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Empirical modeling of plate load test moduli of soil via gene expression programming

COMPUTERS AND GEOTECHNICS 2011年第2期38卷 281-286页

作者： Mollahasani, Ali Alavi, Amir Hossein Gandomi, Amir Hossein Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran Tafresh Univ Coll Civil Engn Tafresh Iran

New empirical models were developed to predict the soil deformation moduli using gene expression programming (GEP). The principal soil deformation parameters formulated were secant (E-s) and reloading (E-r) moduli. The proposed models relate E-s and E-r obtained from plate load-settlement curves to the basic soil physical properties. The best GEP models were 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 a series of plate load tests conducted on different soil types at depths of 1-24 m. To verify the applicability of the derived models, they were employed to estimate the soil moduli of a part of test results that were not included in the analysis. The external validation of the models was further verified using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting E-s and E-r. The proposed models give precise estimates of the soil deformation moduli. The E-s prediction model provides considerably better results in comparison with the model developed for E-r. The simplified formulation for E-s significantly outperforms the empirical equations found in the literature. The derived models can reliably be employed for pre-design purposes. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Soil deformation moduli Soil physical properties gene expression programming Nonlinear modeling

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Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning

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HYDROLOGICAL PROCESSES 2014年第3期28卷 1215-1225页

作者： Shiri, Jalal Marti, Pau Singh, Vijay P. Univ Tabriz Water Engn Dept Fac Agr Tabriz Iran Univ Politecn Valencia Dept Ingn Rural & Agroalimentaria E-46071 Valencia Spain Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77802 USA Texas A&M Univ Dept Civil & Environm Engn College Stn TX 77802 USA

Evaporation of water from free water surfaces or from land surfaces is one of the main components of the hydrological cycle, and its occurrence is governed by various meteorological and physical factors. There is a multitude of models developed for estimating daily evaporation values by using weather data. This paper evaluates a gene expression programming (GEP) model for estimating evaporation through spatial and temporal data scanning techniques. It is by using leave-one-out' procedures, a complete scan of the possible train and test set configurations is carried out according to temporal and spatial criteria. Comparison of the GEP model with empirical-physical models shows that daily evaporation values computed by the GEP model are more accurate. Further, local calibration of the GEP model may not be needed, if enough climatic data are available at other stations. The performance of the GEP model fluctuates throughout the period of study and between stations. A suitable assessment of the model should consider a complete temporal and/or spatial scan of the data set used. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： pan evaporation gene expression programming leave-one-out empirical equations generalized models

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Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第10期57卷 3121-3137页

作者： Ozturk, Gurkan Bahadir, Ozan Teymourifar, Aydin Eskisehir Tech Univ Fac Engn Dept Ind Engn TR-26555 Eskisehir Turkey Eskisehir Tech Univ CIOL TR-26555 Eskisehir Turkey

In this paper, two new approaches are proposed for extracting composite priority rules for scheduling problems. The suggested approaches use simulation and gene expression programming and are able to evolve specific priority rules for all dynamic scheduling problems in accordance with their features. The methods are based on the idea that both the proper design of the function and terminal sets and the structure of the gene expression programming approach significantly affect the results. In the first proposed approach, modified and operational features of the scheduling environment are added to the terminal set, and a multigenic system is used, whereas in the second approach, priority rules are used as automatically defined functions, which are combined with the cellular system for gene expression programming. A comparison shows that the second approach generates better results than the first;however, all of the extracted rules yield better results than the rules from the literature, especially for the defined multi-objective function consisting of makespan, mean lateness and mean flow time. The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems.

关键词： dynamic job shop scheduling priority rules simulation gene expression programming multi-objective optimisation

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Artificial neural networks vs. gene expression programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2013年 99卷 176-185页

作者： Marti, Pau Shiri, Jalal Duran-Ros, Miguel Arbat, Gerard Ramirez de Cartagena, Francesc Puig-Bargues, Jaume Univ Politecn Valencia Dept Engn Rural & Agroalimenteria Valencia 46022 Spain Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Univ Girona Dept Chem & Agr Engn & Technol Girona 17071 Spain

Sand media filters are specially used to avoid emitter clogging when water with large amount of organic pollutants like effluents are used in micro-irrigation systems. Estimation of water quality parameters such as dissolved oxygen at sand filter outlet (DOo) is of great interest for irrigation engineers. Artificial neural networks (ANN), gene expression programming (GEP) and Multi Linear Regression (MLR) based models were trained for estimating DOo using data from 769 experimental filtration cycles. Instead of considering a single configuration of the training and test data sets, which is the usual procedure for those applications in agricultural studies, the performance of those models was assessed through k-fold testing, ensuring a complete performance evaluation. In general, the GEP model tended to provide the most accurate estimations, followed by ANN and, lastly, by MLR models. After the evaluation of the models, the GEP approach was used to provide a new equation to estimate DOo based on the complete data set. This procedure revealed that only inlet DO, pH, electrical conductivity and filter head loss were necessary to feed the models. Furthermore, the consideration of leave one out or, at least, k-fold assessment should be advisable to perform a suitable evaluation of the model performance. Otherwise, conclusions drawn might be only partially valid. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks gene expression programming Dissolved oxygen k-Fold testing Drip irrigation Clogging

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Mathematical-Based gene expression programming (GEP): A Novel Model to Predict Zinc Separation from a Bench-Scale Bioleaching Process

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JOURNAL OF SUSTAINABLE METALLURGY 2023年第4期9卷 1601-1619页

作者： Hosseini, Shahab Javanshir, Sepideh Sabeti, Hamid Tahmasebizadeh, Parastoo Tarbiat Modares Univ Fac Engn Tehran Iran Univ Birjand Fac Engn Birjand Iran Birjand Univ Technol Dept Min Engn Birjand *** Iran

Attempts to predict metal recovery accurately have been hindered by the complexity of the solvent extraction process, nonlinear effects, and the multitude of influencing factors. Conventional computing algorithms must be improved for solving practical problems due to insufficient or noisy data and complex multidimensional scenarios. In this study, a gene expression programming (GEP) model was developed to predict zinc extraction (ZE) from the bioleaching process utilizing the most influential parameters such as stirring speed (0-800 rpm), temperature (25-45 degrees C), pH (1-2.5), Di-(2-ethylhexyl) phosphoric acid (D2EHPA) concentration (5-20%), phase ratio (1:5-10:1), saponification degree (0-40%), and contact time (0-900 s) as the input parameters. Under optimal conditions of 20% D2EHPA, 15% saponification degree, 650 rpm stirring speed, pH2, and an A:O ratio of 1:1, zinc extraction reached 98.4%. The main motivation for constructing the GEP model is to present a rational mathematical model with more accurate results than statistical models. Furthermore, a model should be applicable for future usages to predict the value of metal recovery using independent variables accurately. The developed GEP model outperformed multiple linear regression and multiple nonlinear regression models with the adjusted R2 of 0.9551 and 0.9453, RMSE of 5.5100 and 7.2727, MAE of 0.0758 and 2.8308, MARE of 0.0260 and 0.0334, and VAF of 92.3069 and 87.9199 for the respective training and testing parts. Besides, the sensitivity analysis was performed using the cosine amplitude (CA) technique, and the stirring speed and D2EHPA concentration have the highest (rij = 0.931) and lowest (rij = 0.581) impact on the predicted ZE, respectively. The conducted study proves the appropriateness of the developed GEP model for ZE prediction.

关键词： Solvent extraction of zinc gene expression programming Multiple regression analysis Sensitivity analysis

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Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement

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MEASUREMENT 2017年 95卷 367-376页

作者： Gandomi, Amir H. Alavi, Amir H. Gandomi, Mostafa Kazemi, Sadegh Michigan State Univ BEACON Ctr Study Evolut Act 1450 BPS E Lansing MI 48824 USA Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA Univ Tehran Dept Civil Engn Tehran Iran Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada

In this study, a new variant of genetic programming, namely gene expression programming (GEP) is utilized to predict the shear strength of reinforced concrete (RC) beams with stirrups. The derived model relates the shear strength to mechanical and geometrical properties. The model is developed using a database containing 466 experimental test results gathered from the literature. Sensitivity and parametric analyses are performed for further verification of the model. The comparative study proves the superior performance of the GEP model compared to the expressions developed in several codes of practice. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Shear strength Reinforced concrete beam Normal and high-strength concrete Stirrups gene expression programming Formulation

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gene expression programming for Estimating Shear Strength of RC Squat Wall

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BUILDINGS 2022年第7期12卷 918页

作者： Tariq, Moiz Khan, Azam Ullah, Asad Zamin, Bakht Kashyzadeh, Kazem Reza Ahmad, Mahmood Natl Univ Sci & Technol NUST Sch Civil & Environm Engn NUST Inst Civil Engn NICE Sect H-12 Islamabad 44000 Pakistan CECOS Univ IT & Emerging Sci Civil Engn Dept Peshawar 25000 Pakistan Peoples Friendship Univ Russia Acad Engn Dept Transport RUDN Univ 6 Miklukho Maklaya St Moscow 117198 Russia Univ Engn & Technol Peshawar Dept Civil Engn Bannu Campus Bannu 28100 Pakistan

The flanged, barbell, and rectangular squat reinforced concrete (RC) walls are broadly used in low-rise commercial and highway under and overpasses. The shear strength of squat walls is the major design consideration because of their smaller aspect ratio. Most of the current design codes or available published literature provide separate sets of shear capacity equations for flanged, barbell, and rectangular walls. Also, a substantial scatter exists in the predicted shear capacity due to a large discrepancy in the test data. Thus, this study aims to develop a single gene expression programming (GEP) expression that can be used for predicting the shear strength of these three cross-sectional shapes based on a dataset of 646 experiments. A total of thirteen influencing parameters are identified to contrive this efficient empirical compared to several shear capacity equations. Owing to the larger database, the proposed model shows better performance based on the database analysis results and compared with 9 available empirical models.

关键词： flanged wall barbell wall rectangular walls shear strength gene expression programming

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Comparison of gene expression programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain)

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ENERGY CONVERSION AND MANAGEMENT 2012年 62卷 1-13页

作者： Landeras, Gorka Javier Lopez, Jose Kisi, Ozgur Shiri, Jalal Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Basque Country Res Inst Agr Dev AB NEIKER Alava Basque Country Spain Univ Publ Navarra Dept Projects & Rural Engn Pamplona 31006 Spain CanikBasari Univ Fac Architecture & Engn Dept Civil Engn Samsun Turkey

Surface incoming solar radiation is a key variable for many agricultural, meteorological and solar energy conversion related applications. In absence of the required meteorological sensors for the detection of global solar radiation it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using gene expression programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). A comparison was also made among these techniques and traditional temperature based global solar radiation estimation equations. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE). MAE-based skill score (SSMAE) and r(2) criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with 10 neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m(-2) d(-1) of RMSE). The ability of GEP approach to model global solar radiation based on daily atmospheric variables was found to be satisfactory. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Global solar radiation Artificial intelligence gene expression programming Temperature

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Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt

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AGRICULTURAL WATER MANAGEMENT 2018年 198卷 28-38页

作者： Mattar, Mohamed A. King Saud Univ Coll Food & Agr Sci Agr Engn Dept POB 2460 Riyadh 11451 Saudi Arabia

The Penman-Monteith FAO-56 equation requires the complete climatic records for estimating reference evapotranspiration (ET0). The present study is aimed at developing and evaluating a gene expression programming (GEP) model for estimating mean monthly ET0 by using minimal amount of climatic data. The data used in the analysis are collected from 32 weather stations in Egypt through the CLIMWAT database. The results showed that the accuracy of the GEP model significantly improved when either mean relative humidity (RH) or wind speed at 2-m height (u(2)) was used as additional input variables. The GEP model with the inputs as maximum and minimum air temperature, RH, and u(2) showed the lowest root mean square error (0.426 mm d(-1) and 0.430 mm d(-1)) and, the highest coefficient of determination, (0.963 and 0.962) overall index of model performance (0.960 and 0.960), and index of agreement (0.991 and 0.990) for training and testing sets, respectively. Comparing the results of GEP models with other empirical models showed that the GEP technique are more accurate and can be employed successfully in modelling ET0. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Reference evapotranspiration gene expression programming Penman-Monteith Empirical models

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Determination of the ultimate limit states of shallow foundations using gene expression programming (GEP) approach

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SOILS AND FOUNDATIONS 2015年第3期55卷 650-659页

作者： Poor, A. Tahmasebi Barari, A. Behnia, M. Najafi, T. Khazar Ab Consulting Engineers Co Sari Iran Aalborg Univ Dept Civil Engn DK-9000 Aalborg Denmark Azad Univ Qaemshahr Branch Dept Civil Engn Qaemshahr Iran Babol Univ Technol Dept Civil Engn Babol Sar Iran

In this study, a gene expression programming (GEP) approach was employed to develop modified expressions for predicting the bearing capacity of shallow foundations founded on granular material. The model was validate against the results of load tests on full-scale and model footings obtained from the literature. Two models were developed employing different input variables in the GEP approach. The results achieved using the proposed formulae were compared with those obtained front the Meyerhof and Vesic theories. Statistical analysis was used to demonstrate that the GEP models yielded more accurate results than the traditional solutions. (C) 2015 The Japanese Geotechnical Society. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Shallow foundations Bearing capacity gene expression programming Granular soil

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