• 대한토목학회논문집
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• 제34권6호
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• pp.1779-1786
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• 2014

물리현상의 난해성으로 인해 수학적인 관계식이 제시되기 어려운 경우 인공지능 기술에 근거한 다양한 기법이 적용되어 왔다. 수리학 분야의 대표적인 예로 교각주위 국부세굴 문제를 들 수 있다. 본 연구에서는 유전자 알고리즘의 진화된 방법인 GEP 기법을 이용하여 교각주위 국부세굴을 예측하는 방법을 제시하였다. 64개의 실험 자료를 이용하여 GEP 모형을 학습시켜 회귀식을 구축하였으며, 33개의 실험 자료를 이용하여 구축된 모형의 검증을 실시하였다. 평형세굴심 예측을 위하여 차원을 갖는 일반 변수와 표준화된 변수로 GEP 모형을 구축하여 예측 결과를 비교하였는데, 차원을 갖는 변수에 의한 GEP 모형이 세굴심을 더 잘 예측하는 것으로 나타났다. 구축된 GEP 모형을 두 가지 현장 실측자료에 적용하였다. 적용 결과, 실험 자료에 적용한 경우에 비해 예측의 정확도가 낮아지는 것을 확인하였다. 또한, 현장 실측자료를 이용하여 학습시킨 경우 실험 자료를 이용하는 경우 보다 예측 능력이 많이 향상되는 것으로 나타났다. GEP 모형의 적용성을 위해 ANN 모형과의 비교를 수행하였으며, 본 연구에서 사용된 GEP 모형이 교각주위 국부세굴 예측에 대하여 실내 및 현장 모두 ANN 모형보다 우수한 것으로 나타났다.

• Structural Engineering and Mechanics
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• 제47권3호
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• pp.401-419
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• 2013

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

• Computers and Concrete
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• 제17권4호
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• pp.489-498
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• 2016

In this paper, the flexural strength ($f_{fs}$) and splitting tensile strength ($f_{sts}$) of concrete containing different proportions of fly ash have been modeled by using gene expression programming (GEP). Two GEP models called GEP-I and GEP-II are constituted to predict the $f_{fs}$ and $f_{sts}$ values, respectively. In these models, the age of specimen, cement, water, sand, aggregate, superplasticizer and fly ash are used as independent input parameters. GEP-I model is constructed by 292 experimental data and trisected into 170, 86 and 36 data for training, testing and validating sets, respectively. Similarly, GEP-II model is constructed by 278 experimental data and trisected into 142, 70 and 66 data for training, testing and validating sets, respectively. The experimental data used in the validating set of these models are independent from the training and testing sets. The results of the statistical parameters obtained from the models indicate that the proposed empirical models have good prediction and generalization capability.

• Computers and Concrete
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• 제30권6호
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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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• 제32권1호
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• pp.1-13
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• 2023

This paper discusses a framework for predicting the flexural strength of prestressed and non-prestressed FRP reinforced T-shaped concrete beams using soft computing techniques. An analysis of 83 tests performed on T-beams of varying widths has been conducted for this purpose with different widths of compressive face, beam depth, compressive strength of concrete, area of prestressed and non-prestressed FRP bars, elasticity modulus of prestressed and non-prestressed FRP bars, and the ultimate tensile strength of prestressed and non-prestressed FRP bars. By analyzing the data using two soft computing techniques, named artificial neural networks (ANN) and gene expression programming (GEP), the fundamental parameters affecting the flexural performance of prestressed and non-prestressed FRP reinforced T-shaped beams were identified. The results showed that although the proposed ANN model outperformed the GEP model with higher values of R and lower error values, the closed-form equation of the GEP model can provide a simple way to predict the effect of input parameters on flexural strength as the output. The sensitivity analysis results revealed the most influential input parameters in ANN and GEP models are respectively the beam depth and elasticity modulus of FRP bars.

• Computers and Concrete
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• 제26권6호
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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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• 제36권5호
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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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• 제24권4호
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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.

• Geomechanics and Engineering
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• 제37권1호
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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.

• Computers and Concrete
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• 제27권4호
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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.