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.     

Predictive modeling of the lateral drift capacity of circular reinforced concrete columns using an evolutionary algorithm

The drift capacity of reinforced concrete (RC) columns is a crucial factor in displacement and seismic based design procedure of RC structures, since they might be able to withstand the loads or dissipate the energy applied through deformation and ductility. Considering the high costs of testing methods for observing the drift capacity and ductility of RC structural members in addition to the impact of numerous parameters, numerical analyses and predictive modeling techniques have very much been appreciated by researchers and engineers in this field. This study is concerned with providing an alternative approach, termed as linear genetic programming (LGP), for predictive modeling of the lateral drift capacity (Δ_max) of circular RC columns. A new model is developed by LGP incorporating various key variables existing in the experimental database employed and those well-known models presented by various researchers. The LGP model is examined from various perspectives. The comparison analysis of the results with those obtained by previously proposed models confirm the precision of the LGP model in estimation of the Δ_max factor. The results reveal the fact that the LGP model impressively outperforms the existing models in terms of predictability and performance and can be definitely used for further engineering purposes. These approve the applicability of LGP technique for numerical analysis and modeling of complicated engineering problems.

     

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.     

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.     

A computational intelligence‐based approach for short‐term traffic flow prediction

This paper proposes alternative approaches for the prediction of short‐term traffic flow using three branches of computational intelligence techniques, namely linear genetic programming (LGP), multilayer perceptron (MLP) and fuzzy logic (FL). Different LGP, MLP and FL models are developed for estimating the 5‐ and 30‐min traffic flow rates. New LGP‐ and MLP‐based prediction equations are derived for the traffic flow rates in the 5‐ and 30‐min time intervals. The models are established upon extensive databases of the traffic flow records obtained from Iran's Rasht‐Qazvin highway. The results indicate that the proposed models are effectively capable of predicting the target values. The LGP‐based models are found to be simple, straightforward and more practical for predictive purposes compared with the other derived models.     

A linear genetic programming approach for the prediction of solar global radiation

In this article, the linear genetic programming (LGP) is utilized to predict the solar global radiation. The solar radiation is formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years (1995–2000) in two nominal cities in Iran are used to develop LGP-based models. Separate models are established for each city. To verify the performance of the proposed models, they are applied to estimate the solar global radiation of test data of database. The contribution of the parameters affecting the solar radiation is evaluated through a sensitivity analysis. The results indicate that the LGP models give precise estimations of the solar global radiation and significantly outperform traditional angstrom’s model.     

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP effectively combines the model structure selection ability of the standard GP with the parameter estimation power of classical regression to capture the nonlinear interactions. The capabilities of MGGP are illustrated by applying it to the formulation of various complex structural engineering problems. The problems analyzed herein include estimation of: (1) compressive strength of high-performance concrete (2) ultimate pure bending of steel circular tubes, (3) surface roughness in end-milling, and (4) failure modes of beams subjected to patch loads. The derived straightforward equations are linear combinations of nonlinear transformations of the predictor variables. The validity of MGGP is confirmed by applying the derived models to the parts of the experimental results that are not included in the analyses. The MGGP-based equations can reliably be employed for pre-design purposes. The results of MSGP are found to be more accurate than those of solutions presented in the literature. MGGP does not require simplifying assumptions in developing the models.     

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。     

Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm

Failure mode (FM) and bearing capacity of reinforced concrete (RC) columns are key concerns in structural design and/or performance assessment procedures. The failure types, i.e., flexure, shear, or mix of the above two, will greatly affect the capacity and ductility of the structure. Meanwhile, the design methodologies for structures of different failure types will be totally different. Therefore, developing efficient and reliable methods to identify the FM and predict the corresponding capacity is of special importance for structural design/assessment management. In this paper, an intelligent approach is presented for FM classification and bearing capacity prediction of RC columns based on the ensemble machine learning techniques. The most typical ensemble learning method, adaptive boosting (AdaBoost) algorithm, is adopted for both classification and regression (prediction) problems. Totally 254 cyclic loading tests of RC columns are collected. The geometric dimensions, reinforcing details, material properties are set as the input variables, while the failure types (for classification problem) and peak capacity forces (for regression problem) are set as the output variables. The results indicate that the model generated by the AdaBoost learning algorithm has a very high accuracy for both FM classification (accuracy = 0.96) and capacity prediction (R² = 0.98). Different learning algorithms are also compared and the results show that ensemble learning (especially AdaBoost) has better performance than single learning. In addition, the bearing capacity predicted by the AdaBoost is also compared to that by the empirical formulas provided by the design codes, which shows an obvious superior of the proposed method. In summary, the machine learning technique, especially the ensemble learning, can provide an alternate to the conventional mechanics-driven models in structural design in this big data time.     

Computer simulation of the scatter in steel member strength

A Monte Carlo method for digital computer simulation of the strength of (steel) members and structures is presented and is applied to rolled steel beams and columns, and thin-walled cylinders. Input data are cumulative distribution functions (histograms) for the geometric and strength variables. The output (i.e. the scatter in structural strength) is printed as histograms and is statistically analysed.Each output histogram is compared with the Gaussian normal distribution. Using the nonparametric test of homogeneity a number of histograms may then be compared.The case studies presented deal with the plastic strength of steel beams and the maximum load of axially loaded steel columns and thin-walled cylinders. Mathematical models for beams subject to pure bending moment, moment and axial force, moment and shear, or uniform torsion are presented. For the initially straight, centrally loaded column a tangent modulus theory which considers residual stresses is used.The simulations have been carried out for one HEA beam, four HEB beams and three IPE beams. Comparison of the simulation results show that the scatter in load carrying capacity of the simulated beams and columns can be regarded as normally distributed, that the load carrying capacity of beams and columns of the same group (HEB or IPE) and beams and columns of the groups HEA and HEB have distributions which differ very little from each other, and that the scatter in simulated beam strength, and in simulated column strength for short and medium length columns, is much more affected by the variation in yield strength of the material than by the variation in cross sectional data. This conclusion holds for ordinary distributions in yield strength of structural carbon steel.Comparisons of simulation results and test results show good agreement for the beams. The agreement is not so good for the columns mainly because in the tangent modulus theory it is assumed that the columns are initially straight. For the cylinders excellent agreement was achieved.The experience gained with the simulation system presented here shows that a medium size computer can be economically used to simulate a relatively large number of plays.     

火灾下超高性能混凝土梁斜截面承载性能

为探究火灾下超高性能混凝土(ultra high performance concrete,UHPC)梁斜截面承载性能的退化与损伤演化规律,采用Abaqus建立16个UHPC梁的热-力耦合分析模型,选择剪跨比、载荷水平、配箍率、箍筋配置方式、纵筋配筋率等作为考察参数,通过与试验结果对比验证模型的正确性.火灾下UHPC梁斜截面承载性能削减严重,其破坏延性优于普通混凝土梁.载荷水平和箍筋配置方式是影响UHPC梁耐火极限的主要因素:随着载荷水平增大,耐火极限降低;配置箍筋可以提高试验梁在火灾下的延性,但降低其耐火极限.     

Computer-aided prediction of the Al2O3 nanoparticles’ effects on tensile strength and percentage of water absorption of concrete specimens

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Al₂O₃ nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed-forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, Age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing Al₂O₃ nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Al₂O₃ nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained, and in genetic programming model, when 4 gens was selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.     

Multi-objective optimization of fiber reinforced composite laminates for strength,stiffness and minimal mass

We present a methodology for the multi-objective optimization of laminated composite materials that is based on an integer-coded genetic algorithm. The fiber orientations and fiber volume fractions of the laminae are chosen as the primary optimization variables. Simplified micromechanics equations are used to estimate the stiffnesses and strength of each lamina using the fiber volume fraction and material properties of the matrix and fibers. The lamina stresses for thin composite coupons subjected to force and/or moment resultants are determined using the classical lamination theory and the first-ply failure strength is computed using the Tsai–Wu failure criterion. A multi-objective genetic algorithm is used to obtain Pareto-optimal designs for two model problems having multiple, conflicting, objectives. The objectives of the first model problem are to maximize the load carrying capacity and minimize the mass of a graphite/epoxy laminate that is subjected to biaxial moments. In the second model problem, the objectives are to maximize the axial and hoop rigidities and minimize the mass of a graphite/epoxy cylindrical pressure vessel subject to the constraint that the failure pressure be greater than a prescribed value.     

Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms

Circular failure can be seen in weak rocks, the slope of soil, mine dump, and highly jointed rock mass. The challenging issue is to accurately predict the safety factor (SF) and the behavior of slopes. The aim of this study is to offer advanced and accurate models to predict the SF of slopes through machine learning methods improved by optimization algorithms. To this view, three different methods, i.e., trial and error (TE) method, gravitational search algorithm (GSA), and whale optimization algorithm (WOA) were used to investigate the proper control parameters of least squares support vector machine (LSSVM) method. In the constructed LSSVM-TE, LSSVM-GSA and LSSVM-WOA methods, six effective parameters on the SF, such as pore pressure ratio and angle of internal friction, were used as the input parameters. The results of the error criteria indicated that both GSA and WOA can improve the performance prediction of the LSSVM method in predicting the SF. However, the LSSVM-WOA method, with root mean square error of 0.141, performed better than the LSSVM-GSA with root mean square error of 0.170.

     

Prediction of Online Students Performance by Means of Genetic Programming

ABSTRACT

Problem: Online higher education (OHE) failure rates reach 40% worldwide. Prediction of student performance at early stages of the course calendar has been proposed as strategy to prevent student failure.

Objective: To investigate the application of genetic programming (GP) to predict the final grades (FGs) of online students using grades from an early stage of the course as the independent variable

Method: Data were obtained from the learning management system; we performed statistical analyses over FGs as dependent variable and 11 independent variables; two statistical and one GP models were generated; the prediction accuracies of the models were compared by means of a statistical test.

Results: GP model was better than statistical models with confidence levels of 90% and 99% for the training testing data sets respectively. These results suggest that GP could be implemented for supporting decision making process in OHE for early student failure prediction.     

Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset

Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60 microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results, and their comparison with the ones obtained by Linear Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in the “best” solutions found by genetic programming are presented.     

Automatic modeling of a gas turbine using genetic programming: An experimental study

This work deals with the analysis and prediction of the behavior of a gas turbine (GT), the Mitsubishi single shaft Turbo-Generator Model MS6001, which has a 30 MW generation capacity. GTs such as this are of great importance in industry, as drivers of gas compressors for power generation. Because of their complexity and their execution environment, the failure rate of GTs can be high with severe consequences. These units are subjected to transient operations due to starts, load changes and sudden stops that degrade the system over time. To better understand the dynamic behavior of the turbine and to mitigate the aforementioned problems, these transient conditions need to be analyzed and predicted. In the absence of a thermodynamic mathematical model, other approaches should be considered to construct representative models that can be used for condition monitoring of the GT, to predict its behavior and detect possible system malfunctions. One way to derive such models is to use data-driven approaches based on machine learning and artificial intelligence. This work studies the use of state-of-the-art genetic programming (GP) methods to model the Mitsubishi single shaft Turbo-Generator. In particular, we evaluate and compare variants of GP and geometric semantic GP (GSGP) to build models that predict the fuel flow of the unit and the exhaust gas temperature. Results show that an algorithm, proposed by the authors, that integrates a local search mechanism with GP (GP-LS) outperforms all other state-of-the-art variants studied here on both problems, using real-world and representative data recorded during normal system operation. Moreover, results show that GP-LS outperforms seven other modeling techniques, including neural networks and isotonic regression, confirming the importance of GP-based algorithms in this domain.     

Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels

There are many studies on the hydraulic analysis of steady uniform flows in compound open channels. Based on these studies, various methods have been developed with different assumptions. In general, these methods either have long computations or need numerical solution of differential equations. Furthermore, their accuracy for all compound channels with different geometric and hydraulic conditions may not be guaranteed. In this paper, to overcome theses limitations, two new and efficient algorithms known as linear genetic programming (LGP) and M5 tree decision model have been used. In these algorithms, only three parameters (e.g., depth ratio, coherence, and ratio of computed total flow discharge to bankfull discharge) have been used to simplify its applications by hydraulic engineers. By compiling 394 stage-discharge data from laboratories and fields of 30 compound channels, the derived equations have been applied to estimate the flow conveyance capacity. Comparison of measured and computed flow discharges from LGP and M5 revealed that although both proposed algorithms have considerable accuracy, LGP model with R ² = 0.98 and RMSE = 0.32 has very good performance.     

Model Learning with Local Gaussian Process Regression

Precise models of robot inverse dynamics allow the design of significantly more accurate, energy-efficient and compliant robot control. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time model online learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive learning performance for high-dimensional data while being sufficiently fast for real-time learning. The effectiveness of LGP is exhibited by a comparison with the state-of-the-art regression techniques, such as GPR, LWPR and ν-support vector regression. The applicability of the proposed LGP method is demonstrated by real-time online learning of the inverse dynamics model for robot model-based control on a Barrett WAM robot arm.     

关节锁定空间机械臂负载操作能力评估与轨迹规划

为了使单关节锁定空间机械臂继续执行负载任务,提出关节锁定空间机械臂负载操作能力评估方法及轨迹规划策略.首先,将动态负载能力分析方法与蒙特卡洛法相结合建立动态负载能力容错工作空间,该空间可以直观反映关节锁定空间机械臂负载操作能力及可达性;然后,栅格化该空间,并在代价函数中加入负载能力项以改进A*算法进行搜索轨迹;最后,通过仿真验证关节锁定空间机械臂负载能力评估及轨迹规划方法的正确性,所得轨迹平均带载能力比任务要求高42.5%.