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Application of Artificial Neural Networks for Corrosion Behavior of Ni-Zn Electrophosphate Coating on Galvanized Steel and gene expression programming Models

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FRONTIERS IN MATERIALS 2022年 9卷

作者： Zeraati, Malihe Abbasi, Hossein Ghaffarzadeh, Parvin Chauhan, Narendra Pal Singh Sargazi, Ghasem Shahid Bahonar Univ Kerman Dept Mat Engn Kerman Iran Islamic Afzad Univ Dept Comp Engn Tangestan Branch Tangestan Iran Islamic Azad Univ Dariun Dept Comp Engn Dariun Iran Bam Univ Med Sci Noncommunicable Dis Res Ctr Bam Iran

Zn-Ni electrophosphate coating is one of the most commonly used materials in industrial applications. The corrosion resistance of this coating is very important in order to achieve the minimum corrosion current of the Zn-Ni electrophosphate coating. This study described a new reliability simulation framework to determine the corrosion behavior of coating using a gene artificial neural network (ANN) to estimate the corrosion current of the coating. The input parameters of the model are temperature, pH of electroplating bath, current density, and Ni2+ concentration, and corrosion current defined as output. The effectiveness and accuracy of the model were checked by utilizing the absolute fraction of variance (R-2 = 0.9999), mean absolute percentage error (MAPE = 0.0171), and root mean square error (RMSE= 0.0002). This is determined using the genetic algorithm (GA) and the optimum practice condition.

关键词： artificial neural network Zn-Ni electrophosphate coating corrosion current modeling genetic algorithm gene expression programming

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Rigorous framework determining residual gas saturations during spontaneous and forced imbibition using gene expression programming

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2020年 84卷 103644-103644页

作者： Rostami, Alireza Raef, Abdelmoneam Kamari, Arash Totten, Matthew W. Abdelwahhab, Mohammad Panacharoensawad, Ekarit Petr Univ Technol PUT Dept Petr Engn Ahvaz Iran Kansas State Univ Dept Geol 108 Thompson Hall Manhattan KS 66506 USA Texas Tech Univ Bob L Herd Dept Petr Engn Lubbock TX 79409 USA Al Azhar Univ Geol Dept Fac Sci Cairo Egypt

Aquifer encroachments toward the producing gas zones can substantially decrease the volumetric gas flowrate due to the bypassing of large gas volumes in the porous media. It is well-known that the main parameter monitoring the cumulative gas production is the residual gas saturation;therefore, a complete information about strong aquifer drive gas reservoirs is required relying on both operational and petrophysical parameters. Despite the abundance of studies, attempting to establish comprehensive and adequately precise models for estimating the residual gas saturations is still challenging. In present work, the new method namely, gene expression programming (GEP), was used to propose 9 correlations for force, spontaneous imbibition and the overall waterflood conditions. For this reason, a comprehensive database including 897 residual gas saturation data were adopted from the open source literatures. The variation ranges of the employed dataset include 0.013 to 1 for initial gas saturation, 0.001 to 12950 mD for absolute permeability of rock, 0.008 to 0.494 for porosity (as the input parameters) and 0 to 0.79 for residual gas saturation (as the model output). Numerous types of error analyses such as statistical and visual tools are employed to assess the prediction capability of the new methods developed here as compared with a commonly applied literature model. As a result, it is perceived that the general I, Imbibition I and Force I are the most effective correlations with the root mean square error (RMSE) of 0.09, 0.06 and 0.14, consecutively. The results of the literature model are less efficient than our newly suggested methods. Moreover, the so-called technique of sensitivity analysis revealed that the porosity and permeability have insignificant impacts on predicting the residual gas saturation in the case of spontaneous imbibition. To end with, the results of this study can be the good nominees for fast, accurate and inexpensive estimation of the residual gas

关键词： Aquifer drive gas reservoir gene expression programming Residual gas saturation Correlation Error analysis Sensitivity analysis

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Proposing new breaking wave height prediction formulae using gene expression programming

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OCEAN ENGINEERING 2021年 228卷 108952-108952页

作者： Tadayon, Behrooz Dehghani, Hamid Ershadi, Cyrus Hormozgan Univ Fac Engn Civil Engn Dept Hormozgan Iran

Wave breaking occurs when waves reach a critical level of amplitude, which results in releasing a significant amount of energy. Predicting the wave heights, locations and other characteristics of breaking waves in shallow water is an important area of research. The breaking wave height, which is the most important characteristic, depends on various parameters such as deepwater wavelength, breaking water depth and seabed slope. In this study, new formulae are proposed for predicting the breaking wave height focusing on two types of breaking waves, namely spilling and plunging. The breaking wave types can be classified by using the Iribarren number. A comprehensive set of laboratory and field observations is utilized to develop new formulae based on the gene expression programming (GEP) numerical method. The datapoints representing individual waves whose breaking types have been determined to be different according to different wave breaking classification criteria have been ignored. In comparison with previous formulae, the new formulae proposed based on different breaking types have almost higher accuracy, especially in the plunging case. Therefore, it might be concluded that developing different formulae based on different breaking types, rather than a general one for all types, would lead to more accurate results.

关键词： Wave breaking Breaker type Breaking wave height gene expression programming Iribarren number

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An optimized equation based on the gene expression programming method for estimating tunnel construction costs considering a variety of variables and indexes

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Mahmoodzadeh, Arsalan Nejati, Hamid Reza Tarbiat Modares Univ Sch Engn Rock Mech Div Tehran Iran

Accurate cost estimation in tunneling is key to the project's success. Such information is critical for the early conceptual and design phases when key choices must be made. Numerous variables influence the cost of tunnel construction, and limited information is available at the early stages of design when the possible use of tunnels is being studied. Therefore, there is a limited number of models at engineers' disposal to develop a proper cost estimate for tunneling projects. This study aimed to offer a model for estimating the construction cost of drilling and blasting tunnels in the preliminary stage of a project. For this purpose, an optimized gene expression programming (GEP) method was used based on the study of 900 data points obtained from ten drilling and blasting tunnels, which were randomly split into the training (800 data points) and testing (100 data points) datasets. With the experience of previously constructed tunnels, eleven parameters were considered the most effective for the tunnel's construction cost. The best fit on predictions generated an equation for the optimized GEP model. Finally, by comparing the equation's outputs with the actual costs and its behavior with practice, its potential ability to estimate the construction cost of drilling and blasting tunnels was approved. Moreover, the Graphical User Interface (GUI) of the GEP model was created as a practical tool for estimating the construction cost of tunnels. This model can reduce tunnel uncertainties and give ML development in tunnel planning.

关键词： Soft computing gene expression programming Tunnel construction costs Drilling and blasting tunnels Graphical user interface

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Developing a prediction model for rutting depth of asphalt mixtures using gene expression programming

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CONSTRUCTION AND BUILDING MATERIALS 2021年 267卷 120543-120543页

作者： Majidifard, Hamed Jahangiri, Behnam Rath, Punyaslok Contreras, Loreto Urra Buttlar, William G. Alavi, Amir H. Univ Missouri Dept Civil & Environm Engn Columbia MO 65211 USA Univ Pittsburgh Dept Civil & Environm Engn Pittsburgh PA 15260 USA Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan

This study presents a new model for the prediction of rutting depth of asphalt mixtures using a machine learning technique called gene expression programming (GEP). A database containing a comprehensive collection of Hamburg test results is used to develop a GEP-based prediction model. The database includes 96 tests results for various asphalt mixtures. The model is formulated in terms of typical influencing variables such as asphalt binder high temperature performance grade (PG), mixture type, aggregate size, aggregate gradation, asphalt content, and total asphalt binder recycling content. The model accuracy was assessed through a rigorous validation process. A sensitivity analysis was performed to evaluate the effect of the variables on the rutting depth in the GEP model. A comparative study has been conducted to benchmark the prediction performance of the GEP model against an artificial neural network model. The model is capable of capturing the differences in mixture properties and test conditions. The model is recommended for pre-design purposes or as a tool to determine rut depth in asphalt mixtures when laboratory testing is not feasible. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Hamburg wheel tracking Asphalt concrete Rutting performance Machine learning gene expression programming

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Fire response of steel connections using gene expression programming and finite element method

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2021年第11期30卷 e1862-e1862页

作者： Ketabdari, Hesamoddin Daryan, Amir Saedi Hassani, Nemat Safi, Mohammad Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

In this paper, the behavior of bolted and welded beam-to-column connections exposed to high temperatures is investigated. In this regard, 16 effective parameters are considered as the inputs, along with the connection rotation as the output. Finite element models of bolted and welded connections have been verified and used to generate sufficient data for equation training by the gene expression programming (GEP) method. The results indicated that the relative error of the bolted and welded models is equal to 11% and 12.5%, and R-2 coefficients were 0.9 and 0.85, respectively. Comparing the results acquired from the GEP model and experimental tests approves a proper performance with an acceptable rate of error of the developed equations for both bolted and welded connections in fire conditions. This performance has adequate reliability, particularly in critical temperatures, where the connection failure occurs. Moreover, the development of formulas with such a degree of accuracy indicates the utmost importance of employing applicable algorithms in simulating complex interaction of structure and fire.

关键词： bolted and welded connections failure mode finite element method fire gene expression programming rotation

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Modelling filtered reaction rate in turbulent premixed flames using feature importance analysis, gene expression programming and tiny artificial neural networks

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INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW 2022年 97卷

作者： Kasten, C. Shin, J. Pfitzner, M. Klein, M. Univ Bundeswehr Munich Dept Aerosp Engn Munich Germany

Two different machine learning (ML) approaches, Symbolic regression, more precisely gene expression programming (GEP) and Tiny Artificial Neural Networks (TANN), have been used for deducing a model for the filtered reaction rate in the context of Large Eddy Simulation (LES) of premixed combustion. The models have been trained on data generated from a large range of filter widths and for different turbulence intensities, using a simple chemistry Direct Numerical Simulation (DNS) database of statistically planar turbulent premixed flames. Feature importance analysis has been applied to identify the important input variables for modelling filtered reaction rate in order to enable an efficient optimization process as well as moderate inference times. The resulting models show excellent correlation and good quantitative agreement with the filtered reaction rates extracted from explicitly filtered DNS data. The genetic optimization results in a mathematical expression that can be easily implemented, analysed and documented. Also, the TANN has a small number of trainable parameters that can be shared with the research community. The present results demonstrate that ML algorithms can be successfully used for modelling turbulent premixed flames in the context of Large Eddy Simulation. Both approaches can fulfil the requirements of transparency and have inference times that are acceptable in comparison to algebraic models.

关键词： gene expression programming Tiny artificial neural networks Feature importance analysis Statistically planar turbulent premixed flame Filtered reaction rate Direct numerical simulation

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geneOS: An accurate equation of state for the fast calculation of two-phase geofluids properties based on gene expression programming

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COMPUTER PHYSICS COMMUNICATIONS 2024年 297卷

作者： Esmaeilpour, Morteza Nitschke, Fabian Kohl, Thomas Karlsruhe Inst Technol Inst Appl Geosci Geothermal Energy & Reservoir Technol Karlsruhe Germany

Numerical simulation of two-phase multicomponent flows requires solving continuity, momentum, energy, and transport equations. Typically, these conservation equations are solved for computing the main variables of pressure, enthalpy, velocity, and composition. Variation of thermophysical properties (e.g., density, viscosity, etc.) as functions of the main variables necessitates introducing equations of state (EOS) to the modeling scheme, equating the number of unknowns and equations. The problem arises here as almost all the available EOSs in the literature receive temperature as an input, which is not a main variable. Guessing temperature, as an unknown input, imposes more iterations on the already iterative algorithm of the EOS and increases the computational cost. The primary focus of this study is to provide highly-precise, but fast EOS scheme for calculating two-phase fluid properties using artificial intelligence algorithms. In the first step, a Fugacity-Activity model is implemented to supply a supervised learning algorithm with a large dataset. The provided data are fed into a machine-learning (ML) model called gene expression programming (GEP). The outputs of this GEP model are high-preciseness explicit formulas for non-iterative computing of temperature and equilibrium constants. Testing the proposed GEP equations for 1,000,000 arbitrary sets of inputs revealed high accuracy in predicting desired outputs (e.g., < 0.6% error in calculating temperature). Implementing GEP equations in modeling platforms can result in similar to 90% reduction in EOS-related computational cost. This ML-based EOS is a transparent box for computing thermophysical properties of two-phase mixtures containing H2O, CO2, CH4, N-2, H2S, NaCl, KCl, CaCl2, and MgCl2.

关键词： Equation of state gene expression programming Machine learning Two-phase flow Multicomponent geofluids

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AB-GEP: Adversarial bandit gene expression programming for symbolic regression

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 75卷

作者： Lu, Qiang Xu, Congwen Luo, Jake Wang, Zhiguang China Univ Petr Beijing Key Lab Petr Data Min Beijing 102249 Peoples R China Univ Wisconsin Milwaukee Dept Hlth Sci & Adm Milwaukee WI USA

gene expression programming (GEP) is a commonly used approach for solving symbolic regression (SR) problems. However, GEP often falls into a local optimum. GEP also randomly searches for results and could revisit low value search spaces, which impacts its performance. To overcome the two problems, we propose a new algorithm using an adversarial bandit (AB) technique to enhance GEP and the new algorithm is named AB-GEP. AB-GEP segments the mathematical expression space into many subspaces. It then leverages a new search space selection method, AvgExp3, to enhance the population jump between subspaces. This prevents the algorithm from falling into a local optimum. AvgExp3 dynamically estimates a subspace by the rewards generated in the search space. The dynamical reward estimation makes AB-GEP more adaptive to reward changes and it also provides guidance for AB-GEP to choose a subspace that could contain the correct results. This study proves that AvgExp3 is an unbiased estimation of the average rewards of subspace, and its variance is lower than the standard Exp3 method. The evaluation on two benchmark datasets shows that AB-GEP can maintain better population diversity and obtain better results than three traditional GEPs, GEP, SL-GEP, and SPJ-GEP. AB-GEP ranks top in 50% of the 32 benchmarks, while one of the traditional GEPs only gets no more than 20% of the best results. We release our code at https://***/kgae-cup/ab-gep.

关键词： Adversarial bandit Symbolic regression gene expression programming

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Prediction and minimization of blast-induced flyrock using gene expression programming and cuckoo optimization algorithm

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ENVIRONMENTAL EARTH SCIENCES 2021年第1期80卷 12-12页

作者： Dehghani, Hesam Pourzafar, Mehran zadeh, Mostafa Asadi Hamedan Univ Technol Min Engn Fac Hamadan Hamadan Iran

Flyrock is one of the most dangerous adverse environmental effects of the blast, which can cause serious damage to the nearby buildings and people and even lead to the death in some cases. Therefore, it is very essential to correctly predict the flyrock distance to control and reduce the flyrock and also to determine the safety area. Due to the multiplicity of the parameters influencing the flyrock and the complexity of interacting parameters, the performance of empirical equations for predicting the flyrock distance is not satisfied and they cannot guaranty safety for the surrounding environment. In this research, in the first step, the predictor equation for the blast-induced flyrock was obtained using the gene expression programming. To achieve this goal, the data from 318 blast blocks in the Sungun copper mine was recorded and the amount of flyrock resulting from each blast was accurately measured. Burden, spacing, stemming, charge length and powder factor were used as the input parameters in gene expression programming (GEP) model. Among the gathered datasets, 268 datasets were intended for the model training and 50 datasets for the model testing. Then, the obtained equation from GEP was used as cost function for minimizing flyrock by cuckoo optimization algorithm. For this purpose, five strategies were defined and some optimal blast designs were provided for each strategy. The results showed that the gene expression programming model with the correlation coefficient of 0.91 and the root mean square error of 5.85 m has a good performance in predicting the blast-induced flyrock using the blast design parameters. Also, the maximum flyrock value of blast designs provided by the cuckoo algorithm was reduced by 43.6% relative to the maximum flyrock value of the initial blast designs. Input parameters sensitivity analysis showed that the powder factor is the most effective parameter on the flyrock phenomenon.

关键词： Flyrock gene expression programming Cuckoo optimization algorithm

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