Multi-gene Genetic Programming Method Research Articles

Integrated approach to assessing strength in slag-based geopolymer mortars: experimental study and modeling with advanced techniques

The study consists of two main parts. In the initial phase, a variety of slag-based geopolymer mortars with different activator concentrations were prepared. These mortars underwent curing in both water and air environments for periods of 3, 7, 28, and 90 days, after which their compressive strength was evaluated at the conclusion of each curing interval. The second phase of the study is dedicated to the development of innovative models for estimating the compressive strength based on the data gathered. To achieve this, a range of techniques including multi-gene genetic programming (MGGP), artificial neural networks (ANN), XGBoost, SVM-Gauss, long short-term memory (LSTM), and convolutional neural networks (CNN) were employed to formulate a model capable of estimating compressive strength accurately. The study made use of various performance evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), R-squared, mean absolute error (MAE), and scatter index (SI) to assess the precision of the MGGP method in evaluating slag-based geopolymer mortars under both water and air curing conditions. The findings indicate that the equations generated by the MGGP method exhibit a high level of precision when juxtaposed with experimental outcomes. This research endeavors to enhance the prediction of compressive strength in geopolymer mortars, a subject that has garnered significant interest in scholarly literature.

Correction to: Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners

Correction to: Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners

Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming

Functionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermo-mechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is created for the thermal stress problem of the 2D-FG plate using Multi-gene Genetic Programming (MGGP). In our MGGP model in this study, data sets obtained from the numerical analysis results of the thermal stress problem are used, and formulas that give equivalent stress levels as output data, with the input data being the compositional gradient exponent, are obtained. For the current problem, efficient models that reduce CPU processing time are obtained by using the MGGP method.

Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods

Shear wave velocity is considered as one of the most important rock physical parameters which can be measured by dipole sonic imager (DSI) tool. This parameter is applied to evaluate porosity and permeability, rock mechanical parameters, lithology, fracture assessment, etc. On the other hand, this data is not available in all wells and hence, an accurate and reliable estimation of this parameter with the least uncertainty is of great importance in reservoir characterization. In this study, regression, multi-layer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP) methods are utilized to estimate the shear wave velocity using well log data. Also, the reported empirical correlations in the literature are also investigated in the studied field. The input data include depth, effective porosity, V p , gamma ray logs (natural and spectral), neutron log, density log and caliper log from the Bangestan Group Formation in one of the fields in southwestern Iran. In this study, all the expressed methods are compared based on the best coefficient of determination (R 2 ), root mean square error (RMSE), mean squared error (MSE), average absolute relative error (AARE), and average relative error (ARE). Among the used methods, MGGP was developed for using the useful features of this method including sensitivity analysis and correlation. Sensitivity analysis is performed on the input data using the MLP-ANN and MGGP method. Also, a correlation is suggested based on the MGGP method which is able to predict the shear wave velocity using the mentioned input parameters. The results show that the MLP-ANN method is more accurate, reliable and efficient compared to other methods studied in this paper. R 2 for the train, validation, and test phase are 0.9973, 0.9901 and 0.9898, respectively. The results of sensitivity analysis imply that compressional wave velocity has the highest impact on the shear wave velocity. Finally, Young Dynamic Modulus and Poisson Dynamic Ratio are computed using both real and predicted shear wave velocities. The results indicate that these two parameters can be calculated with high accuracy using predicted shear wave velocity. • Five methods are implemented for predicting shear wave velocity (V s ) using petrophysical logs. • ANN can predict the V s with high accuracy (R 2 Test = 0.9898). • A sensitivity analysis performed on the significance of input variables on the V s using the results of MGGP. • A correlation is introduced for forecasting the V s . • Investigate the effects of V s prediction by ANN on the geomechanical modelling.

Multivariate modeling of soil suction response to various rainfall by multi-gene genetic programing

In this study, field monitoring testing and machine learning are used to analyze vegetated soil’s response to various rainfall events under natural environmental conditions. Parameters that reflect the soil, vegetation, and atmosphere of three monitoring points at different distances from a tree (0.5 m, 1.5 m, and 3.0 m) at a constant depth (0.2 m) are quantified and used for multivariate model development. A machine learning method, multi-gene genetic programming (MGGP), is used to formulate the relationships between two indices representing vegetated soil response and six selected influential parameters. Analysis indicates that for a complicated system, the MGGP method is suitable for establishing an efficient computational model under conditions of limited data. Global sensitivity analysis and parametric study are conducted, based on the obtained multivariate models, to reveal the effect of each influential parameter, indicating that rainfall pattern has much the same impact on variations in soil suction as rainfall amount and intensity and tree canopy do. An advanced rainfall pattern can trigger a more rapid response of vegetated soil than intermediate and delayed rainfall patterns can. Rainfall pattern’s effect on the descent rate of soil suction is nonlinear.

Prediction of Maximum Pressure at the Roofs of Rectangular Water Tanks Subjected to Harmonic Base Excitation Using the Multi-Gene Genetic Programming Method

Liquid storage tanks subjected to base excitation can cause large impact forces on the tank roof, which can lead to structural damage as well as economic and environmental losses. The use of artificial intelligence in solving engineering problems is becoming popular in various research fields, and the Genetic Programming (GP) method is receiving more attention in recent years as a regression tool and also as an approach for finding empirical expressions between the data. In this study, an OpenFOAM numerical model that was validated by the authors in a previous study is used to simulate various tank sizes with different liquid heights. The tanks are excited in three different orientations with harmonic sinusoidal loadings. The excitation frequencies are chosen as equal to the tanks’ natural frequencies so that they would be subject to a resonance condition. The maximum pressure in each case is recorded and made dimensionless; then, using Multi-Gene Genetic Programming (MGGP) methods, a relationship between the dimensionless maximum pressure and dimensionless liquid height is acquired. Finally, some error measurements are calculated, and the sensitivity and uncertainty of the proposed equation are analyzed.

An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach

Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.

Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming

Multi-gene genetic programming (MGGP) is a new nonlinear system modeling approach that integrates the capabilities of standard GP and classical regression. This paper deals with the prediction of compression index of fine-grained soils using this robust technique. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. Several laboratory test results for fine fine-grained were used to develop the models. Various criteria were considered to check the validity of the model. The parametric and sensitivity analyses were performed and discussed. The MGGP method was found to be very effective for predicting the soil compression index. The prediction coefficients of determination were 0.856 and 0.840 for the training and testing data, respectively. A comparative study was further performed to prove the superiority of the MGGP model to the existing soft computing and traditional empirical equations.

Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process

Due to the complexity and uncertainty in the process, the soft computing methods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic and multi-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. Therefore, this paper proposes a modified-MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated and only the high performing genes are combined. In this work, the M-MGGP method is applied in modelling the surface roughness in the turning of hardened AISI H11 steel. The results show that the M-MGGP model produces better performance than those of MGGP, SVR and ANN. In addition, when compared to that of MGGP method, the models formed from the M-MGGP method are of smaller size. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the turning phenomenon of AISI H11 steel by unveiling dominant input process parameters and the hidden non-linear relationships.

Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach

The rapid prototyping (RP) processes, specifically selective laser melting (SLM), are popular for building complex 3D parts directly from the metal powder. The literature reveals that the properties such as surface roughness, waviness, bead width, compressive strength, tensile strength, wear, and dimensional accuracy of an SLM-fabricated prototype depend on the parameter settings of the SLM setup and can be improved by appropriate adjustment. For the selection of an optimal parameter setting, multi-gene genetic programming (MGGP), which develops the model structure and its coefficients automatically, can be applied. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. This paper proposes a modified MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated, and only the high performing genes are combined. The M-MGGP approach is applied on the bead width data obtained from the experiments conducted on the SLM machine, and its performance is found to be better than that of the standardized MGGP and artificial neural network (ANN) models. Between MGGP and ANN, ANN has shown better performance. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the SLM process by unveiling important process parameters and the hidden non-linear relationships.

An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes

Soil nailing is one of the slope stabilisation techniques useful for the strengthening of existing slopes. It helps to reinforce the soil with passive inclusions that increase the overall shear strength of the soil slope and also restrains its displacements. The limit equilibrium method is usually employed to estimate factor of safety (FOS) of nailed slopes through either finite element or finite difference methods. Alternatively, soft computing methods such as multi-gene genetic programming (MGGP), support vector regression (SVR) and artificial neural network (ANN) can also be used to predict the FOS for different soil properties. Among these methods, MGGP possesses the ability to evolve the model structure and its coefficients automatically. Although widely used, the MGGP method has the limitation of producing models that perform poorly on testing data. Therefore, in this study, an integrated structural risk minimisation-multi-gene genetic programming (SRM-MGGP) method is proposed to formulate the mathematical relationship between FOS and the six input variables of cohesion, frictional angle, nail inclination angle, nail length, slope height and slope angle of 3-D nailed slope. The results indicate that the SRM-MGGP model outperforms the other three models (MGGP, SVR and ANN) and is able to generalise the FOS satisfactorily for any given input variables conditions. This would be useful for engineers in their design calculations of slopes with different soil, slope and nail conditions based on certain limitations such as ignorance of effect of pore water pressure or overburden pressure.

Estimation of mechanical properties of nanomaterials using artificial intelligence methods

Computational modeling tools such as molecular dynamics (MD), ab initio, finite element modeling or continuum mechanics models have been extensively applied to study the properties of carbon nanotubes (CNTs) based on given input variables such as temperature, geometry and defects. Artificial intelligence techniques can be used to further complement the application of numerical methods in characterizing the properties of CNTs. In this paper, we have introduced the application of multi-gene genetic programming (MGGP) and support vector regression to formulate the mathematical relationship between the compressive strength of CNTs and input variables such as temperature and diameter. The predictions of compressive strength of CNTs made by these models are compared to those generated using MD simulations. The results indicate that MGGP method can be deployed as a powerful method for predicting the compressive strength of the carbon nanotubes.

Performance evaluation of microbial fuel cell by artificial intelligence methods

In the present study, performance of microbial fuel cell (MFC) has been modeled using three potential artificial intelligence (AI) methods such as multi-gene genetic programming (MGGP), artificial neural network and support vector regression. The effect of two input factors namely, temperature and ferrous sulfate concentrations on the output voltage were studied independently during two operating conditions (before and after start-up) using the three AI models. The data is randomly divided into training and testing samples containing 80% and 20% sets respectively and then trained and tested by three AI models. Based on the input factor, the proposed AI models predict output voltage of MFC at two operating conditions. Out of three methods, the MGGP method not only evolve model with better generalization ability but also represents an explicit relationship between the output voltage and input factors of MFC. The models generated by MGGP approach have shown an excellent potential to predict the performance of MFC and can be used to gain better insights into the performance of MFC.

Modeling intermolecular potential of He–F2 dimer from symmetry-adapted perturbation theory using multi-gene genetic programming

Any molecular dynamical calculation requires a precise knowledge of interaction potential as an input. In an appropriate form, such that the potential, with respect to the coordinates, can be evaluated easily and accurately at arbitrary geometries (in our study parameters for geometry are R and θ), a good potential energy expression can offer the exact intermolecular behavior of systems. There are many methods to create mathematical expressions for the potential energy. In this study for the first time, we utilized the Multi-gene Genetic Programming (MGGP) method to generate a potential energy model for the He–F2 system. The MGGP method is one of the most powerful methods used for non-linear regression problems. A dataset of size 714 created by the SAPT 2008 program is used to generate models of MGGP. The results obtained show the power of MGGP for producing an efficient nonlinear regression model, in terms of accuracy and complexity.

J07310287 Preparation of reinforced thermoplastic

Soil nailing is one of the slope stabilisation techniques useful for the strengthening of existing slopes. It helps to reinforce the soil with passive inclusions that increase the overall shear strength of the soil slope and also restrains its displacements. The limit equilibrium method is usually employed to estimate factor of safety (FOS) of nailed slopes through either finite element or finite difference methods. Alternatively, soft computing methods such as multi-gene genetic programming (MGGP), support vector regression (SVR) and artificial neural network (ANN) can also be used to predict the FOS for different soil properties. Among these methods, MGGP possesses the ability to evolve the model structure and its coefficients automatically. Although widely used, the MGGP method has the limitation of producing models that perform poorly on testing data. Therefore, in this study, an integrated structural risk minimisation-multi-gene genetic programming (SRM-MGGP) method is proposed to formulate the mathematical relationship between FOS and the six input variables of cohesion, frictional angle, nail inclination angle, nail length, slope height and slope angle of 3-D nailed slope. The results indicate that the SRM-MGGP model outperforms the other three models (MGGP, SVR and ANN) and is able to generalise the FOS satisfactorily for any given input variables conditions. This would be useful for engineers in their design calculations of slopes with different soil, slope and nail conditions based on certain limitations such as ignorance of effect of pore water pressure or overburden pressure.