Nonlinear modeling of soil deformation modulus through LGP-based interpretation of pressuremeter test results

Soil deformation modulus is an essential parameter for the analysis of behavior of substructures. Despite its importance, little attention is paid to developing empirical models for predicting the deformation moduli obtained from the field tests. To cope with this issue, this paper presents the development of a new prediction model for the pressuremeter soil deformation modulus utilizing a linear genetic programming (LGP) methodology. The LGP model relates the soil secant modulus obtained from the pressuremeter tests to the soil index properties. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon several pressuremeter tests conducted on different soil types at depths of 3–40 m. To verify the applicability of the derived model, it was employed to estimate the soil moduli of portions of test results that were not included in the analysis. Further, the generalization capability of the model was verified via several statistical criteria. The sensitivity of the soil deformation modulus to the influencing variables was examined and discussed. Moisture content and soil dry unit weight were found to be efficient representatives of the initial state and consolidation history of the soil for determining its deformation modulus. The results indicate that the LGP approach accurately characterizes the soil deformation modulus leading to a very good prediction performance. The correlation coefficients between the experimental and predicted soil modulus values are equal to 0.908 and 0.901 for the calibration and testing data sets, respectively.     

Genetic-based modeling of uplift capacity of suction caissons

In this study, classical tree-based genetic programming (TGP) and its recent variants, namely linear genetic programming (LGP) and gene expression programming (GEP) are utilized to develop new prediction equations for the uplift capacity of suction caissons. The uplift capacity is formulated in terms of several inflecting variables. An experimental database obtained from the literature is employed to develop the models. Further, a conventional statistical analysis is performed to benchmark the proposed models. Sensitivity and parametric analyses are conducted to verify the results. TGP, LGP and GEP are found to be effective methods for evaluating the horizontal, vertical and inclined uplift capacity of suction caissons. The TGP, LGP and GEP models reach a prediction performance better than or comparable with the models found in the literature.     

基因表达式编程在软件可靠性建模中的应用

基因表达式编程是一种基于遗传算法和遗传编程的新型机器学习技术,其具有更为优秀的数据挖掘能力,已被成功应用于函数发现领域。提出一种基于基因表达式编程的非参软件可靠性建模方法,该方法将基因表达式编程算法中的若干关键步骤(如初始种群函数集、适应度函数、终止条件等)与软件可靠性建模的若干重要特征相融合,在失效数据集上进行训练,从而获得基于基因表达式编程算法的非参软件可靠性模型。在若干组真实失效数据集上,将所提出的模型与若干典型的基于人工神经网络以及遗传编程的非参软件可靠性模型进行对比实例研究。实例结果表明,基因表达式编程算法的非参软件可靠性模型具有更为显著的模型拟合与预计性能。     

Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy

In this study, the efficiency of neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the transfer length of prestressing strands in prestressed concrete beams was investigated. Many models suggested for the transfer length of prestressing strands usually consider one or two parameters and do not provide consistent accurate prediction. The alternative approaches such as GEP and ANFIS have been recently used to model spatially complex systems. The transfer length data from various researches have been collected to use in training and testing ANFIS and GEP models. Six basic parameters affecting the transfer length of strands were selected as input parameters. These parameters are ratio of strand cross-sectional area to concrete area, surface condition of strands, diameter of strands, percentage of debonded strands, effective prestress and concrete strength at the time of measurement. Results showed that the ANFIS and GEP models are capable of accurately predicting the transfer lengths used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.     

Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively.

     

A new predictive model for compressive strength of HPC using gene expression programming

In this study, gene expression programming (GEP) is utilized to derive a new model for the prediction of compressive strength of high performance concrete (HPC) mixes. The model is developed using a comprehensive database obtained from the literature. The validity of the proposed model is verified by applying it to estimate the compressive strength of a portion of test results that are not included in the analysis. Linear and nonlinear least squares regression analyses are performed to benchmark the GEP model. Contributions of the parameters affecting the compressive strength are evaluated through a sensitivity analysis. GEP is found to be an effective method for evaluating the compressive strength of HPC mixes. The prediction performance of the optimal GEP model is better than the regression models.     

Soft computing for prediction of river pipeline scour depth

This study presents gene-expression programming (GEP) as an alternative soft computing tool for the prediction of scour below underwater pipeline across river. Actual laboratory measurements were used for the model development. The scour depth was formulated in terms of several influencing parameters. The results indicate that GEP is a very promising approach to predict the river pipeline scour depth.     

Robust genetic programming‐based detection of atrial fibrillation using RR intervals

In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi‐expression programming (MEP) are utilized to detect atrial fibrillation (AF) episodes. LGP‐ and MEP‐based models are derived to classify samples of AF and Normal episodes based on the analysis of RR interval signals. A weighted least‐squares (WLS) regression analysis is performed using the same features and data sets to benchmark the models. Another important contribution of this paper is identification of the effective time domain features of heart rate variability (HRV) signals upon an improved forward floating selection (IFFS) analysis. The models are developed using MIT‐BIH arrhythmia database. The diagnostic performances of the LGP and MEP classifiers are evaluated through receiver operating characteristics (ROC) analysis. The results indicate that the LGP and MEP models are able to diagnose the AF arrhythmia with an acceptable high accuracy. The proposed models have significantly better diagnosis performances than the regression and several models found in the literature.     

Short-term load forecasting of power systems by gene expression programming

Short-term load forecasting is a popular topic in the electric power industry due to its essentiality in energy system planning and operation. Load forecasting is important in deregulated power systems since an improvement of a few percentages in the prediction accuracy will bring benefits worth of millions of dollars. In this study, a promising variant of genetic programming, namely gene expression programming (GEP), is utilized to improve the accuracy and enhance the robustness of load forecasting results. With the use of the GEP technique, accurate relationships were obtained to correlate the peak and total loads to average, maximum and lowest temperatures of day. The presented model is applied to forecast short-term load using the actual data from a North American electric utility. A multiple least squares regression analysis was performed using the same variables and same data sets to benchmark the GEP models. For more verification, a subsequent parametric study was also carried out. The observed agreement between the predicted and measured peak and total load values indicates that the proposed correlations are capable of effectively forecasting the short-term load. The GEP-based formulas are relatively short, simple and particularly valuable for providing an analysis tool accessible to practicing engineers.     

Prediction of splitting tensile strength from the compressive strength of concrete using GEP

Splitting tensile strength is one of the important mechanical properties of concrete that is used in structural design. In this paper, it is aimed to propose formulation for predicting cylinder splitting tensile strength of concrete by using gene expression programming (GEP). The database used for training, testing, and validation sets of the GEP models is obtained from the literature. The GEP formulations are developed for prediction of splitting tensile strength of concrete as a function of water-binder ratio, age of specimen, and 100-mm cube compressive strength. The training and testing sets of the GEP models are randomly selected from the complete experimental data. The GEP formulations are also validated with additional experimental data except from the data used in training and testing sets of the GEP models. GEP formulations’ results are compared with experimental results. Results of this study revealed that GEP formulations exhibited better performance to predict the splitting tensile strength of concrete.     

Prediction of California bearing ratio (CBR) of fine grained soils by AI methods

Advances in field of artificial intelligence (AI) offers opportunities of utilizing new algorithms and models that enable researchers to solve the most complex systems. As in other engineering fields, AI methods have widely been used in geotechnical engineering. Unlikely, there seems quite insufficient number of research related to the use of AI methods for the estimation of California bearing ratio (CBR). There were actually some attempts to develop prediction models for CBR, but most of these models were essentially statistical correlations. Nevertheless, many of these statistical correlation equations generally produce unsatisfactory CBR values. However, this paper is likely one of the very first research which aims to investigate the applicability of AI methods for prediction of CBR. In this context, artificial neural network (ANN) and gene expression programming (GEP) were applied for the prediction of CBR of fine grained soils from Southeast Anatolia Region/Turkey. Using CBR test data of fine grained soils, some proper models are successfully developed. The results have shown that the both ANN and GEP are found to be able to learn the relation between CBR and basic soil properties. Additionally, sensitivity analysis is performed and it is found that maximum dry unit weight (γ_d) is the most effective parameter on CBR among the others such as plasticity index (PI), optimum moisture content (w_opt), sand content (S), clay + silt content (C + S), liquid limit (LL) and gravel content (G) respectively.     

Flexural buckling load prediction of aluminium alloy columns using soft computing techniques

This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling. Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft computing models are compared with existing codes and are found to be more accurate.     

Analysis of gene expression programming for approximation in engineering design

To reduce the computational cost of implementing computer-based simulations and analyses in engineering design, a variety of metamodeling techniques have been developed and used for the construction of metamodels. Metamodels, also called approximation models and surrogate models, can be used to make a replacement of the expensive simulation codes for design and optimization. In this paper, gene expression programming (GEP) algorithm in the evolutionary computing area is investigated as an alternative metamodeling technique to provide the approximation of a design space. The approximation performance of GEP is tested on some low-dimensional mathematical and engineering problems. A comparative study is conducted on GEP and three common metamodeling techniques in engineering design (i.e., response surface methodology (RSM), kriging and radial basis functions (RBF)) for the approximation of the low-dimensional design space. Multiple evaluation criteria are considered in the comparison: accuracy, robustness, transparency and efficiency. Two different sample sizes are adopted: small and large. Comparative results indicate that GEP can achieve the most accurate and robust approximation of a low-dimensional design space for small sample sets. For large sample sets, GEP also presents good prediction accuracy and high robustness. Moreover, the transparency of GEP is the best since it can provide clear function relationships and factor contributions by means of compact expressions. As a novel metamodeling technique, GEP shows great promise for metamodeling applications in a low-dimensional design space, especially when only a few sample points are selected and used for training.     

Gene expression programming based due date assignment in a simulated job shop

In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method dominates all other compared due date assignment methods.     

A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming

Highly nonlinear flow behavior of cement-based grout mixtures has always become an important issue for experimenters during jet grouting applications. In this viewpoint, an investigation has been addressed in this paper on the applicability of a recent soft computing prediction tool, genetic expression programming (GEP), to the prediction of rheological characteristics (i.e., shear stress, viscosity) of the grout mixtures with various stabilizers (clay, sand, lime) for jet grouting purposes. The experimental data (shear stress versus shear rate with respect to stabilizer dosages) of grout mixtures obtained from rheometer tests have been collected from previous study conducted in a wide range of stabilizer dosage rates (0–100 %, by dry weight of binder). For predicting the shear stress and viscosity as the output variables during the train and testing stages, the input variables in the GEP models included shear rate and stabilizer dosage primarily. As a consequence of GEP modeling compared with measured data, this study reveals satisfactory GEP formulations in a good accuracy (R ≥ 0.86) for predictions of shear stress and viscosity regarding the stabilizer additions. The GEP formulas are also found adequate for modeling the flow behavior of the shear stress–shear rate, alternatively to conventional nonlinear regression and rheological models (Herschel–Bulkley, Robertson–Stiff). For assistance of preliminary evaluations, the derived GEP formulas could be potentially considered in practice for estimations of pumping pressure (shear stress), pumping rate (shear rate) and viscosity of jet grout mixtures.

     

Application of gene expression programming in hot metal forming for intelligent manufacturing

Design of the die in hot metal forming operations depends on the required forming load. There are several approaches in the literature for load prediction. Artificial neural networks (ANNs) have been successfully used by a few researches to estimate the forming loads. This paper aims at using the effectiveness of a new evolutionary approach called gene expression programming (GEP) for the estimation of forging load in hot upsetting and hot extrusion processes. Several parameters such as angle (α), L/D ratio (R), friction coefficient (µ), velocity (v) and temperature (T) were used as input parameters. The accuracy of the developed GEP models was also compared with ANN models. This comparison was evidenced by some statistical measurements (R ², RMSE, MAE). The outcomes of the study showed that GEP can be used as an effective tool for representing the complex relationship between the input and output parameters of hot metal forming processes.

     

Gene expression programming as a basis for new generation of electricity demand prediction models

This study proposes a new gene expression programming (GEP) approach for the prediction of electricity demand. The annual population, gross domestic product, stock index, and total revenue from exporting industrial products were used to predict the electricity demand of the same year in Thailand. Several statistical criteria were used to verify the validity of the model. Further, the contributions of the influencing variables to the prediction of the electricity demand were analyzed. Correlation coefficient, root mean squared error and mean absolute percent error were used to evaluate the performance of the model. In addition to its high accuracy, the derived model outperforms regression and other soft computing-based models.     

Development of GEP-based functional relationship for sediment transport in tropical rivers

This study presents gene expression programming (GEP), which is an extension to genetic programming (GP), as an alternative approach to modeling the functional relationships for the River Kurau, River Langat, and River Muda of the Malaysia. A functional relation has been developed using GEP with non-dimensional variables. The development of a GEP non-dimensional model is described. This paper compares current prediction equation with the existing GEP model for the same rivers (Zakaria et al. in Sci Total Environ 408:5078–5085, (2010). The presented model in this study is a less input GEP model and that predicts good performance. The proposed GEP approach gives satisfactory results compared to existing predictors.     

基于改进的基因表达式编程的复杂函数建模

介绍了基因表达式程序设计方法的基本原理,针对求解复杂函数模型反问题中经典GEP算法多样性表现不足,甚至出现早熟的问题,提出了一种基于动态变异算子的改进的GEP算法——IGEP算法,从理论上对该改进算法进行了复杂度分析和收敛性分析。通过求解复杂函数模型反问题的多个实验将改进算法与传统方法、神经网络方法、经典GEP算法进行了对比,结果表明：该方法建立的复杂函数反问题拟合模型比经典GEP方法、传统方法、神经网络方法得到的模型更加优秀。     

Uniaxial compressive strength prediction through a new technique based on gene expression programming

Proper estimation of rock strength is a critical task for evaluation and design of some geotechnical applications such as tunneling and excavation. Uniaxial compressive strength (UCS) test can be measured directly in the laboratory; nevertheless, the direct UCS determination is time-consuming and expensive. In this study, feasibility of gene expression programming (GEP) model in indirect determination of UCS values of sandstone rock samples is examined. In this regard, several laboratory tests including Brazilian test, density test, slake durability test and UCS test were conducted on 47 samples of sandstone which were collected from the Dengkil, Malaysia. Considering multiple inputs, several GEP models were constructed to estimate UCS of the rock and finally, the best GEP model was selected. In order to indicate capability of the proposed GEP model, linear multiple regression (LMR) was also performed. It was found that the GEP model is superior to LMR one in terms of applied performance indices. Based on coefficient of determination (R ²) of testing datasets, by proposing GEP model, it can be improved from 0.930 (which was obtained by LMR model) to 0.965. As a result, it is concluded that the proposed models in this study, could be utilized to estimate UCS of similar rock type in practice.