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Prediction of Compressive Strength of Alccofine-Based Geopolymer Concrete

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第4期48卷 2077-2093页

作者： Diksha Dev, Nirendra Goyal, Pradeep Kumar Delhi Technol Univ Dept Civil Engn New Delhi 110042 India

Geopolymer concrete has been proposed as a superior substitute for ordinary Portland cement concrete, owing to its superior performance under harsh conditions. It significantly decreases the carbon footprint to a considerable extent. The utilization of machine learning techniques in the construction industry is poised to become increasingly prevalent due to its ability to forecast the mechanical properties of concrete mix designs based on their components, thereby obviating the need for destructive testing. The objective of this research is to construct predictive models for the compressive strength of geopolymer concrete mixed with alccofine and subsequently verify their accuracy through comparison with observed values. The results of the experimental investigation indicate that the compressive strength is greater when subjected to oven-dried curing in comparison to ambient curing conditions. The addition of alccofine results in an increase in compressive strength up to a certain percentage, beyond which a decrease is observed. Machine learning techniques such as support vector regression (SVR), gene expression programming (GEP), and artificial neural network (ANN) have been deployed to construct models based on empirical observations. The output variable considered in this study is compressive strength, while the input variables comprise a range of influencing parameters that are utilized for both training and testing purposes. The efficacy of anticipated values is evaluated through diverse statistical parameters. The ANN model outperforms the SVR and GEP models in terms of predictive accuracy, as evidenced by its superior statistical performance when comparing actual and predicted values.

关键词： Geopolymer concrete Compressive strength Alccofine Support vector regression gene expression programming Artificial neural networks

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Numerical and artificial intelligence models for predicting the water advance in border irrigation

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2022年第1期24卷 558-575页

作者： Emamgholizadeh, Samad Seyedzadeh, Amin Sanikhani, Hadi Maroufpoor, Eisa Karami, Gholamhosein Shahrood Univ Technol Dept Environm & Civil Engn Shahrood Iran Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Univ Kurdistan Dept Water Engn Sanandaj Iran Shahrood Univ Technol Dept Geol Engn Shahrood Iran

The water advance time (Ta) is needed for designing and evaluating surface irrigation systems. This study employed artificial neural networks (ANNs) and gene expression programming (GEP) techniques for estimating the water advance time in the border irrigation system as a function of inflow rate per unit width (Q(b)), length of water advance in the border (L), longitudinal slope (S-o), final infiltration rate of the soil (f(o)) and Manning roughness coefficient (n). The techniques were tested on field measurements from agricultural farms in three different provinces of Iran. Results showed that the ANN model was superior to the GEP model for the estimation of water advance time. The performance indicators for the ANN model were R-2 = 0.966, RMSE = 7.805 min and MAE = 5.090 min, MBE = 0.312 and SI = 0.181. Results of the intelligence-based models were also compared with the WinSRFR model. Both ANN and GEP models predicted the water advance time more accurately than did the WinSRFR model.

关键词： gene expression programming Neural Networks Surface irrigation Water Advance Time WinSRFR

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Control of warp tension during weaving procedure using evaluation programming

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JOURNAL OF THE TEXTILE INSTITUTE 2006年第4期97卷 313-324页

作者： Dayik, M. Kayacan, M. C. Calis, H. Cakmak, E. Suleyman Demirel Univ Dept Text Engn Isparta Turkey Suleyman Demirel Univ Dept Elect & Comp Educ Isparta Turkey

In this study, gene expression programming (GEP), one of the Evolution programming methods, is used for the control of the let-off system in a weaving loom. For this control, the function of warp tension occurring in a complete rotation of the main shaft of weaving loom is determined by the method of GEP. The control of let-off system is implemented using this function. Particularly, during the shed opening and beat-up processes to make warp tension constant, warp beam is rotated clockwise and counterclockwise. The values of warp tension obtained by GEP are compared with the values of conventional controlled methods. As a conclusion, the obtained warp tension values arc 11.2% less than values of classical approach. At the same time it is also provided that break rate of warp tension is decreased by 20%. It has shown that GEP is an effective tool for the decreasing of warp break rate.

关键词： weaving warp tension gene expression programming let-off control warp break

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

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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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Variable penalty factors: a new GEP automatic clustering algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2017年第1-2期15卷 3-11页

作者： Chen, Yan Li, Kangshun Huang, Haohua South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China China Banking Regulatory Commiss Guangdong Off Guangzhou 510117 Guangdong Peoples R China

The clustering algorithm is considered as an important and basic method in the field of data mining on interdisciplinary researches. Various problems such as sensitive selection of initial clustering centre, easy to fall into local optimal solution, poor universal search capacity and requiring prior knowledge for determining numbers of clusters still exist in the traditional clustering algorithm. A gene expression programming (GEP) automatic clustering algorithm with variable penalty factors is adopted in this paper, featuring combination of penalty factors and GEP clustering algorithm, no requirements for prior knowledge on the data set, automatic division of clusters and better solution for the impact of isolated points and noise points. The simulation experiment makes further proof of the effectiveness of the algorithm in this paper.

关键词： variable penalty factor gene expression programming GEP clustering algorithm

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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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A Unified Fitness Calculation Method for Automatic Modeling Algorithms

A Unified Fitness Calculation Method for Automatic Modeling ...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Jiang, Dazhi Wang, Zhifei Sun, Haojun Du, Yulin Univ Shantou Dept Comp Sci Shantou Guangdong Peoples R China

ISBN: (纸本)9781424467129

As a branch of Evolutionary Computation (EC), gene expression programming (GEP) and Multi expression programming (MEP) have been applied widely in Automatic Modeling. Based on the gene Reading and Computing Machine, a unified and improved method named Improved Reading and Computing Machine (IGRCM) is presented to calculate the fitness of chromosome for two linear automatic modeling algorithms, which are GEP and MEP. The experimental results indicate that the unified IGRCM could save the CPU running time effectively.

关键词： Automatic Modeling Evolutionary Computation gene expression programming Multi expression programming

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A Comparison of Encoding Methods for Automated Design of Optical Architectures 21

A Comparison of Encoding Methods for Automated Design of Opt...

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21st Annual Conference on Novel Optical Systems Design and Optimization

作者： Olson, C. C. Evans, L. Bayya, N. R. Edelberg, J. US Naval Res Lab Opt Sci Div 455 Overlook Ave SW Washington DC 20375 USA Naval Res Lab Naval Res Enterprise Internship Program Washington DC 20375 USA Naval Res Lab Sci & Engn Apprentice Program Washington DC 20375 USA

ISBN: (数字)9781510620643

ISBN: (纸本)9781510620643

Commercial ray-tracing programs can optimize system parameters for a given optical architecture (e.g., radii, thickness, and spacing of lenses);however, design of the underlying system architecture (e.g., number of lenses, their type, and their order in the optical train) remains an expensive trial-and-error approach driven by prior experience and human intuition. Work on automating the architecture design process has had some success but the problem remains open. We compare the efficacy of novel methods for encoding the design of optical systems on a simple imaging objective.

关键词： Automated Design Optical System Design Evolutionary Algorithms gene expression programming genetic Algorithms Optics

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A New Model Based on GEP-SWPM for Predicting Heavy Metals Speciation 15

A New Model Based on GEP-SWPM for Predicting Heavy Metals Sp...

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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on Parallel and Distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)

作者： Gao, Hongyun Liu, Fujun Sun, Shengjuan Shi, Gaotao Hebei Univ Engn Sch Informat & Elect Engn Handan Peoples R China Hebei Univ Engn Lib Handan Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781509032051

The prediction of heavy metals speciation in municipal solid waste incineration (MSWI) bottom ash belongs to a kind of time series prediction. And in this field, gene expression programming (GEP) has become a research hotspot in recent years. However, due to the influences of various environmental factors, it is difficult to build a good model for predicting heavy metals speciation directly by GEP. In order to enhance prediction precision, this paper uses sliding window prediction method of GEP (GEP-SWPM) to build a heavy metals speciation prediction model and makes some improvements. Experimental results show that the prediction precision of the model based on GEP-SWPM has a significant improvement, compared with that based on GEP. Therefore, GEP-SWPM provides a more accurate and reasonable method that could be applied to nonlinear time series prediction problems well.

关键词： time series prediction gene expression programming GEP-SWPM heavy metals speciation prediction model

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RETRACTED: Computational Investigations of the Impact Resistance of Aluminum-Epoxy-Laminated Composites (Retracted article. See vol. 53, pg. 4427, 2019)

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2012年第5期21卷 623-646页

作者： Nazari, Ali Khalaj, Gholamreza Didehvar, Neda Islamic Azad Univ Dept Mat Sci & Engn Saveh Branch Saveh Iran

In this article, Charpy impact resistance of aluminum-epoxy-laminated composites in both crack divider and crack arrester configurations has been investigated. In both configurations, an analytical investigation has been carried out to evaluate the effects of layer thickness on impact resistance of the specimens. A model based on gene expression programming for predicting impact resistance of the specimens has been presented. To build the model, training and testing were conducted using experimental results from 126 specimens produced of two basic composites. The used data as inputs of gene expression programming models are arranged in a format of seven parameters that cover the thickness of layers, the number of layers, the adhesive type, the crack tip configuration, the content of SiC particles, the content of methacrylated butadiene-styrene particles, and the number of test trials. According to these input parameters, in the gene expression programming model, the impact resistance of each specimen was predicted. The training and testing results in the gene expression programming model have shown a strong potential for predicting impact resistance of aluminum-epoxy-laminated composites.

关键词： aluminum-epoxy-laminated composite analytical investigation Charpy impact resistance crack arrester crack divider gene expression programming

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