring the quality of geopolymer concrete.

An optimized model based on the gene expression programming method to estimate safety factor of rock slopes

An optimized model based on the gene expression programming method to estimate safety factor of rock slopes

Prediction of compressive strength of brick columns confined with FRP, FRCM, and SRG system using GEP and ANN methods

This study assesses the strength capacity of brick columns under various confinement materials, including fiber-reinforced polymer (FRP), fiber-reinforced cementitious matrix (FRCM), and steel-reinforced grout (SRG) using gene expression programming (GEP) and artificial neural networks (ANN) models. To achieve this, a comprehensive database of masonry column test results from existing scientific literature is compiled. The models' performance is evaluated using statistical errors like the coefficient of linear correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Additionally, sensitivity analysis is carried out to assess the significance of individual parameters in the models. The findings reveal that ANN predictions closely match empirical data, demonstrating a strong correlation coefficient of 0.95. The accuracy of the ANN approach is reasonably high, with only 26% of the predicted values deviating by more than 20% from the actual data. Based on the statistical analyses, the correlation coefficient between the actual and estimated data was 0.88, for GEP method. Also, the GEP model yields outcomes, with roughly 43% of the predicted values differing by 20–50% from the actual data. In a comparison of the two models, the ANN model outperforms the GEP model, displaying a 40% reduction in error when estimating the compressive strength of masonry columns. The data estimated by the GEP were sparser than those estimated by the ANN. Nevertheless, the GEP model still maintains an acceptable correlation coefficient and error rate, making it a viable choice for precise predictions. It offers a user-friendly formula and meets the needs of both customers and builders.

A novel non-linear approach for establishing a QSAR model of a class of 2-Phenyl-3-(pyridin-2-yl) thiazolidin-4-one derivatives.

In this investigation, we aimed to address the pressing challenge of treating osteosarcoma, a prevalent and difficult-to-treat form of cancer. To achieve this, we developed a quantitative structure-activity relationship (QSAR) model focused on a specific class of compounds called 2-Phenyl-3-(pyridin-2-yl) thiazolidin-4-one derivatives. A set of 39 compounds was thoroughly examined, with 31 compounds assigned to the training set and 8 compounds allocated to the test set randomly. The goal was to predict the IC50 value of these compounds accurately. To optimize the compounds and construct predictive models, we employed a heuristic method of the CODESSA program. In addition to a linear model using four carefully selected descriptors, we also developed a nonlinear model using the gene expression programming method. The heuristic method resulted in correlation coefficients (R 2) of 0.603, 0.482, and 0.107 for R2 cv and S2, respectively. On the other hand, the gene expression programming method achieved higher R 2 and S2 values of 0.839 and 0.037 in the training set, and 0.760 and 0.157 in the test set, respectively. Both methods demonstrated excellent predictive performance, but the gene expression programming method exhibited greater consistency with experimental values. The successful nonlinear model generated through gene expression programming shows promising potential for designing targeted drugs to combat osteosarcoma effectively. This approach offers a valuable tool for optimizing compound selection and guiding future drug discovery efforts in the battle against osteosarcoma.

An optimized equation based on the gene expression programming method for estimating tunnel construction costs considering a variety of variables and indexes

Accurate cost estimation in tunneling is key to the project’s success. Such information is critical for the early conceptual and design phases when key choices must be made. Numerous variables influence the cost of tunnel construction, and limited information is available at the early stages of design when the possible use of tunnels is being studied. Therefore, there is a limited number of models at engineers’ disposal to develop a proper cost estimate for tunneling projects. This study aimed to offer a model for estimating the construction cost of drilling and blasting tunnels in the preliminary stage of a project. For this purpose, an optimized gene expression programming (GEP) method was used based on the study of 900 data points obtained from ten drilling and blasting tunnels, which were randomly split into the training (800 data points) and testing (100 data points) datasets. With the experience of previously constructed tunnels, eleven parameters were considered the most effective for the tunnel’s construction cost. The best fit on predictions generated an equation for the optimized GEP model. Finally, by comparing the equation’s outputs with the actual costs and its behavior with practice, its potential ability to estimate the construction cost of drilling and blasting tunnels was approved. Moreover, the Graphical User Interface (GUI) of the GEP model was created as a practical tool for estimating the construction cost of tunnels. This model can reduce tunnel uncertainties and give ML development in tunnel planning.

Unveiling the potential of an evolutionary approach for accurate compressive strength prediction of engineered cementitious composites

The different human activities in numerous fields of civil engineering have become possible due to recent development in soft computing. As many researchers have widely extended the use of evolutionary numerical methods to predict the mechanical properties of construction materials, it has become necessary to investigate the performance, accuracy, and robustness of these approaches. Gene Expression Programming (GEP) is a method that stands out among these methods as it can generate highly accurate formulas. In this study, two models of GEP are used to anticipate the compressive strength of engineered cementitious composite (ECC) containing fly ash (FA) and polyvinyl alcohol (PVA) fiber at 28 days. The experimental results for 76 specimens, which are made with ten different mixture properties, are taken from the literature to build the models. Considering the experimental results, four different input variables in the GEP approach are used to arrange the models in two modes: sorted data distribution (SDD) and random data distribution (RDD). Prognosticating the compressive strength values based on the mechanical properties of ECC containing FA and PVA will be possible for the models of the GEP method by using these input variables. The comparison between the experimental results and the results of training, testing, and validation sets of two models (GEP-I and GEP-II), each of which has two distinct distribution modes, is done. It is observed that both modes of RDD and SDD lead to responses with the same accuracy (R-square more than 0.9). Nevertheless, the GEP-I (SDD) model was chosen as the best model in this study based on its performance with the validation data set.

Predicting ultra-high-performance concrete compressive strength using gene expression programming method

There have been extensive experimental studies available on the composition and characteristics of Ultra-High-Performance concrete (UHPC). However, the relation between UHPC characteristics and mixture content, on the other hand, is extremely non-linear and challenging to distinguish utilizing typical statistical approaches. A comprehensive literature research was carried out for this aim to acquire experimental data on the compressive strength of UHPC. The dataset contains 810 experimental values of compressive strength and 15 most influential parameters that include cement, water, nano-silica, quartz powder, limestone powder, gravel, sand, slag, superplasticizer, fiber, temperature, age, fly ash, relative humidity, and silica fume, are considered as input. The suggested gene expression programming (GEP) model can estimate the compressive strength of UHPC by using simple mathematical formulations. There is no predetermined function to evaluate in the GEP technique, and it replicates or eliminates numerous combinations of factors to create the formulation that suits the experimental results. For verification and validation of model performance, various statistical measures, SHAP analysis, external validation checks, and comparing with the regression model, are applied. SHAP analysis provided that age, fiber, silica fume, superplasticizer, cement, sand, and water have a high influence on compressive strength while other input parameters have less influence on compressive strength. The model outcomes indicate the robustness and accuracy of the predictive potential of the proposed model. As a result, the GEP model can be used to give practical insights into the mixture design of UHPC for a variety of construction applications, resulting in better predictive capacity at a cheaper cost and in a considerably shorter period. Also, the present study findings can assist the design engineers and builders to understand the significance of each constituent in UHPC.

Sensitive analysis of meteorological data and selecting appropriate machine learning model for estimation of reference evapotranspiration

This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz and Dezful in the southwest of Iran. Comparison between results of the FAO Penman-Monteith (FPM) method and the mentioned three methods shows that ANN-GA with the Levenberg-Marquardt training method is the best method and the M5T model is the second appropriate method for estimation of reference evapotranspiration. In Ahvaz, R2 and RMSE of ANN-GA method are 0.996, 0.184 mm/day. For M5T method, these values are 0.997 and 0259 mm/day, and for GEP method, they are 0.979 and 0.521 mm/day. In Dezful, R2 and RMSE of ANN-GA method are 0.994, 0.235 mm/day. For M5T method, these values are 0.992 and 0265 mm/day, and for GEP method, they are 0.963 and 0.544 mm/day. In addition, sensitivity analysis shows that the maximum temperature is the most effective parameter, and the wind speed is second effective parameter. In Dezful, the effect of the maximum temperature is more than those of Ahvaz but the effect of wind speed is less than those of Ahvaz. Because Ahvaz is more flatter than Dezful (the movement of wind in Ahvaz is freer than those of Dezful). The third effective meteorological parameter is the average relative humidity in Ahvaz and the sunny hours in Dezful. The reason for this subject is the less distant of Ahvaz from the Persian Gulf (it is source of moisture).

Experimental-Correlative Framework to Evaluate Critical State Deformability Parameters and Free Swelling Index of Cohesive Soils Using Gene Expression Programming

Expansive soils stimulate serious problems for infrastructures and the near surface facilities due to volume change patterns it experiences during the wet and dry seasons. This research study is intended to implement Gene Expression Programming (GEP) scheme and the Multiple Linear Regression (MLR) method in order to assess the swelling aspects and deformability parameters of moderately expansive soils based on a comprehensive exploration and testing program for the Northeastern areas of Zarqa Governate in Jordan. Representative soil samples have been collected from twenty different locations in the area. The soil has been tested for free swelling, matric suction, permeability, consolidation, water content, hydrometer, and soil index properties. The free swelling index and critical state deformability parameters, obtained from the consolidation test, are the primary focus of this research. Several models that account for different situations based on opportunities of the data availability using an extensive GEP modeling scheme have been successfully developed. MLP method has been managed to develop one reasonable formula with less efficiency than the ones schemed by GEP procedure. The successful models have been validated using test results and error analysis procedures. The models have been ordered according to their according to a proposed ranking procedure. GEP method has showed higher performance for the given conditions.

Application of ordinal logistic and gene expression programming methods to predict the collapse sensitivity classes of loess soils, a case study: Golestan Province, northeastern Iran

The current study assesses the collapse sensitivity classes of loess soils using gene expression programming (GEP) and ordinal logistic regression (OLR). The crucial variable to forecast the possible development of loess caves in the Golestan Province (northeast of Iran) is the collapse sensitivity factor (Is). A database of 62 records, including the mechanical and physical characteristics of soils, was used. Oedometer tests were used to estimate the parameters of the collapse coefficient, the time needed for 90% settlement (T90%), and collapse sensitivity. The database includes 10 inputs (grain size, porosity, initial water content, precipitation, climatic data, liquid limit, calcium carbonate content, vegetation, and degree of soil saturation) and one output (collapse sensitivity classes). This is a complicated approach due to the complexity of setting up and performing such kinds of tests in the laboratory. The likelihood of soil classification ranks as severe, moderately severe, moderate, and small sensitivity was inspected using OLR and GEP. This study demonstrated that the OLR approach could effectively differentiate among more than 70% of distinct groups. Furthermore, experimental data reported from Semnan, Sarakhs, and Mashhad also attests to the accuracy of the OLR model. The sensitivity analysis indicated that silt fraction imparts the maximum effect on the collapse sensitivity classes. The trial-and-error method was used to determine the configurations of the GEP model prior to developing an ideal model. The performance of the GEP model to estimate the collapse sensitivity categories in a trustworthy, strong, and useful way is well documented by comparison between the results of the GEP and the experimental findings, which are affordable.

Data-driven nonlinear K-L turbulent mixing model via gene expression programming method

Data-driven nonlinear K-L turbulent mixing model via gene expression programming method

Using gene expression programming to discover macroscopic governing equations hidden in the data of molecular simulations

The unprecedented amount of data and the advancement of machine learning methods are driving the rapid development of data-driven modeling in the community of fluid mechanics. In this work, a data-driven strategy is developed by the combination of the direct simulation Monte Carlo (DSMC) method and the gene expression programming (GEP) method. DSMC is a molecular simulation method without any assumed macroscopic governing equations a priori and is employed to generate data of flow fields, while the enhanced GEP method is leveraged to discover governing equations. We first validate our idea using two benchmarks, such as the Burgers equation and Sine–Gordon equation. Then, we apply the strategy to discover governing equations hidden in the complex fluid dynamics. Our results demonstrate that in the continuum regime, the discovered equations are consistent with the traditional ones with linear constitutive relations, while in the non-continuum regime such as shock wave, the discovered equation comprises of high-order constitutive relations, which are similar to those in the Burnett equation but with modified coefficients. Compared to the Navier–Stokes–Fourier equations and the Burnett equation, the prediction of the viscous stress and heat flux in the shock wave via the presented data-driven model has the best match to the DSMC data. It is promising to extend the proposed data-driven strategy to more complex problems and discover hidden governing equations which may be unknown so far.

Prediction of the Height of Fracturing via Gene Expression Programming in Australian Longwall Panels: A Comparative Study

The caving and subsidence developments above a longwall panel usually result in fractures of the overburden, which decrease the strength of the rock mass and its function. The height of fracturing (HoF) includes the caved and continuous fractured zones affected by a high degree of bending. Among the various empirical models, Ditton’s geometry and geology models are widely used in Australian coalfields. The application of genetic programming (GP) and gene expression programming (GEP) in longwall mining is entirely new and original. This work uses a GEP method in order to predict HoF. The model variables, including the panel width (W), cover depth (H), mining height (T), unit thickness (t), and its distance from the extracted seam (y), are selected via the dimensional analysis and Buckingham’s P-theorem. A dataset involving 31 longwall panels is used to present a new nonlinear regression function. The statistical estimators, including the coefficient of determination (R2), the average error (AE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE), are used to compare the performance of the discussed models. The R2 value for the GEP model (99%) is considerably higher than the corresponding values of Ditton’s geometry (61%) and geology (81%) models. Moreover, the maximum values of the statistical error estimators (AE, MAPE, and RMSE) for the GEP model are 12%, 14%, and 16%, respectively, of the corresponding values of Ditton’s models.

DETERMINATION OF CONSTRUCTION PROJECT DURATION WITH ARTIFICIAL INTELLIGENCE

Aim: With Industry 4.0, the period of digitalization has begun in the construction industry, as in many other industries. The use of artificial intelligence techniques or algorithms instead of traditional methods to solve the problems experienced in construction projects and increase the risk of the project has increased in recent years. These studies have shown that artificial intelligence techniques or algorithms give successful results in solving problems in construction projects. This study investigates the applications of artificial intelligence techniques on construction project duration and demonstrates the use of the Gene Expression Programming method in estimating construction project duration. Method: In this study, analyzes were made with Gene Expression Programming (GEP) technique to estimate the project duration by using the variables of the number of floors, floor area and total construction area of 71 construction projects (60 training and 11 test data) built between 2011-2016. Results: The determination coefficient (R2) for this study was calculated as 0.78 for the training set and as 0.72 for the testing set. Conclusion: In this study, it has been seen that the ability of the created algorithm to estimate the construction time with limited data provides partially acceptable performance.

Peak strength prediction of reinforced concrete columns in different failure modes based on gene expression programming

The peak strength of reinforced concrete (RC) columns plays an important role in the appraisal of inelastic seismic performance. It depends on various parameters related to the geometry, reinforcement detail, material property, confinement effect, and loading condition. In applications, it is usually a prior condition to classify the failure modes of RC columns for predicting the peak strength accurately. Yet, classifying the failure modes of RC columns in an accurate way is a difficult task due to the complexity of the shear transfer mechanism. Thus, there is a need to develop a peak strength prediction model for RC columns failing in different modes directly. In this study, an attempt has been made by implementing the gene expression programming (GEP) method to realize this purpose. The experimental data required for the implementation of the GEP method are based on extensive results of RC columns tested in quasi-static cyclic loading. To validate the efficiency of the developed model, a detailed comparison against existing equations is conducted. The comparative results indicate that the developed model produces a rational prediction for the peak strength of RC columns in various failure modes. Based on the developed model, the peak strength can be predicted in a unified way for both ductile and non-ductile RC columns, which is beneficial for the seismic evaluation of existing structures.

Assessing the competency of a semi-parametric expert system in the realms of response characterization uncertainty in premixed methanol dual fuel diesel combustion strategies: In critique to RSM

Engine response characterization endeavours of the day are faced with the contradictory obligations of developing an accurate engine response map in face of an ever-expanding parametric space and decreasing test-bench based prototype development time cycles. Under such imperatives of sparse experimental regimens, the present study focuses on the pivotal notions of reliability in a data-driven multi-variate response modelling endeavour in the complex realms of a premixed methanol dual fuel diesel operation. In the study, the Gene Expression Programming (GEP) technique with its distinguishing features of adaptivity in model evolution has been critically examined for its credibility as an expert system in such emerging combustion strategies. The relevance and competency of the GEP method has been rationalized to this end by comparing its performance with the traditional Response Surface Methodology which has been the mainstay in such engine response characterization objectives. Model integrity has been analysed and compared through a multitude of conventional and improvised statistical goodness-of-fit measures encompassing absolute and relative error metrics. Model reliability was adjudged through an exhaustive uncertainty analysis. Information theoretic measures of Kullback-Leibler divergence and improvised Thiel uncertainty coefficients were employed to compare the quality of system knowledge gained by the competing techniques. Generalization capability was reckoned through all the respective metrics applied across a test data set held blind to the meta-model training sequence. Comparable regression scores across conventional and standardized correlation measures notwithstanding, all GEP evolved meta-models registered substantially lower footprints in all statistical error metrics together with lower uncertainty bandwidths of estimation and information loss across both training and test data simultaneously for all responses explored. The outcome of the study arguably evokes considerable reconsideration on the choice of conventional RSM based characterization strategies in the paradigms of system response uncertainty prevalent in the emergent engine combustion strategies involving significant yet unknown degrees of non-linearity in its parametric operational space.