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Modeling the correlation between yield strength, chemical composition and ultimate tensile strength of X70 pipeline steels by means of gene expression programming

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INTERNATIONAL JOURNAL OF MATERIALS RESEARCH 2013年第7期104卷 697-702页

作者： Khalaj, Gholamreza Khalaj, Mohammad-Javad Islamic Azad Univ Dept Tech & Engn Sci Saveh Branch Saveh Iran

In the present work, the ultimate tensile strength of steel made using thermomechanically controlled processing has been modeled by means of gene expression programming. To build the model, training and testing using experimental results from 104 specimens were conducted. The data used as inputs in gene expression programming models are arranged in a format of six parameters that cover the carbon equivalent, based upon the International Institute of Welding equation and the chemical portion of the Ito-Bessyo equation, the sum of the Nb, V, and Ti, the sum of the Nb and V, the sum of the Cr, Mo, Ni, and Cu contents and yield strength. The training and testing results in gene expression programming models have shown a strong potential for correlating the ultimate tensile strength to yield strength and chemical composition of X70 pipeline steels.

关键词： Microalloyed steel Mechanical properties API X70 steel gene expression programming

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Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system

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NEURAL COMPUTING & APPLICATIONS 2013年第3-4期23卷 1035-1044页

作者： Terzi, Ozlem Suleyman Demirel Univ TR-32260 Isparta Turkey

This study was conducted using gene expression programming (GEP) and an adaptive neural-based fuzzy inference system (ANFIS) as an alternative approach to estimate daily pan evaporation, which is an important parameter in hydrological and meteorological studies. The input parameters used to estimate daily pan evaporation from Lake Egirdir in the southwestern part of Turkey are the daily pan evaporation data of Lake Kovada (Ko (t) ) and Lake Karacaoren Dam (Ka (t) ) and the previous 1-, 2-, and 3-day pan evaporation values of Lake Egirdir. The various input combinations were tried by using pan evaporation data for the years 1998-2005. The GEP model with the highest Nash-Sutcliffe efficiency and the lowest mean square error have the daily pan evaporation data of Lake Kovada (Ko (t) ) and Lake Karacaoren Dam (Ka (t) ) and the previous 1-day pan evaporation values of Lake Egirdir. The NSE of the best GEP model was obtained as 0.729, 0.722, and 0.701 for training, testing, and validation sets, respectively. Furthermore, the ANFIS models were developed using the same input combinations. It was seen that the GEP model was more superior to the ANFIS model.

关键词： Daily pan evaporation gene expression programming Adaptive neural-based fuzzy inference system Lake Egirdir

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Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第14期40卷 5427-5438页

作者： Nazari, Ali Torgal, F. Pacheco WorldTech Sci Res Ctr WT SRC Dept Concrete & Geopolymer Tehran Iran Univ Minho C TAC Res Ctr Guimaraes Portugal WorldTech Sci Res Ctr WT SRC Dept Modeling & Simulat Tehran Iran

GEP has been employed in this work to model the compressive strength of different types of geopolymers through six different schemes. The differences between the models were in their linking functions, number of genes, chromosomes and head sizes. The curing time, Ca(OH)(2) content, the amount of superplasticizer, NaOH concentration, mold type, aluminosilicate source and H2O/Na2O molar ratio were the seven input parameters considered in the construction of the models to evaluate the compressive strength of geopolymers. A total number of 399 input-target pairs were collected from the literature, randomly divided into 299 and 100 sets and were trained and tested, respectively. The best performance model had 6 genes, 14 head size, 40 chromosomes and multiplication as linking function. This was shown by the absolute fraction of variance, the absolute percentage error and the root mean square error. These were of 0.9556, 2.4601 and 3.4716 for training phase, respectively and 0.9483, 2.8456 and 3.7959 for testing phase, respectively. However, another model with 7 genes, 12 head size, 30 chromosomes and addition as linking function showed suitable results with the absolute fraction of variance, the absolute percentage error and the root mean square of 0.9547, 2.5665 and 3.4360 for training phase, respectively and 0.9466, 2.8020 and 3.8047 for testing phase, respectively. These models showed that gene expression programming has a strong potential for predicting the compressive strength of different types of geopolymers in the considered range. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Geopolymers Compressive strength gene expression programming

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A Novel Function Mining Algorithm Based on Attribute Reduction and Improved gene expression programming

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IEEE ACCESS 2019年 7卷 53365-53376页

作者： Yuan, Changan Qin, Xiao Yang, Lechan Gao, Guangwei Deng, Song Nanning Normal Univ Sch Comp & Informat Engn Nanning 530001 Peoples R China Guangxi Coll Educ Sch Math & Comp Sci Nanning 530023 Peoples R China Jinling Inst Technol Sch Software Engn Nanjing 211169 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 230001 Jiangsu Peoples R China

It is very interesting and important to determine the function model for remote-sensing data. The existing statistical and artificial intelligence models still have some defects. The statistical models rely heavily on prior knowledge and cannot objectively reflect the real function model contained in the remote-sensing data. In addition, the existing artificial intelligence models can very easily fall into the local optimum and have a low efficiency for high-dimensional remote-sensing data. In this paper, we first decrease the complexity of remote-sensing data by using rough sets and propose an attribute reduction algorithm based on rough sets for remote-sensing data (ARRS-RSD). On the basis of the algorithm, this paper presents a function mining algorithm for remote-sensing data by using gene expression programming and rough sets (FMRS-ARGEP). In FMRS-ARGEP, a dynamic population generation policy and a new mutation operation based on self-adaptive rate adjustment are introduced to improve the convergence of the algorithm. The experimental results show that the proposed algorithm outperforms traditional algorithms in terms of the average running time, the number of condition attributes after reduction, the attribute reduction ratio, the average convergence speed, the number of convergences, and the R-2 value of the model.

关键词： gene expression programming attribute reduction function model mining dynamic population generation self-adaptive mutation

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An Extreme Learning Machine and gene expression programming-Based Hybrid Model for Daily Precipitation Prediction

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 1512-1525页

作者： Peng, Yuzhong Zhao, Huasheng Zhang, Hao Li, Wenwei Qin, Xiao Liao, Jianping Liu, Zhiping Li, Jie Nanning Normal Univ Key Lab Sci Comp & Intelligent Informat Proc Univ Nanning 530001 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Guangxi Res Inst Meteorol Disasters Mitigat Nanning 530022 Peoples R China Guangxi Sci & Technol Normal Univ Dept Math & Comp Sci Liuzhou 546100 Peoples R China

Accurate daily precipitation prediction is crucially important. However, it is difficult to predict the precipitation accurately due to inherently complex meteorological factors and dynamic behavior of weather. Recently, considerable attention has been devoted in soft computing-based prediction approaches. This work presents a scheme to reduce the risk of Extreme Learning Machine (ELM) modeling error using gene expression programming (GEP) to improve the prediction performance, and develops an ELM-GEP hybrid model for regional daily quantitative precipitation prediction. In this study, firstly, we use ELM for modeling the data sample of daily rainfall to construct a main model. Secondly, we use GEP for modeling the error of the main model as a compensation of the main model to reduce the prediction error. We conducted eight experiments of two different types of daily precipitation prediction problems using five metrics to evaluate our proposed model performance. Experimental results show that our model is comparable or even superior to five state-of-the-art models with high reliability in terms of all metrics on all datasets. It indicates that the proposed method is a promising alternative prediction tool for higher accuracy and credibility of regional daily precipitation prediction. (C) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Extreme Learning Machine gene expression programming Quantitative precipitation prediction Rainfall prediction Soft computing

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Computation of energy across the type-C piano key weir using gene expression programming and extreme gradient boosting (XGBoost) algorithm

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ENERGY REPORTS 2023年 9卷 310-321页

作者： Bansal, Nipun Singh, Deepak Kumar, Munendra Delhi Technol Univ Dept Comp Sci & Engn Delhi 110042 India Delhi Technol Univ Dept Civil Engn Delhi 110042 India

An accurate assessment of energy loss across dams and weir systems is a critical technical and monetary remedy for understanding the hydraulic system's downstream morphology during the flood. Using the standard empirical formulas, accurately estimating the energy loss across the different hydraulic devices is a tedious and challenging procedure. Consequently, new and concise techniques remain highly sought after. This paper presents two empirical models based on the gene expression programming (GEP) and Extreme Gradient Boosting (XGBoost) techniques to examine energy losses throughout the type-C PKW. The empirical models have been developed to consider five non-dimensional parameters, viz L/W, H-t/P, W-i/Wo, N, and S-i /So, that influence the energy over the weir significantly. The models were created using experimental data from a wide range of residual energy losses and release capacities. Additionally, the adequacy of the constructed GEP and XGBoost models was assessed using the RMSE (root mean square error) and statistical variables coefficient (R-2). As per the outcomes, the XGBoost model beats the GEP with the determination coefficient (R-2) = 0.999, MAE = 0.0062, MAPE = 1.4% and RMSE = 0.0012 in the training stage and R-2 = 0.998, MAE = 0.001, MAPE = 2.1, and RMSE = 0.001 in the testing data. These findings show that the XGBoost algorithm is more accurate than the two algorithms in this study for downstream energy prediction of type-C PKW. (C)2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Residual energy prediction gene expression programming Extreme gradient boosting (XGBoost) Piano key weir

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The recovery prediction of Zn and Pb from cerussite leaching using the fluoroboric acid by gene expression programming

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SEPARATION SCIENCE AND TECHNOLOGY 2021年第1期56卷 194-202页

作者： Sobouti, Arash Rezai, Bahram Rayati, Mohammad Taghi Hoseinian, Fatemeh Sadat Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran Univ Kashan Dept Chem Engn Kashan Iran

Predicting the recoveries of lead and zinc during the leaching process is necessary to evaluate the effect of various parameters on the process efficiency which is possible using appropriate modeling and simulation. Firstly, the dissolution of lead and zinc using fluoroboric acid from cerussite concentrate by hydrometallurgical method was evaluated in order to reduce the disadvantages of the pyrometallurgical methods. Then, new predictive models based on gene expression programming (GEP) were developed to predict the recoveries of lead and zinc. The results indicate the sufficiency of the GEP models in predicting the recoveries of lead and zinc during the process.

关键词： Cerussite concentrate Hydrometallurgy method Fluoroboric acid Prediction gene expression programming

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Performance prediction of tunnel boring machine through developing a gene expression programming equation

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ENGINEERING WITH COMPUTERS 2018年第1期34卷 129-141页

作者： Armaghani, Danial Jahed Faradonbeh, Roohollah Shirani Momeni, Ehsan Fahimifar, Ahmad Tahir, M. M. Amirkabir Univ Technol Dept Civil & Environm Engn Tehran 15914 Iran Tarbiat Modares Univ Dept Min Tehran 14115143 Iran Univ Tehran Sch Civil Engn Tehran 141556619 Iran Lorestan Univ Fac Engn Khorramabad *** Iran Univ Teknol Malaysia UTM Construct Res Ctr ISIIC Fac Civil Engn Johor Baharu 81310 Johor Malaysia

The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang-Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R-2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.

关键词： Tunnel boring machine Penetration rate gene expression programming Multiple regression

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Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran

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AGRICULTURAL WATER MANAGEMENT 2017年 188卷 101-114页

作者： Shiri, Jalal Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran

Accurate estimation of the reference evapotranspiration (ET0) is needed in water resources planning and management, irrigation scheduling and efficient agricultural water management. The FAO56-PM combination model is usually applied as a benchmark model for calculating ET0 and calibrating other ET0 models. However, the need for large amount of meteorological variables is a major drawback of this model, especially in case of data scarcity. Therefore, application of ET0 models relying on fewer meteorological parameters, as well as calculating ET0 using estimated meteorological variables is recommended in literature. The present paper aims at assessing the performances of different ET0 models using the recorded and estimated meteorological parameters and comparing the results with the corresponding gene expression programming (GEP) models (based on the same input parameters of the employed ET0 models) in hyper-arid regions. Daily meteorological parameters from 5 hyper-arid locations of Iran (covering a period of 12 years) were used. The commonly used Hargreaves (HG), Priestley-Taylor (PT), Turc (Tr) and Kimberly-Penman (KP, for alfalfa reference crop) were established and calibrated using both the recorded and estimated solar radiation, relative humidity, and wind speed data. The obtained results revealed that the GEP models outperform the corresponding empirical and semi-empirical models in all three studied categorizes (temperature/humidity-, radiation-, and combination-based approaches). The results also showed that the calibrated PT (original) and Tr (with estimated relative humidity) models gave the most accurate results among the related groups. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Data scarcity ET0 gene expression programming

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Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance

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ROAD MATERIALS AND PAVEMENT DESIGN 2023年第8期24卷 1977-1994页

作者： Tamanna, Afifa Shamsaei, Ezzatollah Urquhart, Robert Nguyen, Hoan D. Sagoe-Crentsil, Kwesi Duan, Wenhui Monash Univ Dept Civil Engn Clayton Vic Australia Australian Rd Res Board ARRB Port Melbourne Vic Australia

This research predicts residual solvent (alpha), which is a key component of the performance assessment for a sprayed/chip seal. In this study, conventional equations for alpha were assessed that showed prediction inefficiency (R-2 value as low as 0.82) under different experimental conditions. Accordingly, gene expression programming (GEP), an emerging branch in artificial intelligence, was utilised to resolve these difficulties by developing empirical models for alpha. The data required for model development was obtained from extensive laboratory tests conducted on bitumen-solvent binder films in this research. Model evaluation results showed an excellent degree of correspondence between predictions and experimental results (R-2 = 0.94). This is the first study to model a key component of sprayed seal performance using GEP. The model is recommended for pre-design purposes or as a tool to determine residual solvent in a sprayed seal when laboratory testing is not feasible, thereby saving time and expenditure.

关键词： Sprayed seal chip seal residual solvent gene expression programming performance prediction performance evaluation

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