• Nuclear Engineering and Technology
• /
• v.48 no.6
• /
• pp.1315-1320
• /
• 2016

The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.

• Steel and Composite Structures
• /
• v.48 no.2
• /
• pp.163-177
• /
• 2023

Complex and intricate preparation techniques, the imperative for utmost precision and sensitivity in instrumentation, premature sample failure, and fragile specimens collectively contribute to the arduous task of measuring the fracture toughness of concrete in the laboratory. The objective of this research is to introduce and refine an equation based on the gene expression programming (GEP) method to calculate the fracture toughness of reinforced concrete, thereby minimizing the need for costly and time-consuming laboratory experiments. To accomplish this, various types of reinforced concrete, each incorporating distinct ratios of fibers and additives, were subjected to diverse loading angles relative to the initial crack (α) in order to ascertain the effective fracture toughness (Keff) of 660 samples utilizing the central straight notched Brazilian disc (CSNBD) test. Within the datasets, six pivotal input factors influencing the Keff of concrete, namely sample type (ST), diameter (D), thickness (t), length (L), force (F), and α, were taken into account. The ST and α parameters represent crucial inputs in the model presented in this study, marking the first instance that their influence has been examined via the CSNBD test. Of the 660 datasets, 460 were utilized for training purposes, while 100 each were allotted for testing and validation of the model. The GEP model was fine-tuned based on the training datasets, and its efficacy was evaluated using the separate test and validation datasets. In subsequent stages, the GEP model was optimized, yielding the most robust models. Ultimately, an equation was derived by averaging the most exemplary models, providing a means to predict the Keff parameter. This averaged equation exhibited exceptional proficiency in predicting the Keff of concrete. The significance of this work lies in the possibility of obtaining the Keff parameter without investing copious amounts of time and resources into the CSNBD test, simply by inputting the relevant parameters into the equation derived for diverse samples of reinforced concrete subject to varied loading angles.

• Computers and Concrete
• /
• v.27 no.4
• /
• pp.333-341
• /
• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• Geomechanics and Engineering
• /
• v.33 no.1
• /
• pp.1-9
• /
• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Geomechanics and Engineering
• /
• v.30 no.6
• /
• pp.551-564
• /
• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Computers and Concrete
• /
• v.31 no.3
• /
• pp.253-265
• /
• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.

• Geomechanics and Engineering
• /
• v.36 no.5
• /
• pp.489-509
• /
• 2024

Slope stability analysis and prediction are of critical importance to geotechnical engineers, given the severe consequences associated with slope failure. This research endeavors to forecast the factor of safety (FOS) for slopes through the implementation of six distinct ML techniques, including back propagation neural networks (BPNN), feed-forward neural networks (FFNN), Takagi-Sugeno fuzzy system (TSF), gene expression programming (GEP), and least-square support vector machine (Ls-SVM). 344 slope cases were analyzed, incorporating a variety of geometric and shear strength parameters measured through the PLAXIS software alongside several loss functions to assess the models' performance. The findings demonstrated that all models produced satisfactory results, with BPNN and GEP models proving to be the most precise, achieving an R² of 0.86 each and MAE and MAPE rates of 0.00012 and 0.00002 and 0.005 and 0.004, respectively. A Pearson correlation and residuals statistical analysis were carried out to examine the importance of each factor in the prediction, revealing that all considered geomechanical features are significantly relevant to slope stability. However, the parameters of friction angle and slope height were found to be the most and least significant, respectively. In addition, to aid in the FOS computation for engineering challenges, a graphical user interface (GUI) for the ML-based techniques was created.

• Steel and Composite Structures
• /
• v.23 no.5
• /
• pp.543-555
• /
• 2017

The mechanical properties of steel fiber reinforced high strength concrete (SFRHSC) made with binary and ternary blends of metakaolin (MK) and ground pumice (GP) are investigated in this study. The investigated properties are ultrasonic pulse velocity ($U_{pv}$), compressive strength ($f_c$), flexural strength ($f_f$) and splitting tensile strength ($f_{st}$) of SFRHSC. A total of 16 steel fiber reinforced concrete mixtures were produced by a total binder content of $500kg/m^3$ for determining the effects of MK and GP on the mechanical properties. The design $f_c$ was acquired from 70 to 100 MPa by using a low water-binder ratio of 0.2. The test results exhibit that high strength concrete can be obtained by replacing the cement with MK and GP. Besides, correlations between these results are executed for comprehending the relationship between mechanical properties of SFRHSC and the strong correlations are observed between these properties. Moreover, two models in the gene expression programming (GEP) for predicting the $f_c$ of SFRHSC made with binary and ternary blends of MK and GP have been developed. The results obtained from these models are compared with the experimental results. These comparisons proved that the results of equations obtained from these models seem to agree with the experimental results.

• Steel and Composite Structures
• /
• v.52 no.3
• /
• pp.293-312
• /
• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.