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Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using gene expression programming

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MATERIALS 2022年第19期15卷 6959页

作者： Amin, Muhammad Nasir Raheel, Muhammad Iqbal, Mudassir Khan, Kaffayatullah Qadir, Muhammad Ghulam Jalal, Fazal E. Alabdullah, Anas Abdulalim Ajwad, Ali Al-Faiad, Majdi Adel Abu-Arab, Abdullah Mohammad King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hufuf 31982 Saudi Arabia Univ Engn & Technol Dept Civil Engn Mardan 23200 Pakistan Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan COMSATS Univ Islamabad Dept Environm Sci Abbottabad Campus Abbottabad 22060 Pakistan Shanghai Jiao Tong Univ Dept Civil Engn Shanghai 200240 Peoples R China Univ Management & Technol Civil Engn Dept Lahore 54770 Pakistan King Faisal Univ Coll Engn Dept Chem Engn Al Hasa 31982 Saudi Arabia

The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (N-c), genes (N-g) and, the head size (H-s) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R-2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the N-c = 100, H-s = 8 and N-g = 3. This model exhibits an R-2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R-2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.

关键词： rapid chloride penetration resistance metakaolin gene expression programming compressive strength of concrete sensitivity parametric analysis

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Optimal Design of the Cement, Fly Ash, and Slag Mixture in Ternary Blended Concrete Based on gene expression programming and the genetic Algorithm

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MATERIALS 2019年第15期12卷 2448-2448页

作者： Wang, Xiao-Yong Kangwon Natl Univ Dept Architectural Engn Chunchon 24341 South Korea

Concrete producers and construction companies are interested in improving the sustainability of concrete, including reducing its CO2 emissions and the costs of materials while maintaining its mechanical properties, workability, and durability. In this study, we propose a simple approach to the optimal design of the fly ash and slag mixture in blended concrete that considers the carbon pricing, material cost, strength, workability, and carbonation durability. Firstly, the carbon pricing and the material cost are calculated based on the concrete mixture and unit prices. The total cost equals the sum of the material cost and the carbon pricing, and is set as the optimization's objective function. Secondly, 25 various mixtures are used as a database of optimization. The database covered a wide range of strengths between 25 MPa and 55 MPa and a wide range of workability between 5 and 25 cm in slump. gene expression programming is used to predict the concrete's strength and slump. The ternary blended concrete's carbonation depth is calculated using the efficiency factors of fly ash and slag. Thirdly, the genetic algorithm is used to find the optimal mixture under various constraints. We provide examples to illustrate the design of ternary blended concrete with different strength levels and environmental CO2 concentrations. The results show that, for a suburban region, carbonation durability is the controlling factor in terms of the design of the mixture when the design strength is less than 40.49 MPa, and the compressive strength is the controlling factor in the design of the mixture when the design strength is greater than 40.49 MPa. For an urban region, the critical strength for distinguishing carbonation durability control and strength control is 45.93 MPa. The total cost, material cost, and carbon pricing increase as the concrete's strength increases.

关键词： cost CO2 emissions fly ash slag gene expression programming genetic algorithm

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Forecasting the seepage loss for lined and un-lined canals using artificial neural network and gene expression programming

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GEOMATICS NATURAL HAZARDS & RISK 2023年第1期14卷

作者： Gad, Manal Abdelhaleem, Hanaa Mohamed Waleed, O. A. S. Delta Higher Inst Engn & Technol Civil Engn Dept Mansoura Egypt Minist Water Resources & Irrigat MWRI Nile Res Inst NRI Natl Water Res Ctr NWRC El Qanater Egypt

Canal lining is customarily used to raise water-use effectiveness and reduce seepage loss. The major water losses in an irrigation channel are due to leakage and evaporation. The Egyptian general Integrated Management for Water Resources and Irrigation introduced a proposal for lining the Al-Hagar canal based on these losses. This study investigates the effect of lining in the Al-Hagar canal on flow characteristics, and compares the canal before and after introducing the lining. Additionally, it discusses the most common type of water loss, namely, losses due to seepage. Fieldwork was conducted on the Al-Hagar canal, Al-Saff Center, South of Helwan city, Egypt. The result revealed that the discharge of the canal after the lining is approximately 1.362-1.573 times greater than that of the un-lined section. Water losses in the Al-Hagar canal were 38.736% when un-lined but decreased to 29.253% when lined. The conveyance effectiveness in the un-lined canal, which is approximately 61.26%, increased to 70.75% when the entire canal is lined, which means a 9.483% improvement of conveyance. New relations were introduced using Artificial Neural Network and gene expression programming to forecast the seepage loss in the lined and the un-lined canal as a function of Manning's coefficient, Froude number and hydraulic radius. The consequences were better using the GEP program than using ANN for the lined and the un-lined canals. The value of the determination coefficient was 0.98, Correlation factor was 0.99, and the RMSE was 0.0017 for lined canals and the value of determination coefficient was 1, Correlation factor was 1, and the RMSE was 0.0003 for un-lined canals.

关键词： Conveyance efficiency gene expression programming lined canal seepage un-lined canal losses

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Multivariate formulation to predict the frictional strength of fiber reinforced soils using gene expression programming

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 134卷

作者： Nawaz, Muhammad Naqeeb Akhtar, Ahmed Yar Awan, Tariq Ahmed Nawaz, Muhammad Muneeb Qamar, Sana Ullah Shehzad, Taimoor Hassan, Waqas Khan, Shah Rukh Farooq, Rashid Natl Univ Sci & Technol NUST NUST Inst Civil Engn Islamabad 44000 Pakistan Swinburne Univ Technol Melbourne Australia Natl Univ Technol NUTECH Civil Engn Dept Islamabad Pakistan Natl Univ Sci & Technol NUST Dept Civil Engn Balochistan Campus Quetta 87300 Pakistan Int Islamic Univ Fac Engn & Technol Dept Civil Engn Islamabad 44000 Pakistan

Modeling and prediction approaches have attracted much interest in recent years to evaluate the efficiency of fiber reinforcement in improving the strength characteristics of loose soils. Several analytical and artificial intelligence (AI) based models have been presented to estimate friction angle of fiber reinforced soils (FRS). However, due to limited input characteristics, these analytical models lack accuracy, whereas AI-based models are developed on advanced programming methods and need substantial programming expertise, resulting in a lack of agreement in mechanism and application. Herein, we provided a multivariate formulation that uses gene expression programming (GEP) to predict the frictional strength of FRS using fifteen influencing factors, which, to the best of our knowledge, has never been reported before. In this regard, two mathematical models are presented, one of which incorporates both qualitative and quantitative input factors, while the other just considers quantitative variables. Laboratory experimental data on FRS from various sources was assessed and properly analysed within the proposed framework while taking into account various affecting factors. The results illustrate the efficiency of GEP in estimating FRS frictional strength and emphasis the need of considering several parameters for prediction. Sensitivity and parametric studies were also performed to identify the relevance of each influencing factor and to determine whether the prediction models meet physical processes or are purely empirical relations. Finally, a user-friendly computer program was developed based on the GEP derived mathematical formulations to anticipate the FRS friction angle, which offers practitioners great field applicability.

关键词： Fiber reinforced soils gene expression programming Multivariate formulation Frictional strength Mathematical models

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An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using gene expression programming

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2012年第2期7卷 1-10页

作者： Moghassem, Abdolrasool Fallahpour, Alireza Shanbeh, Mohsen Islamic Azad Univ Firoozkooh Branch Dept Management Firoozkooh Iran Isfahan Univ Technol Dept Text Engn Esfahan Iran Islamic Azad Univ Qaemshahr Branch Dept Text Engn Qaemshahr Iran

Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, gene expression programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R-2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R-2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm.

关键词： Rotor spun yarn gene expression programming Artificial neural network Draw frame Production speed Break draft Breaking strength

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Synergistic effect of grinding time and submicron (nano) bubbles on the zeta potential state of spent lithium-ion batteries: A gene expression programming approach

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JOURNAL OF ENERGY STORAGE 2023年 70卷

作者： Nazari, Sabereh Hoseinian, Fatemeh Sadat Li, Jiahao Safari, Mehdi Khoshdast, Hamid Li, Jinlong He, Yaqun China Univ Min & Technol Sch Chem Engn & Technol Xuzhou 221116 Jiangsu Peoples R China Amirkabir Univ Technol Dept Min Engn Tehran 158754413 Iran Mintek Minerals Proc Div Private Bag X3015 ZA-2125 Randburg South Africa Shahid Bahonar Univ Kerman Dept Min Engn Higher Educ Complex Zarand Zarand *** Iran Shahid Bahonar Univ Kerman Mineral Ind Res Ctr Kerman 76169133 Iran

The present work aims at the investigation and advanced simulation of the synergistic effect of grinding con-ditions and sub-micron (nano) bubbles (NBs) on the zeta potential mechanism of spent lithium-ion batteries (LIBs). For this purpose, the variation of the zeta potential of electrode active materials under different conditions was measured based on an effective Historical Data (HD) experimental design. The impact of operating variables including grinding time (0-25 min), pH (4.5-11.5), collector type (collectorless, n-dodecane, kerosene, and diesel oil), and NBs (absence and presence) were assessed through one-way analysis of variance (ANOVA). The process was then simulated using a genetic algorithm (GA) as an optimization algorithm of the artificial neural network (ANN). The statistical results indicated that the process was significantly influenced by pH, collector, and NBs (pvalue < 0.05) through a nonlinear trend. Although the individual effect of grinding time was not significant, a complex nonlinear interaction between grinding time and pH was observed. The effect of collectors followed the order of diesel oil > n-dodecane > kerosene;however, the effect of collectors was inverted at a pH of 10. Finally, the intelligent simulation results revealed that the process could be modeled using a genetic al-gorithm with a determination coefficient and error of 94.44 % and 3.28 %, respectively.

关键词： Spent lithium -ion batteries Zeta potential Nanobubbles Grinding time gene expression programming Simulation

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Online state of health estimation for lithium-ion batteries based on gene expression programming

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ENERGY 2024年 294卷

作者： Zhang, Zhengjie Cao, Rui Zheng, Yifan Zhang, Lisheng Guang, Haoran Liu, Xinhua Gao, Xinlei Yang, Shichun Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Imperial Coll London Dept Mech Engn London England

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (gene expression programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness.

关键词： Lithium -ion batteries gene expression programming SOH estimation stacking strategy

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Developing reference-based correlations for temperature distribution in VVER reactor using gene expression programming and single-heated channel approach

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NUCLEAR ENGINEERING AND DESIGN 2024年 424卷

作者： Sadeghi, Khashayar Ghazaie, Seyed Hadi Kalanke, Natasha Sokolova, Ekaterina Aghaie, Mahdi Naserbegi, Azadeh Modestov, Victor Peter Great St Petersburg Polytech Univ Dept Nucl & Heat Power Engn St Petersburg 195251 Russia Botswana Int Univ Sci & Technol Dept Phys & Astron Palapye Botswana Shahid Beheshti Univ Engn Dept POB *** Tehran Iran

Temperature distribution in different channels of a light-water nuclear reactor is essential for Multiphysics coupling approaches in core calculation. Machine learning algorithms trained by well-structured datasets can be applied for the prediction of temperature profiles in different components of the core including coolant, clad and fuel. In this study, gene expression programming (GEP) as an evolutionary machine learning technique is used for developing proper nondimensionalized correlations to estimate the temperature profiles in the VVER-1000 reactor. The single-heated channel approach based on solving mass, energy, and momentum equations is used as a mathematical model for generating datasets to train the GEP. To determine the most influential parameters on the temperature profiles a two-step sensitivity analysis including Pearson correlation coefficient and one-at-atime sensitivity measure is performed. According to Wilks' statistical technique, 59 samples are generated to uniformly distribute the uncertainties in the input parameters. By considering 80 % of the acquired data as a training dataset, three reference-based correlations for coolant, clad and center of fuel are developed. Mean Square Error (MSE) and coefficient of determination are used to measure the error of each approach. The high predictability (i.e., R-2 > 0.95) presented in the temperature profiles indicates that the GEP could correlate input parameters successfully. The calculations for relative error show that at some axial directions, the maximum relative error can get to 5 %, while the average relative error is 0.21, 0.23, and 1.71 % respectively for coolant, clad, and fuel temperature profiles.

关键词： Machine learning Thermal-hydraulics of nuclear reactor Single-heated channel approach gene expression programming temperature distribution in VVER

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Taiwan Stock Investment with gene expression programming

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Procedia Computer Science 2014年 35卷 137-146页

作者： Cheng-Han Lee Chang-Biau Yang Hung-Hsin Chen Department of Computer Science and Engineering National Sun Yat-sen University 70 Lienhai Rd. Kaohsiung 80424 Taiwan

In this paper, we first find out some good trading strategies from the historical series and apply them in the future. The profitable strategies are trained out by the gene expression programming (GEP), which involves some well-known stock technical indicators as features. Our data set collects the 100 stocks with the top capital from the listed companies in the Taiwan stock market. Accordingly, we build a new series called portfolio index as the investment target. For each trading day, we search for some similar template intervals from the historical data and pick out the pertained trading strategies from the strategy pool. These strategies are validated by the return during a few days before the trading day to check whether each of them is suitable or not. Then these suitable strategies decide the buying or selling consensus signal with the majority vote on the trading day. The training period is from 1996/1/6 to 2012/12/28, and the testing period is from 2000/1/4 to 2012/12/28. Two simulation experiments are performed. In experiment 1, the best average accumulated return is 548.97% (average annualized return is 15.47%). In experiment 2, we increase the diversity of trading strategies with more training. The best average accumulated return is increased to 685.31% (average annualized return is 17.18%). These two results are much better than that of the buy-and-hold strategy, whose return is 287.00%.

关键词： gene expression programming stock investment majority vote technical indicator strategy pool.

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Robust gene expression programming

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Procedia Computer Science 2011年 6卷 165-170页

作者： Noah Ryan David Hibler Christopher Newport University PCSE Department 1 University Place Newport News Virginia 23606 USA

genetic/evolutionary methods are frequently used to deal with complex adaptive systems. The classic example is a genetic Algorithm. A genetic Algorithm uses a simple linear representation for possible solutions to a problem. This is usually a bit vector. Unfortunately, the natural representation for many problems is a tree structure. In order to deal with these types of problems many evolutionary methods make use of tree structures directly. gene expression programming is a new, popular evolutionary technique that deals with these types of problems by using a linear representation for trees. In this paper we present and evaluate Robust gene expression programming (RGEP). This technique is a simplification of gene expression programming that is equally efficient and powerful. The underlying representation of a solution to a problem in RGEP is a bit vector as in genetic Algorithms. It has fewer and simpler operators than those of gene expression programming. We describe the basic technique, discuss its advantages over related methods, and evaluate its effectiveness on example problems.

关键词： Evolutionary Computation gene expression programming

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