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A data-driven field-scale approach to estimate the permeability of fractured rocks

INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2022年第10期36卷 671-687页

作者： Li, Danqi Shirani Faradonbeh, Roohollah Lv, Adelina Wang, Xiangyu Roshan, Hamid Curtin Univ WA Sch Mines Mineral Energy & Chem Engn Kalgoorlie WA 6430 Australia UNSW Sch Minerals & Energy Resources Engn Sydney NSW Australia Curtin Univ Australasian Joint Res Ctr Bldg Informat Modelli Perth WA Australia

In this study, based on compiled numerical-experimental datasets, important variables, including porosity, density, compressional and shear wave velocity, were utilised to develop a novel model for predicting coal permeability with high accuracy. Multiple linear regression, response surface methodology and gene expression programming were employed to develop empirical models based on the foregoing variables. The performance of the developed models was evaluated using statistical indices. The results showed that the GEP-based model has the best prediction performance compared with other techniques. The proposed GEP-based model with its explicit structure can be readily used in practical applications by field engineers.

关键词： Fractured rocks gene expression programming multiple linear regression permeability response surface methodology

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A novel prediction model for post-fire elastic modulus of circular recycled aggregate concrete-filled steel tubular stub columns

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STEEL AND COMPOSITE STRUCTURES 2022年第3期44卷 295-310页

作者： Memarzadeh, Armin Shahmansouri, Amir Ali Poologanathan, Keerthan Univ Mazandaran Dept Civil Engn Babolsar Iran Northumbria Univ Fac Engn & Environm Newcastle Upon Tyne England

The post-fire elastic stiffness and performance of concrete-filled steel tube (CFST) columns containing recycled aggregate concrete (RAC) has rarely been addressed, particularly in terms of material properties. This study was conducted with the aim of assessing the modulus of elasticity of recycled aggregate concrete-filled steel tube (RACFST) stub columns following thermal loading. The test data were employed to model and assess the elastic modulus of circular RACFST stub columns subjected to axial loading after exposure to elevated temperatures. The length/diameter ratio of the specimens was less than three to prevent the sensitivity of overall buckling for the stub columns. The gene expression programming (GEP) method was employed for the model development. The GEP model was derived based on a comprehensive experimental database of heated and non-heated RACFST stub columns that have been properly gathered from the open literature. In this study, by using specifications of 149 specimens, the variables were the steel section ratio, applied temperature, yielding strength of steel, compressive strength of plain concrete, and elastic modulus of steel tube and concrete core (RAC). Moreover, parametric and sensitivity analyses were also performed to determine the contribution of different effective parameters to the post-fire elastic modulus. Additionally, comparisons and verification of the effectiveness of the proposed model were made between the values obtained from the GEP model and the formulas proposed by different researchers. Through the analyses and comparisons of the developed model against formulas available in the literature, the acceptable accuracy of the model for predicting the post-fire modulus of elasticity of circular RACFST stub columns was seen.

关键词： concrete-filled steel tube elastic modulus gene expression programming post-fire behavior recycled aggregate concrete

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An expert system for predicting the infiltration characteristics

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WATER SUPPLY 2022年第3期22卷 2847-2862页

作者： Singh, Balraj Ebtehaj, Isa Sihag, Parveen Bonakdari, Hossein Panipat Inst Engn & Technol Dept Civil Engn Panipat 132102 India Laval Univ Dept Soils & Agrifood Engn Laval PQ G1V0A6 Canada Chandigarh Univ Dept Civil Engn Chandigarh India

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. This study includes a comparative analysis of the two machine learning techniques;M5P model tree (M5P) and gene expression programming (GEP) in predictions of the infiltration characteristics. The models were trained and tested using the 7 combination (CMB1 - CMB7) of input parameters;moisture content (m), bulk density of soil (D), percentage of the silt (SI), sand (SA) & Clay (C), and time (t), with output parameters;cumulative infiltration (CI) and infiltration rate (IR). Results suggested that GEP has an edge over M5P to predict the IR and CI with R, RMSE & MAE values 0.9343, 15.9667 mm/hr & 8.7676 mm/hr, and 0.9586, 9.2522 mm and 7.7865 mm for IR and CI, respectively with CMB1. Although the M5P model also gave good results with R, RMSE & MAE values 0.9192, 14.1821 mm/hr, & 19.2497 mm/hr, and 0.8987, 11.2144 mm & 18.4328 mm for IR and CI, respectively, but lower than GEP. Furthermore, single-factor ANOVA and uncertainty analysis were used to show the significance of the predicted results and to find the most efficient soft computing techniques respectively.

关键词： cumulative infiltration gene expression programming infiltration rate M5P model tree

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Effects of machining parameters and reinforcement content on thrust force during drilling of hybrid composites

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MATERIALS TESTING 2022年第3期58卷 280-284页

作者： Aldas, Kemal Ozkul, Iskender Hayajneh, Mohammed T. Aksaray Univ Fac Engn Aksaray Turkey Aksaray Univ Dept Mech Engn Aksaray Turkey Jordan Univ Sci & Technol Dept Ind Engn Irbid Jordan

In this study, aluminum metal matrix composites were fabricated using the powder metallurgy technique with different ratios of alumina (Al2O3) and graphite as reinforcing elements. The obtained workpieces were then drilled using different machining parameters and the thrust force was measured using a force dynamometer. The thrust force data were modeled using a gene programming soft-computing technique and a function was obtained using the input parameters.

关键词： gene expression programming drilling thrust force metal matrix composites Al2O3 reinforcement

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A COMPARATIVE STUDY ON POWER CALCULATION METHODS FOR CONVEYOR BELTS IN MINING INDUSTRY

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2022年第1期36卷 26-45页

作者： Koken, Ekin Lawal, Abiodun Ismail Onifade, Moshood Ozarslan, Ahmet Abdullah Gul Univ Dept Nanotechnol Engn Kayseri Turkey Fed Univ Technol Akure Dept Min Engn Akure Nigeria Univ Nambia Dept Civil & Min Engn Ongwediva Namibia Zonguldak Bulent Ecevit Univ Dept Min Engn Zonguldak Turkey

This paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS's operational features, the power consumption (P-c, kW) was measured directly on motorised head-pulleys. The P-c was investigated through several conventional, statistical, and machine learning methods. This study shows that the DIN 22,101 could be the most convenient conventional method for the investigated CBSs. On the other hand, based on the nonlinear regression (NLR) and genetic expression programming (GEP) models, two new approaches were suggested for the design and optimisation of the P-c.

关键词： Conveyor belt mining power calculation regression algorithms gene expression programming

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Using the group method for the synthesis of copper/ZrO2 nanocomposites to achieve high wear resistance by ball milling and spark plasma sintering

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CERAMICS INTERNATIONAL 2022年第12期48卷 17576-17588页

作者： Shojaei, Mohammdreza Hasani, Atefe Amiri, Zahra Khayati, Gholam Reza Sharif Univ Technol Dept Mat Sci & Engn Tehran Iran Shahid Bahonar Univ Kerman Dept Mat Sci & Engn POB 76135-133 Kerman Iran

The use of ZrO2 nanoparticles is common in the Cu matrix to manufacture Cu/ZrO2 nanocomposites. Many approaches and parameters produce Cu/ZrO2 nanocomposites with an appropriate wear rate and other properties. Hence, proposing an accurate and reliable model was imperative. The main goal of this paper is to find out the best models among the ant colony optimization (ACO), gene expression programming (GEP), artificial bee colony (ABC), and gray wolf optimization algorithm (GWOA) to predict the wear rate of/ZrO2 nanocomposites. To the best of our knowledge, this is the first research that predicts Cu/ZrO2 nanocomposites wear rate using group modeling. To develop models, 105 data were collected from the Cu/ZrO2 manufacturing by ball milling and spark plasma sintering in different conditions. The input parameters are Cu and ZrO2 concentration, milling time, and sintering temperature. The proposed rules for the prediction of wear rate based on the collected dataset are assessed based on several metrics such as (R square), mean absolute error percentage (MAPE), root mean square error (RRSE), mean square error (MSE), relative error and average absolute relative. According to the results, the ACO enables to predict wear rate by R square = 0.9923, MAPE = 5.2, RRSE = 0.0143, and MSE = 0.0043. Also, the sensitivity analysis results revealed that the milling time and ZrO2 content are the most effective parameters on the wear rate of Cu/ZrO2 nanocomposites. Moreover, experiments were done at the best model to prove the validity of the ACO. According to the results, it can be concluded that the ACO model and real analysis are fully functional to accurately estimate the optimal conditions for ball milling conditions in the production of Cu/ZrO2 nanocomposites.

关键词： Cu/ZrO2 nanocomposite Spark plasma sintering Modeling Ant colony optimization gene expression programming Artificial bee colony Gray wolf optimization algorithm

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Evaluation of liquefaction potential by energy-basil and stress-based methods and gene expressing programming (case study: Tabriz city)

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GEOMECHANICS AND GEOENGINEERING-AN INTERNATIONAL JOURNAL 2023年第6期18卷 517-538页

作者： Alamdari, Armin Sahebkaram Dabiri, Rouzbeh Jani, Rasoul Sarand, Fariba Behrouz Islamic Azad Univ Dept Civil Engn Tabriz Branch Tabriz Iran

Liquefaction in soil layers is a vital factor intensifying earthquake *** study compares the numerical evaluation process and the results of two methods based on stress and energy. It is inferred from the calculations that the stress-based method predicts a higher liquefaction potential with a lower safety factor as it promises liquefaction in deeper soil layers. In return, liquefaction tends to occur at a shallower depth with higher intensity in the energy-based method. Through applying the two approaches based on the data collected from different areas around Tabriz, a liquefaction-zoning map is presented. Despite being far from the fault, the central to the western and the southwestern parts of Tabriz has a high liquefaction potential. Eventually, based on the evaluated liquefaction potential using the stated methods and adopting the gene expression programming (GEP) approach, an equation is introduced to estimate the liquefaction potential for the case study. The predictions of the proposed models were consistent with the findings of experimental methods, demonstrating appropriate statistical measures and parametric analysis. It can also be concluded from the results of the parametric analysis, that the parameters of maximum acceleration, earthquake magnitude, and SPT number have more great impact on soil liquefaction.

关键词： Liquefaction potential energy base method stress base method zoning map prediction model gene expression programming Tabriz city

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An automatic hyperparameter optimization DNN model for precipitation prediction

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APPLIED INTELLIGENCE 2022年第3期52卷 2703-2719页

作者： Peng, Yuzhong Gong, Daoqing Deng, Chuyan Li, Hongya Cai, Hongguo Zhang, Hao Nanning Normal Univ Sch Comp & Informat Engn Nanning 530001 Peoples R China Fudan Univ Coll Comp Sci & Technol Shanghai 200433 Peoples R China Shangqiu Univ Appl Sci & Technol Coll Dept Sci Kaifeng 475000 Peoples R China Guangxi Coll Educ Dept Math & Comp Sci Nanning 530023 Peoples R China

Deep neural networks (DNN) have gained remarkable success on many rainfall predictions tasks in recent years. However, the performance of DNN highly relies upon the hyperparameter setting. In order to design DNNs with the best performance, extensive expertise in both the DNN and the problem domain under investigation is required. But many DNN users have not met this requirement. Therefore, it is difficult for the users who have no extensive expertise in DNN to design optimal DNN architectures for their rainfall prediction problems that is to solve. In this paper, we proposed a novel automatic hyperparameters optimization method for DNN by using an improved gene expression programming. The proposed method can automatically optimize the hyperparameters of DNN for precipitation modeling and prediction. Extensive experiments are conducted with three real precipitation datasets to verify the performance of the proposed algorithm in terms of four metrics, including MAE, MSE, RMSE, and R-Squared. The results show that: 1) the DNN optimized by the proposed method outperforms the existing precipitation prediction methods including Multiple Linear Regression (MLR), Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF) and DNN;2) the proposed DNN hyperparameter optimization method outperforms state-of-the-art DNN hyperparameter optimization methods, including genetic Algorithm, Bayes Search, Grid Search, Randomized Search, and Quasi Random Search.

关键词： Deep neural networks Precipitation prediction Neural structure optimization Neural architecture search Hyperparameter optimization gene expression programming

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Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2022年第6期23卷 2083-2098页

作者： Eleyedath, Abhary Swamy, Aravind Krishna Indian Inst Technol Delhi Dept Civil Engn New Delhi India

Due to the issues like operational difficulties, and extensive resource requirements at pavement design stage, dynamic modulus (vertical bar E*vertical bar) is estimated using predictive models. However, these models suffer from issues like systematic bias, prediction accuracy, and extensive testing requirements. This study presents a novel hybrid Principal Component Analysis (PCA) - gene expression programming (GEP) approach to predict the vertical bar E*vertical bar of asphalt concrete. The database developed during NCHRP 9-19 study was used for developing this methodology. The information of all properties (i.e. variables) was used as input. PCA helped in removing the redundancy at the input stage while reducing the dimensionality. The extracted principal components (PC's) were used to develop first set of vertical bar E*vertical bar predictive models. The second set of vertical bar E*vertical bar predictive models were developed using the parameters mostly contributing to the individual PC's. Comparison of these two sets indicated that predictive model obtained using variables as direct input resulted in improved accuracy. Comparison of this finalized model with the existing regression-based equations using goodness of fit indicators indicated that proposed hybrid model offers efficient and accurate alternative. The proposed model has flexibility to be used with any new database with recalibration.

关键词： Dynamic modulus gene expression programming principal component analysis machine learning technique Witczak model Hirsch model al-Khateeb model complex modulus binder shear modulus

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Comparative evaluation of AI-based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4-coated MWCNT hybrid nanofluids for potential application in energy systems

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第13期46卷 19242-19257页

作者： Sharma, Prabhakar Said, Zafar Memon, Saim Elavarasan, Rajvikram Madurai Khalid, Mohammad Xuan Phuong Nguyen Anh Tuan Hoang Lan Huong Nguyen Arici, Muslum Delhi Skill & Entrepreneurship Univ Sch Engn Sci Delhi India Univ Sharjah Dept Sustainable & Renewable Energy Engn POB 27272 Sharjah U Arab Emirates Univ Sci & Technol NUST US Pakistan Ctr Adv Studies Energy USPCAS E Islamabad Pakistan London South Bank Univ Sch Engn London Ctr Energy Engn Solar Thermal Vacuum Engn Res Grp London England Texas A&M Univ Clean & Resilient Energy Syst CARES Lab Galveston TX USA Nestl Pvt Ltd Res & Dev Unit Power & Energy Chennai Tamil Nadu India Sunway Univ Sch Engn & Technol Graphene & Adv 2D Mat Res Grp GAMRG Petaling Jaya Malaysia Ho Chi Minh City Univ Transport PATET Res Grp Ho Chi Minh City Vietnam Kocaeli Univ Engn Fac Mech Engn Dept Umuttepe Campus Kocaeli Turkey HUTECH Univ Inst Engn Ho Chi Minh City Vietnam Vietnam Maritime Univ Sch Mech Engn Haiphong Vietnam

Hybrid nanofluids are gaining popularity owing to the synergistic effects of nanoparticles, which provide them with better heat transfer capabilities than base fluids and normal nanofluids. The thermophysical characteristics of hybrid nanofluids are critical in shaping heat transmission properties. As a result, before using thermophysical qualities in industrial applications, an in-depth investigation of thermophysical properties is required. In this paper, a metamodel framework is constructed to forecast the effect of nanofluid temperature and concentration on numerous thermophysical parameters of Fe3O4-coated MWCNT hybrid nanofluids. Evolutionary gene expression programming (GEP) and an adaptive neural fuzzy inference system (ANFIS) were employed to develop the prediction models. The model was trained using 70% of the datasets, with the remaining 15% used for testing and validation. A variety of statistical measurements and Taylor's diagrams were used to assess the proposed models. The Pearson's correlation coefficient (R), coefficient of determination (R-2) was used for the regression index, the error in the model was evaluated with root mean squared error (RMSE). The model's comprehensive assessment additionally includes modern model efficiency indices such as Kling-Gupta efficiency (KGE) and Nash-Sutcliffe efficiency (NSCE). The proposed models demonstrated impressive prediction capabilities. However, the GEP model (R > 0.9825, R-2 > 0.9654, RMSE = 0.7929, KGE > 0.9188, and NSCE > 0.9566) outperformed the ANFIS model (R > 0.9601, R-2 > 0.9218, RMSE = 1.495, KGE > 0.8015, and NSCE > 0.8745) for the majority of the findings. The generated metamodel was robust enough to replace the repetitive expensive lab procedures required to measure thermophysical properties. Highlights Predictions of thermophysical properties of Fe3O4-coated MWCNT hybrid nanofluid AI-based ANFIS and GEP models performed well on statistical indices ANFIS and GEP-based prognostic models validat

关键词： artificial intelligence gene expression programming machine learning nanofluids neuro fuzzy thermophysical properties

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