• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.9 no.2
• /
• pp.83-89
• /
• 2005

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

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

• Structural Engineering and Mechanics
• /
• v.83 no.5
• /
• pp.577-591
• /
• 2022

This paper develops a comparatively time-efficient methodology for performing seismic fragility analysis of the reinforced concrete (RC) buildings in the presence of uncertainty sources. It aims to appraise the effectiveness of any variation in the material's mechanical properties as epistemic uncertainty, and the record-to-record variation as aleatory uncertainty in structural response. In this respect, the fuzzy set theory, a well-known 𝛼-cut approach, and the Genetic Algorithm (GA) assess the median of collapse fragility curves as a fuzzy response. GA is requisite for searching the maxima and minima of the objective function (median fragility herein) in each membership degree, 𝛼. As this is a complicated and time-consuming process, the authors propose utilizing the Gene Expression Programming-based (GEP-based) equation for reducing the computational analysis time of the case study building significantly. The results indicate that the proposed structural analysis algorithm on the derived GEP model is able to compute the fuzzy median fragility about 33.3% faster, with errors less than 1%.

• Computers and Concrete
• /
• v.30 no.6
• /
• pp.377-391
• /
• 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
• /
• v.18 no.2
• /
• pp.155-163
• /
• 2016

This paper, proposes 20 models for predicting compressive strength of recycled aggregate concrete (RAC) containing silica fume by using gene expression programming (GEP). To construct the models, experimental data of 228 specimens produced from 61 different mixtures were collected from the literature. 80% of data sets were used in the training phase and the remained 20% in testing phase. Input variables were arranged in a format of seven input parameters including age of the specimen, cement content, water content, natural aggregates content, recycled aggregates content, silica fume content and amount of superplasticizer. The training and testing showed the models have good conformity with experimental results for predicting the compressive strength of recycled aggregate concrete containing silica fume.

• 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.

• Computers and Concrete
• /
• v.24 no.4
• /
• pp.295-302
• /
• 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
• /
• v.31 no.6
• /
• pp.637-651
• /
• 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.

• Computers and Concrete
• /
• v.26 no.4
• /
• pp.327-342
• /
• 2020

The objective of this study is to manufacture environmentally-friendly synthetic lightweight aggregates that may be used in the structural lightweight concrete production. The cold-bonding pelletization process has been used in the agglomeration of the pozzolanic materials to achieve these synthetic lightweight aggregates. In this context, it was aimed to recycle the waste fly ash by employing it in the manufacturing process as the major cementitious component. According to the well-known facts reported in the literature, it is stated that the main disadvantage of the synthetic lightweight aggregate produced by applying the cold-bonding pelletization technique to the pozzolanic materials is that it has a lower strength in comparison with the natural aggregate. Therefore, in this study, the metakaolin made of high purity kaolin and calcined kaolin obtained from impure kaolin have been employed at particular contents in the synthetic lightweight aggregate manufacturing as a cementitious material to enhance the particle crushing strength. Additionally, to propose a curing condition for practical attempts, different curing conditions were designated and their influences on the characteristics of the synthetic lightweight aggregates were investigated. Three substantial features of the aggregates, specific gravity, water absorption capacity, and particle crushing strength, were measured at the end of 28-day adopted curing conditions. Observed that the incorporation of thermally treated kaolin significantly influenced the crushing strength and water absorption of the aggregates. The statistical evaluation indicated that the investigated properties of the synthetic lightweight aggregate were affected by the thermally treated kaolin content more than the kaoline type and curing regime. Utilizing the thermally treated kaolin in the synthetic aggregate manufacturing lead to a more than 40% increase in the crushing strength of the pellets in all curing regimes. Moreover, two numerical formulations having high estimation capacity have been developed to predict the crushing strength of such types of aggregates by using soft-computing techniques: gene expression programming and artificial neural networks. The R-squared values, indicating the estimation performance of the models, of approximately 0.97 and 0.98 were achieved for the numerical formulations generated by using gene expression programming and artificial neural networks techniques, respectively.

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