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Modified Particle Swarm Optimization-Artificial Neural Network and gene expression Programing for Predicting High Temperature Oxidation Behavior of Ni-Cr-W-Mo Alloys

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INTERNATIONAL JOURNAL OF ENGINEERING 2020年第11期33卷 2327-2338页

作者： Hasibi, H. Mahmoudian, A. Khayati, G. R. Shahid Bahonar Univ Kerman Dept Mat Sci & Engn Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Met Kerman Iran

This paper is an attempt to model the oxidation behavior of Ni-base alloys by considering the alloying elements, i.e., Cr, W, Mo, as variables. Modified particle swarm optimization-artificial neural network (MPSO-ANN) and gene expression programming (GEP) techniques were employed for modeling. Data set for construction of (MPSO-ANN) and GEP models selected from 66 cyclic oxidation performed in the temperature range of 400-1150 degrees C for 27 different Ni-based alloy samples at various amounts of Cr, W, and Mo. The weight percent of alloying elements selected as input variables and the changes of weight during the oxidation cycle considered as output. To analyze the performance of proposed models, various statistical indices, viz. root mean squared error (RMSE) and the correlation coefficient between two data sets (R-2) were utilized. The collected data of GEP randomly divided into 21 training sets and 6 testing sets. The results confirmed that the possibility of oxidation behavior modeling using GEP by 12 2. = 0.981, RMSE =0.0822. By consideration of oxidation resistance as criteria, Cr, Mo, and W enhanced the oxidation resistance of Ni-based alloys. The results showed that in the presence of Cr as alloying element, especially at Cr contents higher than 22 wt.%, the effect of W and Mo were negligible. However, the same trend was reversed at the sample with Cr content lower than 20 wt.%. In these cases, the effect of W and Mo on oxidation resistance were significantly enhanced.

关键词： Artificial Neural Network gene expression programming High-temperature Alloys Modified Particle Swarm Optimization Oxidation Behavior

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Predicting tunnel water inflow using a machine learning-based solution to improve tunnel construction safety

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TRANSPORTATION GEOTECHNICS 2023年 40卷

作者： Mahmoodzadeh, Arsalan Ghafourian, Hossein Mohammed, Adil Hussein Rezaei, Nafiseh Ibrahim, Hawkar Hashim Rashidi, Shima Tarbiat Modares Univ Sch Engn Rock Mech Div Tehran Iran Univ Massachusetts Amherst Dept Civil & Environm Engn Amherst MA USA Cihan Univ Erbil Fac Engn Dept Commun & Comp Engn Erbil Kurdistan Iraq Univ Qom Fac Engn Dept Civil Engn Qom Iran Salahaddin Univ Erbil Coll Engn Dept Civil Engn 44002 Erbil Kurdistan Reg Erbil Kurdistan Iraq Univ Human Dev Coll Sci & Technol Dept Comp Sci Sulaymaniyah Kurdistan Iraq

Water inflow is a typical and complicated geological hazard that may have a significant effect on both the building timeline and the safety of a tunnel under construction. Therefore, accurate water inflow estimation in tunneling is a key factor for the project's success. Such information is critical for the early conceptual and design phases, when key choices must be made. For this purpose, an optimized model based on the gene expression programming (GEP) method was proposed to estimate the water inflow in tunnels. An equation was generated for the optimized GEP model through the best fit of the predictions. Finally, by comparing the equation's outputs with the actual ones and comparing its behavior with practice, its potential ability for estimating the water inflow of tunnels was approved. This model can reduce the uncertainties about tunnels and give machine learning development in tunnel planning.

关键词： Tunneling Water inflow Safety Machine learning gene expression programming

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Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models

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PLANT METHODS 2022年第1期18卷 48-48页

作者： Sadat-Hosseini, Mohammad Arab, Mohammad M. Soltani, Mohammad Eftekhari, Maliheh Soleimani, Amanollah Vahdati, Kourosh Univ Jiroft Fac Agr Dept Hort Jiroft Iran Univ Tehran Coll Aburaihan Dept Hort Tehran Iran Univ Tehran Coll Aburaihan Dept Irrigat & Draintage Engn Tehran Iran Ta Rbiat Modares Univ TMU Fac Agr Dept Hort Sci Tehran Iran Univ Jiroft Dept Agron & Plant Breeding Fac Agr Jiroft Iran

Background Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R-2), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied. Results Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R-2 of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability. Conclusion Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in op

关键词： Walnut in vitro propagation Artificial neural network gene expression programming k-nearest neighbors Prediction model

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Schema Theorem Based on Probability for Multigenic Chromosomes genes expression programming

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2016年第1期10卷 87-94页

作者： Cheng, Huifang Zhao, Jianli Wang, Haitao Hebei Univ Engn Sch Informat & Elect Engn Handan 056038 Peoples R China Hebei Elect Power Design & Res Inst Shijiazhuang 050031 Peoples R China Hebei Univ Engn Coll Sci Handan 056038 Peoples R China

Schema theorem is the theoretical foundation of the evolutionary algorithm. This paper focuses on a new, and general schema theory for multigenic chromosomes gene expression programming. The theory is applicable to every operator in GEP via the method of probability. Not only the destruction but also the creation probability of the schema is considered in the evolutionary process. An equivalent form of general schema theory of GEP was deduced. The result shows that the individuals with high fitness values, shorter schema order, more function nodes, and the shorter the insertion sequence have the greater transmission probability than the average ones.

关键词： gene expression programming GEP Schema Theory Multigenic Chromosomes

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Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs

ADVANCES IN GEO-ENERGY RESEARCH

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ADVANCES IN GEO-ENERGY RESEARCH 2020年第2期4卷 126-134页

作者： Rostami, Alireza Daneshi, Alireza Miri, Rohaldin Petr Univ Technol Put Dept Petr Engn Ahwaz Iran Shahid Chamran Univ Oil & Gas Fields & Equipment Res & Dev Ctr Ahwaz Iran Iran Univ Sci & Technol Lust Dept Petr Engn Tehran Iran

Bubble point pressure is of great significance in reservoir engineering calculations affecting the success of reservoir simulation. For determining this valuable parameter, experimental tests are the most reliable techniques;however, these measurements are costly and time-consuming. So, it is crucial to propose an empirical model for estimating bubble point pressure. The existing correlations mainly have large errors and develop based on restricted database from a specific geographical location. As a result, development of an all-inclusive correlation is essential. In current article, gene expression programming (GEP) was used to create a generalized model for bubble point pressure estimation. To do this, an all-inclusive source of data was utilized for training and testing the model from the petroleum industry. Several statistical approaches including both illustration tools and diverse error functions were utilized to show the supremacy of the developed GEP model. Consequently, the recommended model is the most accurate as compared to the similar correlations in literature with the average absolute relative error (AARE = 11.41%) and determination coefficient (R-2 = 0.96). Furthermore, the solution gas-oil ratio shows to be the most influencing variable on determining bubble point pressure according to sensitivity analysis. The results of contour map analysis demonstrate that most portions of the experimental region are predicted via the GEP equation with fewer errors as compared to two well-known literature correlations. Finally, the proposed GEP model can be of high prominence for accurate bubble point pressure estimation.

关键词： Bubble point pressure gene expression programming correlation sensitivity analysis comprehensive error analysis

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AI-Based Prognostic Modeling and Performance Optimization of CI Engine Using Biodiesel-Diesel Blends

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2021年第2期11卷 701-708页

作者： Sharma, Prabhakar Sharma, Avdhesh Kr DCR Univ Sci & Technol Mech Engn Dept Murthal 131039 Haryana India

This article describes aspects of the development of an artificial intelligence (AI)-based prognostic modeling and performance optimization of a single-cylinder CI engine powered by biodiesel-diesel blends. It is a tool based on gene expression programming (GEP) followed by response surface methodology (RSM). RSM is employed to establish an explicit mathematical relationship between input and outputs. A database of experimental data on a computerized engine test bench was collected for model development and its testing. The prognostic ability of the GEP model was verified by error analysis, where the coefficient of determination (R-2) and mean absolute percentage error (MAPE) varied marginally within the range of 0.979 +/- 0.020 and 2.15 +/- 0.25, respectively. The model captures adequate trends. Optimum input conditions of engine load, biodiesel-diesel blending ratio, fuel injection pressure, and fuel injection timing are observed to be 60.49 %, 14.32 %, 231.35 bar, and 23.7 degrees bTDC, respectively, while optimized results of brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and peak in-cylinder pressure (P-max) are found to be 24.28 %, 0.3135 kg/kWh, and 58.95 bar, respectively. GEP approach followed by RSM is observed to be a robust tool.

关键词： Artificial Intelligence gene expression programming Response surface methodology diesel engine

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Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Inqiad, Waleed Bin Siddique, Muhammad Shahid Ali, Mujahid Najeh, Taoufik Natl Univ Sci & Technol NUST Mil Coll Engn MCE Islamabad 44000 Pakistan Silesian Univ Tech Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat & Maintenance Operat Maintenance & Acoust Lulea Sweden

The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C-S) of FR-SCC using gene expression programming (GEP) and Multi expression programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses.

关键词： Self-compacting concrete genetic programming Fiber-reinforced self-compacting concrete Multi expression programming gene expression programming

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Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms

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TRANSPORTATION GEOTECHNICS 2021年 30卷

作者： Jalal, Fazal E. Xu, Yongfu Iqbal, Mudassir Jamhiri, Babak Javed, Muhammad Faisal Shanghai Jiao Tong Univ State Key Lab Ocean Engn Dept Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai 200240 Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 KP Pakistan

In this study, gene expression programming (GEP) and multi gene expression programming (MEP) are utilized to formulate new prediction models for determining the compaction parameters (rho(dmax) and wopt) of expansive soils. A total of 195 datasets with five input parameters (i.e., clay fraction C-F, plastic limit w(P), plasticity index IP, specific gravity Gs, maximum dry density rho(dmax)), and two output *** and wopt are collected from the literature comprising 119 internationally published research articles to develop the GEP and MEP models. Simplified mathematical expressions were derived for these models to determine the rho(dmax) and w(opt) of expansive soils. The performance of the models was tested using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). Sensitivity and parametric analyses were also performed on the GEP and MEP models. Additionally, external validation of the models was also verified using commonly recognized statistical criteria. It is clear from the results that the GEP and MEP methods accurately characterize the compaction characteristics of expansive soils resulting in reasonable prediction performance, however, GEP model yielded relatively better performance. Also, the proposed predictive models were compared with previously available empirical models and they exhibited robust and superior performance. Moreover, the rho(dmax) model provided significantly improved results as compared to the w(opt) prediction model in the case of GEP, and vice versa in the MEP model. It is therefore recommended that the proposed GP based models can reliably be used for determining the compaction parameters of expansive soils which effectively reduces the time-consuming and laborious testing, hence attaining sustainability in the field of geoenvironmental engineering.

关键词： Expansive soil gene expression programming Multi expression programming Maximum dry density Optimum moisture content

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Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches

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JOURNAL OF WATER PROCESS ENGINEERING 2024年 58卷

作者： Aldrees, Ali Khan, Majid Taha, Abubakr Taha Bakheit Ali, Mujahid Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia COMSATS Univ Islamabad Civil Engn Dept Abbottabad Campus Abbottabad 22060 Pakistan Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland

Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional modeling approaches rely on extensive datasets with numerous unspecified inputs, leading to time-consuming WQI assessment procedures. Numerous studies have used machine learning (ML) methods for WQI analysis but often lack model interpretability. To address this issue, this study developed five interpretable predictive models, including two gene expression programming (GEP) models, two deep neural networks (DNN) models, and one optimizable Gaussian process regressor (OGPR) model for estimating electrical conductivity (EC) and total dissolved solids (TDS). For the model development, a total of 372 records on a monthly basis were collected in the Upper Indus River at two outlet stations. The efficacy and accuracy of the models were assessed using various statistical measures, such as correlation (R), mean square error (MAE), root mean square error (RMSE), and 5-fold cross-validation. The DNN2 model demonstrated outstanding performance compared to the other five models, exhibiting R-values closer to 1.0 for both EC and TDS. However, the genetic programming-based models, GEP1 and GEP2, exhibited comparatively lower accuracy in predicting the water quality indexes. The SHapely Additive exPlanation (SHAP) analysis revealed that bicarbonate, calcium, and sulphate jointly contribute approximately 78 % to EC, while the combined presence of sodium, bicarbonate, calcium, and magnesium accounts for around 87 % of TDS in water. Notably, the influence of pH and chloride was minimal on both water quality indexes. In conclusion, the study highlights the cost-effective and practical potential of predictive models for EC and TDS in assessing and monitoring river water quality.

关键词： Water quality indexes gene expression programming Deep neural networks Optimizable Gaussian process regressor SHAP

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Modeling soil enzyme activity using easily measured variables: Heuristic alternatives

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APPLIED SOIL ECOLOGY 2021年 157卷 103753-103753页

作者： Ebrahimi, Mitra Sarikhani, Mohammad Reza Shiri, Jalal Shahbazi, Farzin Univ Tabriz Fac Agr Dept Soil Sci Tabriz Iran Univ Tabriz Water Engn Fac Agr Tabriz Iran Univ Tabriz Fac Civil Engn Ctr Excellence Hydroinformat Tabriz Iran

In the present research, gene expression programming (GEP) and artificial neural network (ANN) techniques were used to estimate soil enzyme activity (SEA), including urease, alkaline phosphatase and dehydrogenase. Data from 65 soil samples located in Mirabad region, Suldoz plain (West Azerbaijan, Iran) were used to test the adopted methodology. The soil samples were selected from areas with different land usages (apple orchard, crop production, and rich pasture). Various combinations of the input parameters including soil texture, pH, organic carbon (OC), electrical conductivity (EC), microbial biomass carbon (MBC), and microbial soil respiration (SIR) were utilized to feed the applied models. The root mean square error (RMSE) and the coefficient of determination (R-2) were employed for assessing the models' performance accuracy. The highest R-2 and lowest RMSE were obtained for the models that used all available input parameters. The results showed that among targets (urease activity (UA), alkaline phosphatase (ALP) and/or dehydrogenase activity (DHA)), the highest performance accuracy was obtained for urease activity models. The obtained results revealed that the most effective parameters in estimating urease activity were soil texture, pH, EC and OC;where about 69% and 68% of its variability was predictable by the ANN and GEP, respectively.

关键词： Artificial neural network Dehydrogenase gene expression programming Phosphatase Urease

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