• Computers and Concrete
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• v.30 no.6
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• pp.377-391
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• 2022

The use of carbon fiber-reinforced polymers (CFRP) has widely increased due to its enhancement in the ultimate strength and ductility of the reinforced concrete (RC) structures. This study presents a prediction model for the axial compressive strength and strain of normal-strength concrete cylinders confined with CFRP. Besides, soft computing approaches have been extensively used to model in many areas of civil engineering applications. Therefore, the genetic expression programming (GEP) models to predict axial compressive strength and strain of CFRP-confined concrete specimens were used in this study. For this purpose, the parameters of 283 CFRP-confined concrete specimens collected from 38 experimental studies in the literature were taken into account as input variables to predict GEP based models. Then, the results of GEP models were statistically compared with those of models proposed by various researchers. The values of R² for strength and strain of CFRP-confined concrete were obtained as 0.897 and 0.713, respectively. The results of the comparison reveal that the proposed GEP-based models for CFRP-confined concrete have the best efficiency among the existing models and provide the best performance.

• Computers and Concrete
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• v.27 no.4
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• Asian Journal of Atmospheric Environment
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• v.7 no.2
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• pp.85-94
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• 2013

Methane concentrations have been monitored at the Anmyeon-do Observatory, Korea, since 1999. In recent years, the methane concentration has increased, but the sources of this increase have yet to be identified. This study was designed to identify the major source contributing to the increase by using World Data Centre for Greenhouse Gases (WDCGG) data and the Greenhouse Gases Emission Presumption (GEP) method. The data were collected at Anmyeon-do between 2003 and 2009 (except 2008), and the analyses showed that the increase in methane concentration originated mainly in rice paddies around the observation point. The annual average methane concentration at Anmyeon-do was 1894 ppb, of which 100-103 ppb (5.3-5.4%) was shown to originate mainly from rice paddies. The seasonal fluctuation in methane concentration from May to October estimated by the GEP method was compared with experimental data of previous research conducted on rice paddies. The close match obtained through this comparison shows that the GEP method is effective. The difference in methane concentration was also analyzed in terms of land use and land cover. It was shown that although rice paddies account for only 14.7% of the area surveyed, they accounted for between 69 and 90% of the total increase in methane concentration. These results confirm that rice paddies are the main source of the increase in methane concentration observed at Anmyeon-do.

• Computers and Concrete
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• v.28 no.5
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• pp.451-463
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• 2021

Inorganic basalt fiber (BF) is a novel sort of commercial concrete fiber which is made with basalt rocks. Previous studies have not sufficiently handled the behavior of self-compacted concrete, at elevated temperature, containing basalt fiber. Present endeavor covers experimental work to examine the characteristics of this material at high temperature considering different fiber content and applied temperature. Different tests were carried out to measure the mechanical properties such as compressive strength (fc), modulus of elasticity (E), Poisson's ratio, splitting tensile strength (fsplit), flexural strength (fflex), and slant shear strength (fslant) of HSC and hybrid concrete. Gene expression programming (GEP) was employed to propose new constitutive relationships depending on experimental data. It was noticed from the testing records that there is no remarkable effect of BF on the Poisson's ratio and modulus of elasticity of self-compacted concrete. The flexural strength of basalt fiber self-compacted concrete was not sensitive to temperature in comparison to other mechanical properties of concrete. Fiber volume fraction of 0.25% was found to be the optimum to some extend according to degradation of strength. The proposed GEP models were in good matching with the experimental results.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• Computers and Concrete
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• v.24 no.4
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• pp.295-302
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• 2019

Evolutionary algorithms based on conventional statistical methods such as regression and classification have been widely used in data mining applications. This work involves application of gene expression programming (GEP) for predicting compressive strength of fly ash geopolymer concrete, which is gaining increasing interest as an environmentally friendly alternative of Portland cement concrete. Based on 56 test results from the existing literature, a model was obtained relating the compressive strength of fly ash geopolymer concrete with the significantly influencing mix design parameters. The predictions of the model in training and validation were evaluated. The coefficient of determination ($R^2$), mean (${\mu}$) and standard deviation (${\sigma}$) were 0.89, 1.0 and 0.12 respectively, for the training set, and 0.89, 0.99 and 0.13 respectively, for the validation set. The error of prediction by the model was also evaluated and found to be very low. This indicates that the predictions of GEP model are in close agreement with the experimental results suggesting this as a promising method for compressive strength prediction of fly ash geopolymer concrete.

• Computers and Concrete
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• v.26 no.6
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• pp.497-504
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• 2020

Concrete can lose its alkalinity by concrete carbonation causing steel corrosion. Thus, the determination of the carbonation depth is necessary. An empirical model is proposed in this research to predict the carbonation depth of concrete using Gene expression programming (GEP). The GEP model was trained and validated using a large and reliable database collected from the literature. The model was developed using the six parameters that predominantly control the carbonation depth of concrete including carbon dioxide CO2 concentration, relative humidity, water-to-cement ratio, maximum aggregate size, aggregate to binder ratio and carbonation period. The model was statistically evaluated and then compared to the Jiang et al. model. A parametric study was finally performed to check the proposed GEP model's sensitivity to the selected input parameters.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.637-651
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• 2022

The seismic design of embankment dams requires more comprehensive studies to understand the behaviour of dams. Deformations primarily control this behaviour occur during or after earthquake loading. Dam failures and incidents show that the impacts of deformations should be reviewed for existing and new embankment dams. Overtopping erosion failure can occur if crest deformations exceed the freeboard at the time of the deformations. Therefore, crest settlement is one of the most critical deformations. This study developed empirical formulas using Gene Expression Programming (GEP) based on 88 cases. In the analyses, dam height (H_d), alluvium thickness (H_a), the magnitude-acceleration-factor (MAF) values developed based on earthquake magnitude (Mw) and peak ground acceleration (PGA) within this study have been chosen as variables. Results show that GEP models developed in the paper are remarkably robust and accessible tools to predict earthquake-induced crest settlement of embankment dams and perform superior to the existing formulation. Also, dam engineering professionals can use them practically because the variables of prediction equations are easily accessible after the earthquake.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.651-669
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• 2018

This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The $R^2$ value associated with the GEP-PSO algorithm, is about 0.13 higher than the $R^2$ associated with MLR.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.65-72
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• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.