COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK

Laboratory experiments were used to estimate the hydraulic performance of emitters, i.e., the emitter flow variation (qvar) and manufacturer’s coefficient of variation (CVm), by measuring the discharge of different labyrinth-channel emitters at different operating pressures (P) and water temperatures (T). Considering the importance of the structural parameters of the labyrinth-channel emitters in drip irrigation design, which has been experimentally confirmed, artificial neural network (ANN) and gene expression programming (GEP) models were developed to predict qvar and CVm. The ANN and GEP models were trained and tested using structural parameters (including the number, height (H), and spacing of trapezoidal units and the flow path width and length) of different labyrinth-channel emitters, P and T as the input variables, and qvar and CVm as the outputs. Statistical criteria, including the coefficients of correlation (r), relative root-mean-square error (RRMSE), and mean absolute error (MAE), were used to examine the accuracy of the developed models. The ANN models exhibited good correlation with experimental values, with high r values 0.995 and 0.969 for qvar and 0.997 and 0.947 for CVm in the training and testing processes, respectively. The ANN models had lower RRMSE and MAE values than the GEP models. Furthermore, H was the dominant variable for obtaining the most accurate prediction model. The results confirm that the ANN models are superior to the GEP models for the prediction of the hydraulic performance of emitters.

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.

Hydraulic jumps (HJs) play a vital role in energy dissipation in hydraulic systems and are critical for the effective design of water management structures. This study employed Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models to predict the roller length ratio (L*) of HJs over rough beds. The analysis utilized a dataset of 367 experimental observations with a 70–30 training and testing split. Comprehensive data descriptions were conducted, ensuring a detailed understanding of the inputs, including the upstream Froude number (F*), the ratio of initial to sequent HJ depth (H* = h2/h1), and the ratio of channel bed roughness to initial HJ depth (K* = ks/h1). Descriptive statistics revealed moderate variability and mostly symmetric distributions, making the dataset suitable for predictive modeling. A sensitivity analysis was conducted and confirmed that the depth ratio (H*) had the highest influence on L*, followed by F* and K*. The ANN model achieved a training R2 of 0.937 and a testing R2 of 0.935, with RMSEs of 1.737 and 1.719, respectively. The GEP model demonstrated a training R2 of 0.941 and a testing R2 of 0.930, with RMSEs of 1.682 and 1.780. Both models displayed reliable predictive capabilities, with minimal bias and consistent performance in unseen data, supported by comprehensive error distribution analysis and uncertainty evaluations. Moreover, the models demonstrated a high level of agreement with prior research results, highlighting the importance of thorough data characterization and model validation. Thus, ANN and GEP models have been recognized as effective techniques for predicting hydraulic jump length.Graphical

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized.

Prediction of flow duration curves for ungauged basins

Temperature and solar radiation-based modeling procedures are reported in this study for estimating daily reference evapotranspiration (ET0) by using gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS). A comparison is also made among these techniques and the corresponding traditional temperature/radiation-based ET0 estimation equations. Two data management scenarios were evaluated for estimating ET0: (1) the models were trained and tested using the local data of each studied weather station; and (2) the models were trained using the pooled data from all the stations and tested in each individual station. The GEP and ANFIS models were found to be better than the Hargreaves–Samani, Makkink and Turc ET0 equations in the first scenario. Comparison of GEP and ANFIS models trained with pooled data and tested for each station showed that the ANFIS models generally performed better than the GEP models. However, the comparison of GEP and ANFIS models trained and tested with pooled data revealed that the GEP models performed better than the ANFIS models in the second scenario.

Composite columns have superior strength and ductility performance, and they have become more widely accepted in the engineering applications. However, the filled tubular columns require more attention. This study aims to present a new formulation for the axial load carrying capacity (N u ) of circular concrete filled steel tubular (CFST) short columns having various geometrical and material properties. Although there have been some empirical relations for predicting N u in the literature, genetic algorithm based explicit formulation is not available. In the current study, 314 comprehensive experimental data samples presented in the previous studies were examined to prepare a data set for training and testing of the prediction model. The prediction parameters were selected as outer diameter of column (D), wall thickness (t), length of column (L), compressive strength of concrete (f c ), and yield strength of steel (f y ). The prediction model was obtained by means of gene expression programming (GEP). The proposed model was compared with available ones presented in the current design codes (ACI, Australian Standards, AISC, AIJ, Eurocode 4, DL/T, and CISC) and some existing empirical models proposed by researchers. The prediction performance of all models were also evaluated by the statistical parameters. The results indicated that the GEP model was much better than the available formulae, yielding higher correlation coefficient and lower error.

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.

This paper is aimed to introduce and validate a gene expression programming (GEP) model to estimate the rate of air flow in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure and air outlet pressure. To achieve the aim of this study, a series of laboratory experiments were designed and carried out and then a database comprising 47 datasets was prepared to develop new predictive models. A gene expression programming (GEP) model for prediction of air flow was proposed using the prepared datasets. In this regard, a series of sensitivity analyses were performed to choose the best GEP model. For comparison purposes, multiple regression (MR) analysis was also employed for air flow estimation. Several performance indices, i.e., coefficient of determination (CoD), mean absolute error (MAE), root mean square error (RMSE) and variance account for (VAF) were considered and calculated to evaluate the performance prediction of the developed models. Considering both training and testing datasets, the developed GEP model can provide higher performance prediction of rate of air flow in comparison to the MR model.

Compressive strength of axially loaded circular concrete-filled double-skin steel tubular short columns

The ultimate bond strength between Fiber Reinforced Polymers (FRP) and concrete is one of the most important elements in the performance of the strengthened beam and its failure mode and failure mechanism. In this investigation an Artificial Neural Network (ANN) model has been developed to predict the ultimate bond strength (P u ) between FRP and concrete based on several factors that influence it. These factors, which were used as input to the ANN, include concrete prism width (b c ), concrete compressive strength (f cu ), concrete tensile strength (f t ) as well as the FRP thickness (t f ), width (bf), tensile strength (f f ), elastic modulus (E f ) and the bond length (L) between FRP and concrete. The ANN predicted ultimate strength loads were compared with experimental values. It is concluded that the ultimate bond strength predicted by the ANN model are reasonably accurate compared to the experimental values and the accuracy can be further improved by using sufficient data generated by similar standardized tests. Based on the developed model, a parametric study can be carried out to investigate the influence of several parameters on the ultimate bond-strength between FRP and concrete and on the behaviour of bond slip compared to existing models.

ABSTRACTThis paper investigates the ability of five different data‐driven methods, artificial neural networks (ANNs), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), support vector regression (SVR) and gene expression programming (GEP), in predicting long‐term monthly temperatures by using data from 50 stations in Iran. The periodicity component (month of the year), station latitude, longitude and altitude values were used as inputs to the applied models to predict the long‐term monthly temperatures. The overall accuracy of the SVR model was found to be better than that of the other models. The GEP model gave the worst estimates. The maximum determination coefficient (R2) values were found to be 0.996, 0.999, 0.997 and 0.959 for the ANN, ANFIS‐GP, ANFIS‐SC and GEP models in Karaj and Qazvin stations, respectively. The highest R2 value (0.999) of SVR model was found for the Tabas station. The minimum R2 values were respectively found as 0.988, 0.946 and 0.985 for the ANFIS‐GP, ANFIS‐SC and SVR models in Bandar Abbas station while the ANN and GEP models gave the minimum R2 values of 0.982 and 0.886 in the Abadan and Kerman stations, respectively. The results indicated that the long‐term monthly temperatures of any site can be successfully estimated by data‐driven methods applied in this study using geographical inputs. The interpolated maps of temperatures were also obtained by using the optimal SVR model and evaluated in the study. The temperature maps showed that the highest temperatures were occurred in the southeastern and central parts of the Iran.

Oil price forecasting using gene expression programming and artificial neural networks

The aim of this research is to predict preplaced-aggregate concrete (PAC) compressive strength (CS) by using machine learning approaches such as gene expression programming (GEP) and random forest (RF). PAC requires injecting a portland cement-sand grout with admixtures into a mold after coarse aggregate has been deposited, making CS prediction complicated and requiring substantial study. Machine learning methods were used to cut down on the time and money needed for extensive experimental testing. The database includes 135 values for CS with eleven input variables. There is an acceptable degree of agreement between predicted and experimental values, as shown by the CS R2 values of 0.94 for GEP and 0.96 for RF. When comparing RF with GEP, RF performed better as measured by R2. The lower values displayed by the statistical error also showed that RF performed better than GEP. To compare, the GEP model's COV, MAE, RSME, and RMSLE were 0.527, 1.569, 2.706, and 0.133, whereas those for RF were 0.450, 1.648, 2.17, and 0.092. The SHAP analysis showed the effects of each input parameter, illuminating the positive effect of increasing the superplasticizer content on strength and the negative effect of raising the water-to-binder ratio. Using machine learning approaches to forecast the CS of PAC, this study has the potential to boost environmental protection and economic advantage.

Investigating the corrosion performance of Ti-6Al-4V biomaterial alloy with hydroxyapatite coating by artificial neural network