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PREDICTION OF TBM PENETRATION RATE OF WATER CONVEYANCE TUNNELS IN IRAN USING MODERN METHODS

CIVIL ENGINEERING JOURNAL-STAVEBNI OBZOR 2020年第4期30卷 459-473页

作者： Afradi, Alireza Ebrahimabadi, Arash Hallajian, Tahereh Islamic Azad Univ Dept Min & Geol Qaemshahr Branch Qaemshahr Iran

TBM has been used extensively in civil engineering activities and plays an important role in tunnelling projects. Hence, the penetration rate of these machines plays a crucial role in the success of their application. Therefore, in order to predict the TBM penetration rate in this study in several water conveyance tunnels including the tunnels of Karaj, Ghomrood, Golab, Nosoud and Sabzkooh, four intelligence techniques including multiple linear and nonlinear regression analysis, gene expression programming (GEP) method, and Support Vector Machine (SVM) were applied. The obtained values of R-2 and RMSE included 0.43 and 3.08 for linear regression, 0.68 and 2.3 for nonlinear regression, 0.74 and 2.09 for GEP method and 0.97 and 0.6 for SVM method, respectively which were utilized to predict TBM penetration rate. By investigating the tunnels database, the results indicated that the SVM method had the most accurate prediction of penetration rate (in terms of R-2 and RMSE) and the maximum amount of R-2 and the minimum amount of RMSE among all predictive modelings. Finally, respecting the amount of R-2 and RMSE, the other methods like GEP method, nonlinear regression, and linear regression are listed to have the required accuracy in predicting penetration rate.

关键词： Linear regression Non-linear regression gene expression programming Support vector machine

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Predicting Standardized Streamflow index for hydrological drought using machine learning models

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2020年第1期14卷 339-350页

作者： Shamshirband, Shahabbodin Hashemi, Sajjad Salimi, Hana Samadianfard, Saeed Asadi, Esmaeil Shadkani, Sadra Kargar, Katayoun Mosavi, Amir Nabipour, Narjes Chau, Kwok-Wing Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh Vietnam Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh Vietnam Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Urmia Univ Fac Engn Dept Civil Engn Orumiyeh Iran Obuda Univ Kalman Kando Fac Elect Engn Budapest Hungary Oxford Brookes Univ Sch Built Environm Oxford England Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Hong Kong Peoples R China

Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), gene expression programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186.

关键词： gene expression programming hydrological drought M5 model tree machine learning models Standardized Streamflow index support vector regression

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Life cycle assessment of eco-friendly concrete mixtures incorporating natural zeolite in sulfate-aggressive environment

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CONSTRUCTION AND BUILDING MATERIALS 2021年 268卷 121136-121136页

作者： Shahmansouri, Amir Ali Bengar, Habib Akbarzadeh AzariJafari, Hessam Univ Mazandaran Dept Civil Engn Babolsar Iran MIT Dept Civil & Environm Engn Cambridge MA 02139 USA

The incorporation of natural pozzolans into the binder has been considered as an effective method for reducing the climate change impact of concrete mixtures. When quantifying the environmental impacts of concrete mixtures with and without natural pozzolans and with equivalent mechanical properties, their lifespan has been usually overlooked. The importance of lifespan in the life cycle assessment (LCA) is more pronounced when the concrete structure is exposed to an aggressive environment. In this study, the system boundaries of LCA were expanded to a cradle-to-grave scope and the lifespan of concrete mixtures was incorporated into the reference flow calculation. To have a representative experimental result for the lifespan calculation, 54 mixtures with three binder contents and three water-to-cementitious materials ratios with six replacement rates of natural zeolite (NZ) were cast. The hardened specimens were tested against sulfate attack, the prevalent deterioration mechanism in the Caspian Sea. The experimental program was followed by a gene expression programming (GEP) modeling to predict the strength reduction and service life of the mixtures. The contribution of the concrete mixtures with and without NZ to global warming potential (GWP) was evaluated using the characterization factors of the IPCC 2013 model for a 100-year time horizon. The results show that the mechanical and durability performances of NZ mixtures are better than those in the mixtures without NZ. The incorporation of 20% NZ considerably increased the durability properties of mixtures, for example, a 27% reduction in water permeability. Based on the test results, the service life of the modified concrete exposed to a sodium sulfate attack was precisely predicted via the GEP method. The LCA results indicate that replacing 20% of PC with NZ reduced GWP by up to 69.7%, suggesting NZ has the potential to make an eco-friendly concrete mixture. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Service life estimation Sulfate attack Durability LCA gene expression programming Climate change

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A predictive model for the bond strength of near-surface-mounted FRP bonded to concrete

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COMPOSITE STRUCTURES 2021年 262卷 113618-113618页

作者： Zhang, Ruiliang Xue, Xinhua Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China

This paper presents two artificial intelligence techniques (e.g., gene expression programming (GEP) and random forest (RF)) for predicting the bond strength of near-surface-mounted (NSM) fiber-reinforced polymer (FRP) strips or rods bonded to concrete. Experimental data from 145 direct pullout tests collected from the literature and five parameters, namely, the bond length, FRP axial rigidity, groove depth-to-width ratio, epoxy tensile strength, and concrete compressive strength, were used to develop the GEP and RF models. A comparison was conducted between the proposed GEP and RF models and two existing empirical models, namely, Seracino's model and Zhang's model, and six statistical indices were used to evaluate the performance of these four models. The results show that the proposed GEP and RF models had higher coefficient of determination (R-2) values and lower root mean squared error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), mean absolute percentage error (MAPE), and integral absolute error (IAE) values than the two existing empirical models. Finally, a detailed parametric study was conducted to investigate the influence of each input variable on the bond strength. The results showed that the bond strength increased with increasing bond length, FRP axial rigidity, groove depth-to-width ratio, and concrete compressive strength, while the epoxy tensile strength had little effect on the bond strength.

关键词： gene expression programming Random forest Fiber-reinforced polymer Bond strength Concrete

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Transformer Fault Diagnosis Based on gene expression programming Classifier

Transformer Fault Diagnosis Based on Gene Expression Program...

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International Conference on Energy, Environment and Sustainable Development (ICEESD 2011)

作者： Dong, Zhuo Zhu, Yongli N China Elect Power Univ Baoding 071003 Hebei Peoples R China

ISBN: (纸本)9783037852668

gene expression programming (GEP), which is suitable for transformer fault diagnosis Classification, is combined with transformer oil dissolved gas analysis (DGA), and also a method of transformer fault diagnosis based on self-adaptive GEP classification algorithm is proposed. We choose 400 groups of DGA measured data which includes a variety of failure and does not redundant as the training samples and test samples of the GEP classifier. A large number of diagnostic examples show that the proposed self-adaptive classification GEP is suitable for transformer fault diagnosis, and its performance is better than using Naive Bayes (NB) classifier, BP network and Immune classification.

关键词： gene expression programming DGA data normalization transformer fault diagnosis

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Mathematical modeling for a new portfolio selection problem in a bubble condition using a new risk measure

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SCIENTIA IRANICA 2021年第5期28卷 2812-2829页

作者： Ghahtarani, A. Sheikhmohammady, M. Najafi, A. A. Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran KN Toosi Univ Technol Fac Ind Engn Tehran Iran

A portfolio selection model is developed in this study using a new risk measure. The proposed risk measure is based on the fundamental value of stocks. For this purpose, a mathematical model is developed and transformed into an integer linear programming. In order to analyze its efficiency, the actual data of the Tehran Stock Exchange market are used in 12 scenarios to solve the proposed model. In order to evaluate the scenarios, data mining approaches are employed. Data mining methods used in this paper include Adaptive Neuro-Fuzzy Inference System (ANFIS), decision tree, random forest, Fisher Discriminant Analysis (FDA), and gene expression programming (GEP). The best method for scenario evaluation is GEP based on numerical results. Hence, the market values are evaluated by this algorithm. Software packages like MATLAB, GEP xpero tools, and LINGO are used to solve the model. Different trends of market value and fundamental value volatility in the optimum stock portfolio are determined. It is possible to examine the optimum portfolio profitability in different scenarios. By using real-world data, trends are extracted and analyzed. Results show that the developed model can be effectively applied in bubble situations. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Decision tree Financial bubble Fundamental value gene expression programming Portfolio selection problem Risk measure

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A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

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MATERIALS 2021年第7期14卷 1792页

作者： Das, Sourav Mansouri, Iman Choudhury, Satyabrata Gandomi, Amir H. Hu, Jong Wan Natl Inst Technol Silchar Dept Civil Engn Silchar 788010 Assam India Birjand Univ Technol Dept Civil Engn Birjand 97175569 Iran Univ Technol Sydney Fac Engn & IT Ultimo NSW 2007 Australia Incheon Natl Univ Dept Civil & Environm Engn Incheon 22012 South Korea Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon 22012 South Korea

Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames.

关键词： effective stiffness ratio reinforced concrete columns performance-based design experimental data gene expression programming unified performance-based design

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gene expression programming for Static Security Assessment of Power Systems

Gene Expression Programming for Static Security Assessment o...

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IEEE Power and Energy Society general Meeting

作者： H. M. Khattab A. Y. Abdelaziz S. F. Mekhamer M. A. L. Badr E. F. El-Saadany Engineering for the Petroleum and Process Industries (ENPPI) Cairo Egypt Department of Electrical Power and Machines Faculty of Engineering Ain Shams University Abbasia Cairo Egypt Electrical and Computer Engineering Department University of Waterloo Canada

ISBN: (纸本)9781467327299

In this paper, a novel gene expression programming (GEP) algorithm is introduced for power system static security assessment. The GEP algorithms as evolutionary algorithms for pattern classification have recently received attention for classification problems because they can perform global searches. The proposed methodology introduces the GEP for the first time in static security assessment problems. The proposed algorithm is examined using different IEEE standard test systems. Different contingency case studies have been used to test the proposed methodology. The GEP based algorithm formulates the problem as a multi-class classification problem using the one-against-all binarization method. The algorithm classifies the security of the power system into three classes, normal, alert and emergency. Performance of the algorithm is compared with other neural network based algorithm classifiers to show its superiority in static security assessment.

关键词： Static security gene expression programming probabilistic neural network radial basis function neural network line outage voltage level power system classifier

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Intrusion Detection on Hybrid gene expression programming

Intrusion Detection on Hybrid Gene Expression Programming

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International Conference on Electrical Information and Mechatronics (ICEIM)

作者： Deng, S. Lin, W. M. Zhang, T. State Grid Elect Power Res Inst Res Inst Informat Technol & Commun Nanjing 211106 Peoples R China

ISBN: (纸本)9783037853115

This paper present intrusion detection on hybrid gene expression programming (ID-HGEP), which combined theory of attribution reduction in rough set to improve precision and efficiency on intrusion detection. Through extensive experiments on high dimension data sets, it is shown that ID-HGEP is apparently more advantageous in terms of convergence speed, intrusion detection efficiency and average consumptive time in contrast with traditional intrusion detection algorithm on GEP.

关键词： intrusion detection rough set attribution reduction gene expression programming

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Predictive model for bidirectional shear strength of reinforced concrete columns subjected to biaxial cyclic loading

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ENGINEERING STRUCTURES 2021年 244卷 112781-112781页

作者： Murad, Yasmin Zuhair Univ Jordan Civil Engn Dept Amman 11942 Jordan

Corner columns are subjected to bidirectional lateral loading in three-dimensional framed structures during seismic events, which can deteriorate their shear strength. The majority of the design codes do not consider the effect of biaxial lateral loading in the calculation of the column shear strength, and hence an accurate formulation that can predict the bidirectional shear strength is still lacking. gene expression programming (GEP) was used in this research to predict the bidirectional shear strength of columns subjected to biaxial cyclic loads based on a database collected from different experimental studies found in the literature. Based on the previous experimental studies, it is found that the biaxial shear strength of RC columns are predominantly controlled by the column axial load, cross-sectional area of the columns, column shear reinforcement area, concrete compressive strength, and the height of the column. These parameters were used to develop the GEP models in this research. The models were statistically evaluated in order to ensure their applicability, and then they were compared to the ACI-318 formulation of the uniaxial shear strength. The R-2 value obtained using the ACI-318 formulations is low compared to that obtained using the GEP models, where the R-2 value obtained using the ACI formulation is 0.35, and that obtained using the first and second GEP models are 0.87 and 0.73, respectively. This confirms that the GEP models are more fitting to the experimental results compared to the ACI-318 formulation.

关键词： gene expression programming Biaxial shear strength Bidirectional lateral loading RC columns

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