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Explicit data-driven models for prediction of pressure fluctuations occur during turbulent flows on sloping channels

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2020年第5期34卷 691-707页

作者： Samadi, Mehrshad Sarkardeh, Hamed Jabbari, Ebrahim IUST Sch Civil Engn POB 16765-163 Tehran Iran Hakim Sabzevari Univ Dept Civil Engn POB 961797648 Sabzevar Iran

Pressure fluctuations are among the favorite topics for hydraulic engineers due to their critical role in the design and safe operation of hydraulic structures. In this study, three explicit data-driven models, including Classification and Regression Trees (CART), gene expression programming (GEP), and Multivariate Adaptive Regression Splines (MARS), are used. These methods are applied to predict the pressure fluctuation intensity oC 0 PTHORN, which occurs beneath a hydraulic jump on sloping channels using reliable experimental observations. The main features of the suggested data-driven models are their capability to provide practical formulas for prediction of C0 P based on the dimensionless form of the effective parameters. The application of these models leads to the extraction of knowledge from data in the form of predictive formulas. In addition, to evaluate the accuracy and performance of the proposed models, some statistical indices and several validation criteria are employed. The obtained results confirm that MARS predicts C 0 P better than CART and GEP. Based on the effective independent variables, MARS presents a simple predictive formula for the computation of C 0 P. GEP-based equations use mathematical trigonometric functions that cause a more complicated and time-consuming process to predict C0 P. However, the application of simple rules generated by CART enables hydraulic engineers to predict C0 P, easily and quickly.

关键词： Multivariate adaptive regression splines gene expression programming Decision trees Pressure fluctuation Hydraulic structures Turbulent flow

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Exterior RC joints subjected to monotonic and cyclic loading

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ENGINEERING COMPUTATIONS 2020年第7期37卷 2319-2336页

作者： Murad, Yasmin Abdel-Jabar, Haneen Diab, Amjad Abu Hajar, Husam Univ Jordan Dept Civil Engn Amman Jordan

Purpose The purpose of this study is to develop two empirical models that predict the shear strength of exterior beam-column joints exposed to monotonic and cyclic loading using gene expression programming (GEP). Design/methodology/approach The GEP model developed for the monotonic loading case is trained and validated using 81 data test points and that for cyclic loading case is trained and validated using 159 data test points that collected from different 9 and 39 experimental programs, respectively. The parameters that are selected to develop the cyclic GEP model are concrete compressive strength, joint aspect ratio, column axial load and joint transverse reinforcement. The monotonic GEP model is developed using concrete compressive strength, column depth, joint width and column axial load. Findings GEP models are proposed in this paper to predict the joint shear strength of beam-column joints under cyclic and monotonic loading. The predicted results obtained using the GEP models are compared to those calculated using the ACI-352 code formulations. A sensitivity analysis is also performed to further validate the GEP models. Originality/value The proposed GEP models provide an accurate prediction for joint shear strength of beam-column joints under cyclic and monotonic loading that is more fitting to the experimental database than the ACI-352 predictions where the GEP models have higher R-2 value than the code formulations.

关键词： Cyclic loading gene expression programming ACI Joint shear strength Monotonic loading

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Deep neural network-based predictive modeling of road accidents

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NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12417-12426页

作者： Singh, Gyanendra Pal, Mahesh Yadav, Yogender Singla, Tushar DeenbandhuChhotu Ram Univ Sci & Technol Civil Engn Dept Sonepat Haryana India Natl Inst Technol Dept Civil Engn Kurukshetra Haryana India

This work proposes to use deep neural networks (DNN) model for prediction of road accidents. DNN consists of two or more hidden layers with large number of nodes. Accident data of non-urban sections of eight highways were collected from official records, and dataset consists of a total of 2680 accidents. The data of 16 explanatory variables related to road geometry, traffic and road environment were collected from official records as well as through field studies. Out of a total of 222 data points of accident frequency, 148 were used for training and remaining 74 to test the models. To compare the performance of DNN-based modeling approach, gene expression programming (GEP) and random effect negative binomial (RENB) models were used. A correlation coefficient value of 0.945 (root mean square error = 5.908) was achieved by DNN in comparison with 0.914 (RMSE = 7.474) by GEP, and 0.891 (RMSE = 8.862) by RENB with the test dataset, indicating an improved performance by DNN in prediction of road accidents. In comparison with DNN, though lower value of correlation coefficient was achieved by GEP model, it quantified the effects of various variables on accident frequency and provided a ranked list of variables based upon their importance.

关键词： gene expression programming Deep neural network Back-propagation neural network Random effect negative binomial model Accident prediction model Road safety

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Hybrids of machine learning techniques and wavelet regression for estimation of daily solar radiation

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第12期36卷 4255-4269页

作者： Mohsenzadeh Karimi, Sahar Mirzaei, Majid Dehghani, Adnan Galavi, Hadi Huang, Yuk Feng Simon Fraser Univ Dept Geog Burnaby BC Canada Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur Malaysia Univ Putra Malaysia Fac Engn Serdang Selangor Malaysia Univ Zabol Dept Water Sci & Engn Zabol Iran Univ Tunku Abdul Rahman Fac Sci & Engn Dept Civil Engn Kajang Malaysia

As a primary input in meteorology, the accuracy of solar radiation simulations affects hydrological, climatological, and agricultural studies and sustainable development practices and plans. With the advent of machine learning models and their proven capabilities in modelling the hydro-meteorological phenomena, it is necessary to find the best model suitable for each phenomenon. Models performance depends upon their structure and the input data set. Therefore, some well-known and newest machine learning models with different inputs are tested here for solar radiation simulation in Illinois, USA. The data mining models of Support Vector Machine (SVM), gene expression programming (GEP), Long Short-Term Memory (LSTM), and their combination with the wavelet transformation building a total of six model structures are applied to five data sets to examine their suitability for solar radiation simulation. The five input data sets (SCN_1 to SCN_5) are based on five readily accessible parameters, namely extraterrestrial radiation (R-a), maximum and minimum air temperature (T-min, T-max), corrected clear-sky solar irradiation (ICSKY), and Day of Year (DOY). The LSTM outperformed other models, consulting the performance measures of RMSE, SI, MAE, SSRMSE, and SSMAE. Of the different input data sets, in general, SCN_4 was the best input scenario for predicting global daily solar radiation using Ra, Tmax, Tmin, and DOY variables. Overall, six machine learning based models showed acceptable performances for estimating solar radiation, with the LSTM machine learning technique being the most recommended.

关键词： Daily solar radiation Support vector machine gene expression programming Wavelet decomposition Long short-term memory

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Experimental evaluation of two new GEP-based ensemble classifiers

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第9期38卷 10932-10939页

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat PL-80952 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

The paper proposes applying gene expression programming (GEP) to induce ensemble classifiers. Two new algorithms inducing such classifiers are proposed. The proposed ensemble classifiers use two different measures to select genes produced by the gene expression programming procedure. Selection of genes from the set of the non-dominated ones in the process of meta-learning is supported by a genetic algorithm. Integration of genes (i.e. learners) is based on the majority voting. The proposed algorithms were validated experimentally using several datasets and the results were compared with those of other well established classification methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： gene expression programming Classification

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A new prediction method for the rheological behavior of grout with bottom ash for jet grouting columns

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SOILS AND FOUNDATIONS 2017年第3期57卷 384-396页

作者： Gullu, Hamza Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey

Among the many variables involved in jet grout technology, dealing with the complex phenomena of grout flow, specifically related to the pumping pressure (shear stress), the pumping rate (shear rate) and the viscosity, mostly becomes a difficult task for grouting in practice. Thus, this study presents the capability of a new methodology in soft computing techniques, called gene expression programming (GEP), to predict the rheological behavior (i.e., the shear stress versus the shear rate and the viscosity versus the shear rate) of grout with bottom ash for jet grouting columns, as an alternative approach to traditional methods. For this purpose, shear stress and viscosity formulas, including the main input variables of the shear rate and bottom ash proportion, are derived using GEP modeling through the stages of production and testing. Then, the performances of the GEP formulas are compared with the measured data and the regression and conventional rheological models (De Kee and Robertson-Stiff) for use in practice. The results indicate that the GEP formulas are able to yield estimations with good precision resulting in better predictions (R > 0.96) compared to the regression model. A successful description of the pseudoplastic response of rheological behavior is given, and a response consistent with conventional rheological models is obtained. Moreover, the measured data (shear stress and viscosity) generally follow the GEP modeling well, but the level of satisfaction is more favorable at high proportions of bottom ash. In conclusion, the study reveals that the derived GEP formulas are relatively promising for estimating the pumping pressure, the viscosity and the pumping rate of grout with bottom ash for jet grouting columns, at least in assisting conventional methods for preliminary designs. (C) 2017 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society. This is an open access article under the CC BY-NC-ND license.

关键词： Jet grouting column Grout Bottom ash Rheological behavior gene expression programming

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Suspended Load Modeling of River Using Soft Computing Techniques

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WATER RESOURCES MANAGEMENT 2024年第6期38卷 1965-1986页

作者： Moradinejad, Amir Agr Res Educ & Extent Org AREEO Markazi Agr & Nat Resources Res & Educ Ctr Soil Conservat & Watershed Management Res Dept Arak Iran

The phenomenon of sediment transport has always affected many river and civil structures. Not knowing the exact amount of sediment, causes much damage. Correct estimation of river sediment concentration is essential for planning and managing water resources projects and environmental issues. For this, you can use the artificial intelligence method, which has high flexibility. In this research, adaptive neuro-fuzzy models (ANFIS), gene expression programming (GEP), support vector regression (SVR), Group Method of Data Handling (GMDH), and the classical method of sediment rating curve (SRC) were used to model and prediction. For this purpose, the daily data of temperature, rainfall, sediment, and discharge of the Jalair station located in the Markazi province of Iran were used. The results obtained from these five methods were compared with each other and with the measured data. To evaluate the methods used, correlation coefficient, root mean square error, mean absolute error, and Taylor diagram were used. The results show the acceptable performance of data mining methods compared to the Sediment rating curve. Also, the model's superiority (GEP) was shown with the highest coefficient of determination R2 with a value of 0.98 and the lowest root mean square error RMSE in terms of tons per day with a value of 3721. The efficiency of the ANFIS and GMDH model with R2 values of 0.93, 0.98, and RMSE values of 16556, and 18638 was somewhat better than the SVR model with an R2 value of 0.90 and RMSE value of 35158.

关键词： Suspended load gene expression programming Jalair Support vector regression Adaptive neuro-fuzzy interference system GMDH

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A new conventional criterion for the performance evaluation of gang saw machines

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MEASUREMENT 2019年 146卷 159-170页

作者： Haghshenas, Sina Shaffiee Faradonbeh, Roohollah Shirani Mikaeil, Reza Haghshenas, Sami Shaffiee Taheri, Abbas Saghatforoush, Amir Dormishi, Alireza Urmia Univ Technol Fac Min & Met Engn Orumiyeh Iran Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia Tarbiat Modares Univ Fac Engn Tehran 14115143 Iran Shahrood Univ Technol Fac Min Petr & Geophys Shahrood Iran

The process of cutting dimension stones by gang saw machines plays a vital role in the productivity and efficiency of quarries and stone cutting factories. The maximum electrical current (MEC) is a key variable for assessing this process. This paper proposes two new models based on multiple linear regression (MLP) and a robust non-linear algorithm of gene expression programming (GEP) to predict MEC. To do so, the parameters of Mohs hardness (Mh), uniaxial compressive strength (UCS), Schimazek's F-abrasiveness factor (SF-a), Young's modulus (YM) and production rate (Pr) were measured as input parameters using laboratory tests. A statistical comparison was made between the developed models and a previous study. The GEP-based model was found to be a reliable and robust modelling approach for predicting MEC. Finally, according to the conducted parametric analysis, Mh was identified as the most influential parameter on MEC prediction. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Gang saw machine Carbonate rocks Cutting dimension stones Maximum electrical current gene expression programming Multiple linear regression

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Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils

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INTERNATIONAL JOURNAL OF CIVIL ENGINEERING 2014年第1B期12卷 55-64页

作者： Shahnazari, H. Shahin, M. A. Tutunchian, M. A. Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Curtin Univ Dept Civil Engn Perth WA 6845 Australia

Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial regression (EPR), classical genetic programming (GP), and gene expression programming (GEP), are utilized to obtain more accurate predictive settlement models. The models are developed using a large databank of standard penetration test (SPT)-based case histories. The values obtained from the new models are compared with those of the most precise models that have been previously proposed by researchers. The results show that the new EPR and GP-based models are able to predict the foundation settlement on cohesionless soils under the described conditions with R-2 values higher than 87%. The artificial neural networks (ANNs) and genetic programming (GP)-based models obtained from the literature, have R-2 values of about 85% and 83%, respectively which are higher than 80% for the GEP-based model. A subsequent comprehensive parametric study is further carried out to evaluate the sensitivity of the foundation settlement to the effective input parameters. The comparison results prove that the new EPR and GP-based models are the most accurate models. In this study, the feasibility of the EPR, GP and GEP approaches in finding solutions for highly nonlinear problems such as settlement of shallow foundations on granular soils is also clearly illustrated. The developed models are quite simple and straightforward and can be used reliably for routine design practice.

关键词： Shallow foundations Settlement prediction Evolutionary polynomial regression genetic programming gene expression programming Cohesionless soils

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Computer-aided modeling for predicting layer thickness of a duplex treated ceramic coating on tool steels

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CERAMICS INTERNATIONAL 2014年第4期40卷 5515-5522页

作者： KhalaJ, Gholamreza Pouraliakbar, Hesam Islamic Azad Univ Dept Mat Engn Saveh Branch Saveh Iran WT SRC Dept Adv Mat Tehran Iran

Five different tool steels (DIN 1.2080, 1.2210, 1.2344, 1.2510 and 1.3343) have been targeted for a duplex surface treatment consisted of nitriding followed by vanadium thermo-reactive diffusion (IRD). TRD"process was performed in molten salt bath at 575, 650 and 725 degrees C for 1 to 15 h. A duplex ceramic coating of vanadium carbonitride (VCN) with a thickness up to 10.2 mu m was formed on tool steel substrates. Characterization of the ceramic coating by means of scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) indicated that the diffused compact and dense layers mainly consisted of V(C,N) and V-2(C,N) phases. Layer thickness of duplex coating has been modeled by gene expression programming (GEP). Recently, application of GEP as a computer-aided technique has got appreciable attraction especially for modeling and to formulate engineering demands. For GEP approaches, chemical composition of steel substrates along with different bath and processing parameters totally composed of 17 different parameters were considered as inputs to establish mathematical correlations. Finally, the training and testing results in models have shown strong potential for predicting the layer thickness of duplex treated ceramic coating on tool steels. (C) 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

关键词： Duplex surface treatment Ceramic coating Layer thickness gene expression programming Modeling

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