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Mathematical formulation for predicting moisture damage indices of asphalt mixtures treated with sustainable waste plastic modifiers using gene expression programming

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CONSTRUCTION AND BUILDING MATERIALS 2024年 426卷

作者： Haider, Safeer Nawaz, Muhammad Naqeeb Hafeez, Imran Nawaz, Muhammad Muneeb Azab, Marc Hassan, Moavia Univ Engn & Technol Taxila Taxila Inst Transportat Engn Taxila Pakistan Natl Univ Sci & Technol NUST Inst Civil Engn Islamabad Pakistan Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Univ Engn & Technol Taxila Comp Sci Dept Taxila Pakistan

Flexible pavements are susceptible to rutting, fatigue, and moisture damage failures. As a sustainable approach, waste plastic modifiers have been used to improve the moisture damage resistance of asphalt mixtures. However, assessing moisture damage indices such as tensile strength ratio (TSR), rut depth (RD), and loss in Marshal stability (LI) for waste plastic modified asphalt mixtures (WPMAM) via traditional lab methods is timeconsuming and resource intensive. As a solution, this study suggests a gene expression programming (GEP) approach for predicting moisture damage indices of WPMAM. The models were constructed based on the outcomes from qualitative lab tests on loose coated asphalt specimens, including the static water immersion test (SWIT), rolling bottle test (RBT), and boiling water test (BWT). The proposed models demonstrated robust performance with high R values and favorable low error metrics. TSR achieved 0.99 (training) and 0.98 (testing) R values, while RD and LI displayed strong predictive accuracy with 0.95, 0.93 (training), and 0.94, 0.77 (testing) R values. Notably, lower root means square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) values reinforce the models ' reliability. Sensitivity analysis identified RBT as the most influential factor for TSR, RD, and LI of WPMAM. Finally, parametric analysis verified the alignment of proposed models with underlying physical processes. Thus, these models can be successfully implemented for predicting moisture damage indices of WPMAM, offering a streamlined alternatives to labor-intensive laboratory procedures, making them valuable for optimizing material choices and implementing preventive measures in construction.

关键词： Flexible pavement Sustainable waste plastic modifiers gene expression programming Moisture damage indices Prediction models

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Prediction performance of PEM fuel cells by gene expression programming

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2012年第24期37卷 18972-18980页

作者： Nazari, Ali Islamic Azad Univ Saveh Branch Dept Mat Engn Saveh Iran

In the present study, gene expression programming has been utilized to evaluate the output voltage of different PEM fuel cells as the performance symbol of these structures. A total number of 843 data were collected from the literature, randomly divided into 682 and 161 sets, and then trained and tested, respectively by different models. The used data as input parameters were consisted of current density, fuel cell temperature, anode humidification temperature, cathode humidification temperature, operating pressures, fuel cell type, 02 flow rate, air flow rate and active surface area of the PEM fuel cells. According to these input parameters, in the gene expression programming models, the voltage of each PEM fuel cell in different conditions was predicted. The training and testing results in the gene expression programming model have shown an acceptable potential for predicting voltage values of the PEM fuel cells in the considered range. Copyright (C) 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： PEM fuel cell Voltage gene expression programming Modeling

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Prediction of compressive and splitting tensile strength of concrete with fly ash by using gene expression programming

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STRUCTURAL CONCRETE 2022年第4期23卷 2435-2449页

作者： Shah, Hammad Ahmed Rehman, Sardar Kashif Ur Javed, Muhammad Faisal Iftikhar, Yusra Cent South Univ Sch Civil Engn Changsha Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan

The mechanical properties of concrete are one of the most important properties in a design code. Accurate prediction models for mechanical properties are always desirable. Given environmental benefits, fly ash (FA) is often used as a replacement for Portland cement. In this study, two mechanical properties of concrete with FA, that is, compressive strength f 0 co THORN and splitting tensile strength f sto THORN were modeled by using gene expression programming (GEP). The GEP models were compared with response surface methodology, multiple linear and nonlinear regression. The sensitivity and parametric analysis were also performed. In addition, the influence of parameters of the GEP algorithm on the performance of the developed GEP models was evaluated by testing various linking functions, head sizes, number of genes, number of chromosomes, and fitness functions. It was revealed that the models developed by GEP have high predictive and generalization capability for both f 0 c and f st of concrete with FA as compared with the other modeling techniques.

关键词： compressive strength fly ash gene expression programming splitting tensile strength

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Groundwater level simulation using gene expression programming and M5 model tree combined with wavelet transform

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2020年第8期65卷 1430-1442页

作者： Bahmani, Ramin Solgi, Abazar Ouarda, Taha B. M. J. INRS ETE Canada Res Chair Stat Hydroclimatol Quebec City PQ Canada Shahid Chamran Univ Ahvaz Dept Water Resources Engn Fac Water Sci Engn Ahwaz Iran

In order to understand and adequately manage hydrological stress, it is necessary to simulate groundwater levels accurately. In this research, gene expression programming (GEP) and M5 model tree (M5) are used to simulate monthly groundwater levels. The models are combined with wavelet transform to produce two hybrid models: wavelet gene expression programming (WGEP) and wavelet M5 model tree (WM5). For the simulation, groundwater level, temperature and precipitation values from three observation wells and one meteorological station, located in Iran, are used. The results indicate that the hybrid models, WGEP and WM5, lead to a better performance than the simple models, GEP and M5. The performance of the two hybrid models is similar. It is also observed that selecting a suitable time lag for inputs plays an important role in the accuracy of the simple models. The selection of a suitable decomposition level strongly affects the accuracy of hybrid models.

关键词： groundwater level gene expression programming hybrid model M5 model tree wavelet transform

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Surface EMG based handgrip force predictions using gene expression programming

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NEUROCOMPUTING 2016年 207卷 568-579页

作者： Yang, Zhongliang Chen, Yumiao Tang, Zhichuan Wang, Jianping Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China Donghua Univ Fash & Art Design Inst Shanghai 200051 Peoples R China Zhejiang Univ Modern Ind Design Inst Hangzhou 310027 Zhejiang Peoples R China

The main objective of this study is to precisely predict muscle forces from surface electromyography (sEMG) for hand gesture recognition. A robust Variant of genetic programming, namely gene expression programming (GEP), is utilized to derive a new empirical model of handgrip sEMG-force relationship. A series of handgrip forces and corresponding sEMG signals were recorded from 6 healthy male subjects and during 4 levels of percentage of maximum voluntary contraction (%MVC) in experiments. Using one-way ANOVA with multiple comparisons test, 10 features of the sEMG time domain were extracted from homogeneous subsets and used as input vectors. Subsequently, a handgrip force prediction model was developed based on GEP. In order to compare the performance of this model, other models based on a back propagation neural network and a support vector machine were trained using the same input vectors and data sets. The root mean square error and the correlation coefficient between the actual and predicted forces were calculated to assess the performance of the three models. The results show that the GEP model provide the highest accuracy and generalization capability among the studied models. It was concluded that the proposed GEP model is relatively short, simple and excellent for predicting handgrip forces based on sEMG signals. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Surface electromyography Grip force gene expression programming Force prediction

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A new model based on gene expression programming to estimate air flow in a single rock joint

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ENVIRONMENTAL EARTH SCIENCES 2016年第9期75卷 1-13页

作者： Khandelwal, Manoj Armaghani, Danial Jahed Faradonbeh, Roohollah Shirani Ranjith, P. G. Ghoraba, Saber Federat Univ Australia Fac Sci & Technol POB 663 Ballarat Vic 3353 Australia Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat 81310UTM Skudai Johor Malaysia Tarbiat Modares Univ Dept Min Engn Tehran 14115143 Iran Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia Islamic Azad Univ Tehran South Branch Tehran Iran

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.

关键词： Fractured rock Air flow rate gene expression programming Multiple regression

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On the evaluation of crude oil oxidation during thermogravimetry by generalised regression neural network and gene expression programming: application to thermal enhanced oil recovery

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COMBUSTION THEORY AND MODELLING 2021年第7期25卷 1268-1295页

作者： Mohammadi, Mohammad-Reza Hemmati-Sarapardeh, Abdolhossein Schaffie, Mahin Husein, Maen M. Karimian, Milad Ranjbar, Mohammad Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran Univ Calgary Dept Chem & Petr Engn Calgary AB Canada Razi Univ Dept Chem Engn Kermanshah Iran

Enhancing oil recovery using in-situ combustion (ISC) is an attractive alternative, especially for heavy crudes. During ISC, part of the hydrocarbon is pyrolysed/oxidised, which generates heat and deposits fuel in the combustion front. In this study, crude reactions during ISC are modelled after their thermogravimetry thermo-oxidative profiles using advanced machine learning systems. The model inputs include the weight per cent of asphaltenes, resins, and degrees API gravity of the oil as well as the heating rate and the temperature. Four types of artificial neural networks (ANNs);namely multilayer perceptron (MLP), generalised regression neural network (GRNN), cascade-forward neural network (CFNN), and radial basis function (RBF) neural network, were employed to develop models for accurate prediction of the weight per cent of residual crude oil based on 2289 experimental data points. Moreover, three optimisation algorithms;including Bayesian Regularisation (BR), Levenberg-Marquardt (LM), and Scaled Conjugate Gradient (SCG) were applied in the training step of MLP and CFNN to improve the prediction ability. GRNN provided the most accurate prediction with similar to 2.3% overall average absolute per cent relative error and coefficient of determination of 0.9983. GRNN model is reliable for crude oils with degrees API gravity of 5-35 and up to 820 degrees C. Lastly, a mathematical correlation was developed to estimate the residual crude oil from thermogravimetry analysis using gene expression programming (GEP). GEP also predicted the thermo-oxidative profile with high accuracy. On the basis of sensitivity analysis, residue formation during crude oil oxidation was impacted the most by the temperature, oil degrees API gravity, and asphaltenes content, respectively. The Leverage approach identified 2.9% of the data points as doubtful.

关键词： in-situ combustion EOR TGA intelligent model gene expression programming generalised regression neural network

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generalizability of gene expression programming and random forest methodologies in estimating cropland and grassland leaf area index

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2018年 144卷 232-240页

作者： Karimi, Sepideh Sadraddini, Ali Ashraf Nazemi, Amir Hossein Xu, Tongren Fard, Ahmad Fakheri Univ Tabriz Fac Agr Water Engn Dept Tabriz Iran Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China

Leaf Area Index (LAI) is a very important structural attribute of ecosystems which affects the energy, water and carbon exchanges between the land surface and atmosphere. Direct measurement of LAI is costly and time consuming so indirect measurement approaches have been developed for determining its magnitude. The present paper aimed at modeling LAI in cropland and grassland sites using the available meteorological data through two heuristic data driven techniques, namely, gene expression programming (GEP) and random forest (RF). Different data set organizations were designed using local (temporal) and external (spatial) norms to provide a thoroughgoing data scanning strategy. The results showed that the external GEP and RF models (EGEP and ERF) might be suitable approaches for modeling LAI by average scatter index (SI) values of 0.275 and 0.270 (for cropland) and 0.273 and 0.279 (for grassland) when compared to the local GEP and RF models with average SI values of 0.207 and 0.204 (cropland), and 0.249 and 0.204 (grassland), respectively. The presented methodology allowed the evaluation in each site of models developed (trained) using local patterns and the models developed using the exogenous data (patterns from ancillary sites).

关键词： Meteorological data Leaf area index gene expression programming Random forest

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An improved gene expression programming model for streamflow forecasting in intermittent streams

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JOURNAL OF HYDROLOGY 2018年 563卷 669-678页

作者： Mehr, Ali Danandeh Antalya Bilim Univ Dept Civil Engn Antalya Turkey

Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology. In this study, genetic algorithm (GA) was combined with gene expression programming (GEP) as a new hybrid model for month ahead streamflow forecasting in an intermittent stream. The hybrid model was named GEP-GA in which sub-expression trees of the best evolved GEP model were rescaled by appropriate weighting coefficients through the use of GA optimizer. Auto-correlation and partial auto-correlation functions of the streamflow records as well as evolutionary search of GEP were used to identify the optimum predictors (i.e., number of lags) for the model. The proposed methodology was demonstrated using monthly streamflow data from the Shavir Creek in Iran. Performance of the GEP-GA was compared to that of classic genetic programming (GP), GEP, multiple linear regression and GEP-linear regression models developed in the present study as the benchmarks. The results showed that the GEP-GA outperforms all the benchmarks and motivated to be used in practice.

关键词： Streamflow forecasting gene expression programming genetic algorithm Evolutionary optimization Intermittent streams

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Prediction of creep index of soft clays using gene expression programming

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SOFT COMPUTING 2023年第21期27卷 16265-16278页

作者： Xue, Xinhua Deng, Chubing Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

The creep index plays an important role in calculating the long-term settlement of natural soft clays, so it is vital to determine the creep index quickly and accurately. However, the prediction accuracy of the existing creep index models is low. This study presents seven gene expression programming (GEP) models by using different combinations of the liquid limit w(L), plasticity index I-p, void ratio e and clay content CI as input variables for the prediction of creep index. A total of 151 datasets were collected from the available literature for building and testing the GEP models. The proposed GEP models were compared with two machine learning (ML) models (i.e., back propagation neural network and random forest) and five conventional empirical models in terms of three statistical indicators. The research results showed that the prediction performances of the two proposed GEP models (i.e., with combinations CI-w(L)-e and CI-I-p-w(L)-e as input, respectively) surpass those of the five conventional empirical models and two ML-based models, recommended for predicting the creep index of natural soft clays in engineering practice.

关键词： gene expression programming Creep index Plasticity index Liquid limit Void ratio Clay content

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