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

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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gene expression programming as a data classification tool. A review

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期36卷 91-100页

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Fac Math Phys & Informat Inst Informat PL-80308 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst Gdynia Poland

The paper reviews classification algorithms based on gene expression programming (GEP) used for mining the real-life datasets. Our aim is to show, chronologically, most important developments as well as the current state-of-the-art in the area of GEP-based classifiers, with a view to attract further real life applications. We begin with reviewing approaches to building basic, stand alone, GEP classifiers and eventually, combining them into the classifier ensemble. In the following part of the paper we describe and illustrate with example several hybrid solutions where GEP is integrated with other methods. Next, we review specialized and dedicated methods including multiple criteria and incremental GEP-based classification tools. Final part of the paper reviews specialized GEP-based classifiers used to mine the real-life datasets.

关键词： gene expression programming classification classifier ensemble

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Mathematical model for prediction of compressive strength of ternary blended cement concrete utilizing gene expression programming

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SCIENTIFIC AFRICAN 2023年 22卷

作者： Alabi, Stephen Adeyemi Arum, Chinwuba Adewuyi, Adekunle Philip Arum, Roland Chinwuba Afolayan, Joseph Olasehinde Mahachi, Jeffrey Univ Botswana Dept Civil Engn Gaborone Botswana Fed Univ Technol Akure Dept Civil Engn Akure Nigeria Univ Johannesburg Dept Civil Engn Technol Johannesburg South Africa

In order to encourage the utilization of rice husk ash (RHA), ceramic waste powder (CWP) and glass waste powder (GWP) in their ternary combinations in the production of concrete as well as determine the variability in the resulting compressive strength (CS), the application of softcomputing algorithms is of great interest. Therefore, this study developed a predictive model using the gene expression programming (GEP) technique. The predictive model was trained and developed using extensive and trustworthy data of the compressive strength of ternary blended cement concrete comprising four (4) input parameters, including the RHA, CWP, GWP, and curing day (CD). The model was developed based on 70 % training datasets, and the model's accuracy was checked using 15 % testing datasets;the model's outcomes were validated using 15 % experimental datasets. The performance of the developed model was further assessed by applying statistical checks, comparing regression models and sensitivity analysis. The R-values in the training, testing and validation phases of GEP models are 0.95, 0.93 and 0.89 respectively with an objective function (OF) of 0.04. Based on the optimum GEP model, a closed-form mathematical equation is presented, proving to have excellent adaptability, predictive ability and capability of accurately estimating the compressive strength of concrete made from ternary blended cement mixes. Therefore, the outcomes of this study can help the construction sector predict the properties of pozzolanic concrete and manage scarce resources.

关键词： Pozzolanic concrete Ternary binders Machine learning Waste management Prediction gene expression programming

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Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

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BIORESOURCES AND BIOPROCESSING 2020年第1期7卷 1-22页

作者： Abdulsalam, Jibril Lawal, Abiodun Ismail Setsepu, Ramadimetja Lizah Onifade, Moshood Bada, Samson Univ Witwatersrand Fac Engn & Built Environm Sch Chem & Met Engn DSI NRF Clean Coal Technol Res Grp ZA-2050 Johannesburg South Africa Inha Univ Dept Energy Resources Engn Incheon South Korea Fed Univ Technol Akure Dept Min Engn Akure Nigeria Univ Namibia Dept Min & Met Engn Windhoek Namibia

Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass using gene expression programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R-2), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R-2 = 0.976, 0.955, 0.996;MAE = 2.24, 2.11, 0.93;MBE = 0.16, 0.37, 0.12) for MY, EY and HHV, respectively. The GEP technique's ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

关键词： Artificial neural network Biomass gene expression programming Higher heating value Hydrochars Hydrothermal carbonization

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Drought modeling using classic time series and hybrid wavelet-gene expression programming models

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JOURNAL OF HYDROLOGY 2020年 587卷 125017-125017页

作者： Mehdizadeh, Saeid Ahmadi, Farshad Mehr, Ali Danandeh Safari, Mir Jafar Sadegh Urmia Univ Water Engn Dept Orumiyeh Iran Shahid Chamran Univ Ahvaz Dept Hydrol & Water Resources Engn Ahvaz Iran Antalya Bilim Univ Fac Engn Dept Civil Engn Antalya Turkey Yasar Univ Dept Civil Engn Izmir Turkey

The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e., SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end, two types of classic time series models, namely linear autoregressive (AR) and non-linear bi-linear (BL) are used. Furthermore, the hybrid models are proposed by coupling the wavelet (W) and gene expression programming (GEP). Five various mother wavelets (i.e., Haar, db4, Symlet, Meyer, and Coifflet), for the first time, are employed and compared for implementing the hybrid W-GEP approach in drought modeling. The modeling results of SPEI droughts via the time series models illustrated that the non-linear BL performs slightly better than the linear AR. Moreover, all the hybrid W-GEP models developed in the study region provide superior performances compared to the standalone GEP. In general, db4 in SPEI-3 modeling and Symlet for modeling the SPEI-6 and SPEI-12 of the studied locations are the optimal wavelets to develop the W-GEP. Finally, the SPEI series at each target station is modeled through applying the SPEI data of the five neighboring stations. It is found that the SPEI data of neighboring stations are appropriate for modeling the SPEI series of the target station when the SPEI data of the target station is not at hand. For this case, the performance of standalone GEP for modeling the SPEI-3 and SPEI-6 of the stations is generally better than the case of utilizing the original SPEI data at each target station.

关键词： Drought modeling Time series models Hybrid models Wavelet analysis gene expression programming

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Force-Displacement Relationship of the Butterfly-Shaped Beams Based on gene expression programming

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2020年第6期20卷 2009-2019页

作者： Farzampour, Alireza Mansouri, Iman Mortazavi, Seyed Javad Hu, Jong Wan Virginia Tech Dept Civil & Environm Engn Blacksburg VA USA Birjand Univ Technol Dept Civil Engn Birjand Iran Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran Incheon Natl Univ Dept Civil & Environm Engn Incheon 22012 South Korea Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon 22012 South Korea

Structural steel plates with engineered cut-outs to exhibit a controlled yielding mechanism is recently proposed for desirable structural performance compared to conventional systems. Butterfly-shaped beams with hexagonal cut-outs inside of the beam's web are implemented to better align the bending strength diagram along the link length with the corresponding demand shape of the applied moment diagram. In previous studies, it has been reported that these links have a substantial energy dissipation capability and sufficient ductility which necessities further investigations and structural behavior prediction studies. In this study, a set of 240 nonlinear finite element models are developed for the creation of a database and subsequently calibrated with finite element software packages. The capability of the gene expression programming (GEP) is explored for the prediction of the force-displacement relationship of a butterfly-shaped beam. Two new models are developed based on the reliable generated database. Subsequently, the proposed models are validated with several conducted analyses and statistical parameters, for which the comparisons are shown in detail. The results represent that the proposed models are able to predict the force-displacement relationship of a butterfly-shaped beam with satisfactory accuracy.

关键词： Structural fuses Butterfly-shaped beam Finite element analysis gene expression programming OpenSees

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New predictive models via gene expression programming and multiple nonlinear regression for SFRC beams

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2020年第6期9卷 14294-14306页

作者： Sharifi, Yasser Moghbeli, Adel Vali E Asr Univ Rafsanjan Dept Civil Engn Rafsanjan Iran

A soft computing based study aimed to predict the shear capacity of steel fiber RC (SFRC) beams is qualified and new formulae based on gene expression programming (GEP) and nonlinear multiple regression (NMR) are suggested. To extend the proposed formulae, extensive experimental data collected from existing studies in the literature were considered. The data utilized in the GEP and NMR models are ordered in a format of six input such as concrete compressive strength, effective depth, shear span to the effective depth ratio, the ratio of the longitudinal reinforcement, length of fiber to the diameter of itself, and fiber content by volume. The accuracy of the developed formulae is verified versus the experimental data and the rates of efficiency and performance are compared with those provided by suggested equations presented by some of the researchers. The developed models that investigated in present study demonstrated that the GEP and NMR methodologies displayed strong potential for estimating the shear capacity of SFRC beams. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Steel fiber reinforced concrete Shear strength Strength prediction gene expression programming Multiple nonlinear regression

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gene expression programming for evaluation of aggregate angularity effects on permanent deformation of asphalt mixtures

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CONSTRUCTION AND BUILDING MATERIALS 2019年 211卷 470-478页

作者： Leon, Lee P. Gay, Derek UWI Dept Civil & Environm Engn St Augustine Trinidad Tobago

The shape of the aggregate influences the particle interlocking capabilities, thus influencing the deformation behaviour of mixes. The research paper explores the use of gene-expression programming (GEP) as an unconventional approach to predict permanent deformation of asphalt concrete with the inclusion of aggregate angularity. Data for prediction of permanent deformation of dense graded asphalt concrete was obtained through laboratory experiments. The development and analysis of the proposed GEP technique was completed by using laboratory data. The new technique for the assessment of permanent deformation strain with additional angularity factor was developed. The GEP approach concluded satisfactory outcomes of the estimation of permanent deformation when compared to regression analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Aggregate angularity Permanent deformation Asphalt concrete gene expression programming

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Prediction model for concrete carbonation depth using gene expression programming

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COMPUTERS AND CONCRETE 2020年第6期26卷 497-504页

作者： Murad, Yasmin Z. Tarawneh, Bashar K. Ashteyat, Ahmed M. Univ Jordan Dept Civil Engn Amman 11942 Jordan

Concrete can lose its alkalinity by concrete carbonation causing steel corrosion. Thus, the determination of the carbonation depth is necessary. An empirical model is proposed in this research to predict the carbonation depth of concrete using gene expression programming (GEP). The GEP model was trained and validated using a large and reliable database collected from the literature. The model was developed using the six parameters that predominantly control the carbonation depth of concrete including carbon dioxide CO2 concentration, relative humidity, water-to-cement ratio, maximum aggregate size, aggregate to binder ratio and carbonation period. The model was statistically evaluated and then compared to the Jiang et al. model. A parametric study was finally performed to check the proposed GEP model's sensitivity to the selected input parameters.

关键词： carbonation depth of concrete gene expression programming Jiang model

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Information Fusion in Offspring generation: A Case Study in gene expression programming

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IEEE ACCESS 2020年 8卷 74782-74792页

作者： Liu, Tonglin Zhang, Hengzhe Zhang, Hu Zhou, Aimin Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai 200062 Peoples R China

gene expression programming (GEP), which is a variant of genetic programming (GP) with a fixed-length linear model, has been applied in many domains. Typically, GEP uses genetic operators to generate offspring. In recent years, the estimation of distribution algorithm (EDA) has also been proven to be efficient for offspring generation. genetic operators such as crossover and mutation generate offspring from an implicit model by using the individual information. By contrast, EDA operators generate offspring from an explicit model by using the population distribution information. Since both the individual and population distribution information are useful in offspring generation, it is natural to hybrid EDA and genetic operators to improve the search efficiency. To this end, we propose a hybrid offspring generation strategy for GEP by using a univariate categorical distribution based EDA operator and its original genetic operators. To evaluate the performance of the new hybrid algorithm, we apply the algorithm to ten regression tasks using various parameters and strategies. The experimental results demonstrate that the new algorithm is a promising approach for solving regression problems efficiently. The GEP with hybrid operators outperforms the original GEP that uses genetic operators on eight out of ten benchmark datasets.

关键词： gene expression Sociology Statistics Probabilistic logic genetic algorithms programming genetic programming gene expression programming estimation of distribution algorithm offspring generation

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