• Computers and Concrete
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• v.19 no.6
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• pp.717-724
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• 2017

In this paper, four formulas are proposed via gene expression programming (GEP)-based models and regression analysis (RA) to predict the flexural strength ($f_s$) values of mortars containing different mineral admixtures that are ground granulated blast-furnace slag (GGBFS), silica fume (SF) and fly ash (FA) at different ages. Three formulas obtained from the GEP-I, GEP-II and GEP-III models are constituted to predict the $f_s$ values from the age of specimen, water-binder ratio and compressive strength. Besides, one formula obtained from the RA is constituted to predict the $f_s$ values from the compressive strength. To achieve these formulas in the GEP and RA models, 972 data of the experimental studies presented with mortar mixtures were gathered from the literatures. 734 data of the experimental studies are divided without pre-planned for these formulas achieved from the training and testing sets of GEP and RA models. Beside, these formulas are validated with 238 data of experimental studies un-employed in training and testing sets. The $f_s$ results obtained from the training, testing and validation sets of these formulas are compared with the results obtained from the experimental studies and the formulas given in the literature for concrete. These comparisons show that the results of the formulas obtained from the GEP and RA models appear to well compatible with the experimental results and find to be very credible according to the results of other formulas.

• Structural Engineering and Mechanics
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• v.47 no.3
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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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• v.32 no.1
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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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• 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.

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

• 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.

• Earthquakes and Structures
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• v.15 no.4
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• pp.403-410
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• 2018

The choice of attenuation relationships is one of the most important parts of seismic hazard analysis as using a different attenuation relationship will cause significant differences in the final result, particularly in near distances. This problem is responsible for huge sensibilities of attenuation relationships which are used in seismic hazard analysis. For achieving this goal, attenuation relationships require a good compatibility with the target region. Many researchers have put substantial efforts in their studies of strong ground motion predictions, and each of them had an influence on the progress of attenuation relationships. In this study, two attenuation relationships are presented using seismic data of Mazandaran province in the north of Iran by Genetic Expression Programming (GEP) algorithm. Two site classifications of soil and rock were considered regarding the shear wave velocity of top 30 meters of site. The quantity of primary data was 93 records; 63 of them were recorded on rock and 30 of them recorded on soil. Due to the shortage of records, a regression technique had been used for increasing them. Through using this technique, 693 data had been created; 178 data for soil and 515 data for rock conditions. The Results of this study show the observed PGA values in the region have high correlation coefficients with the predicted values and can be used in seismic hazard analysis studies in the region.

• Korean Management Science Review
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• v.17 no.1
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• pp.117-134
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• 2000

Pubilc utility industries including the electric utility industry are facing a new stream of privatization com-petition with the private sector and deregulation. The necewssity to solve now and in the future power supply and demand problems has been increasing through the sophisticated generation expansion plan(GEP) approach con-sidering not only KEPCo's supply-side resources but also outside resources such as non-utility generation(NUG) demand-side management (DSM). Under the environmental situation in the current electric utility industry a new approach is needed to acquire multiple resources competitively. This study presents the development of a modified electric generation expansion analysis system(EGEAS) model with avoided cost based on the existing EGEAS model which is a dynamic program to develope an optimal generation expansion plan for the electric utility. We are trying to find optimal GEP in Korea's case using our modified model and observe the difference for the level of reliabilities such as the reserve margin(RM) loss of load probability(LOLP) and expected unserved energy percent(EUEP) between the existing EGEAS model and our model. In addition we are trying to calculate avoided cost for NUG resources which is a criterion to evaluate herem and test possibility of connection calculation of avoided cost with GEP implementation using our modified model. The results of our case study are as follows. First we were able to find that the generation expansion plan and reliability measures were largely influenced by capacity size and loading status of NUG resources, Second we were able to find that avoided cost which are criteria to evaluate NUG resources could be calculated by using our modified EGEAS model with avoided cost. We also note that avoided costs were calculated by our model in connection with generation expansion plans.

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

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.