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Predictive model for stress at ultimate in internally unbonded steel tendons based on genetic expression programming

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RESULTS IN ENGINEERING 2022年 13卷

作者： Oukaili, Nazar Peera, Iqbal Univ Baghdad Coll Engn Dept Civil Engn Baghdad Iraq

Predicting the stress increment (delta f(ps)) and in turn, the total stress (f(ps)) in internally unbonded steel tendons at ultimate flexural strength of post-tensioned concrete members is one of the major challenges due to the lack of bond between the steel tendon and surrounding concrete. This article presents the development of a new predictive model to evaluate the stress increment (delta f(ps)) and in turn, the total stress (f(ps)) based on a genetic expression programming (GEP). GEP has the ability to collect all the influential parameters, among all the cross-sectional and the member's geometrical properties, the mechanical properties of materials, and the applied load, in one single closed-form expression for predicting (delta f(ps)). The proposed model was developed based on a comprehensive experimental database of 196 concrete specimens that were collected from literature and the experimental outcomes of 22 beams and one-way post-tensioned concrete slabs which were previously prepared, fabricated, and tested by authors of the present study. A parametric study was conducted to obtain an idea of the relationship between the independent variables and their effect on the stress increment in unbonded tendons at ultimate (i.e., the target parameter (delta f(ps))). To inspect the potentiality of the proposed (GEP) model against 22 analytical models available in the literature, an extensive comparative study was carried out. The performance of the proposed model was checked according to the values of the coefficient of determination (R-2) and different achieved statistical errors. In comparison to the experimental values of (delta f(ps)) and (f(ps)), the proposed model showed the highest coefficient of determination (R-2) of 0.78 and 0.93, respectively, compared to the other 22 analytical models. While the values of mean absolute error, relative absolute error, root mean square error, and root-relative square error to determine (delta f(ps)) and (f(ps)) were found

关键词： Soft computer approaches Predictive model gene expression programming Stress increment Unbonded tendon Post-tensioning Flexural concrete member

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Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

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RESULTS IN ENGINEERING 2024年 21卷

作者： Rabi, Musab Jweihan, Yazeed S. Abarkan, Ikram Ferreira, Felipe Piana Vendramell Shamass, Rabee Limbachiya, Vireen Tsavdaridis, Konstantinos Daniel Santos, Luis Fernando Pinho Jerash Univ Dept Civil Engn Jerash 26150 Jordan Mutah Univ Coll Engn Civil & Environm Engn Dept POB 7 Al Karak 61710 Jordan Abdelmalek Essaadi Univ Fac Sci Dept Phys Tetouan 93002 Morocco Univ Fed Uberlandia Fac Civil Engn Campus Santa Monica Uberlandia MG Brazil Brunel Univ London Dept Civil & Environm Engn London England London South Bank Univ Sch Build Environm & Architecture Div Civil & Bldg Serv Engn London England City Univ London Sch Sci & Technol Dept Engn Northampton Sq London EC1V 0HB England

The use of periodical elliptically -based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength -to -weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web -post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web -post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web -post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and gene expression programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings.

关键词： Finite element modelling Web -post buckling resistance Elliptically -based web openings High strength steel beams Artificial neural network gene expression programming Support vector machine regression

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Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

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HELIYON 2024年第1期10卷 e23375页

作者： Khan, Majid Khan, Adil Khan, Asad Ullah Shakeel, Muhammad Khan, Khalid Alabduljabbar, Hisham Najeh, Taoufik Gamil, Yaser COMSATS Univ Islamabad Abbottabad Campus Abbottabad 22060 Pakistan Univ Bradford Civil & Struct Engn Dept Bradford BD7 1DP West Yorkshire England Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat Maintenance & Acoust Lulea Sweden Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile

关键词： Fiber-reinforced polymers Flexural capacity Machine learning gene expression programming Multi-expression programming

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Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading

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RESULTS IN ENGINEERING 2024年 24卷

作者： Husnain, Ali Iqbal, Munir Waqas, Hafiz Ahmed El-Meligy, Mohammed Javed, Muhammad Faisal Ullah, Rizwan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Swabi 23640 Khyber Pakhtoon Pakistan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Jadara Univ Jadara Univ Res Ctr POB 733 Irbid Jordan Western Caspian Univ Baku Azerbaijan Comsats Univ Islamabad Abbottabad Campus Dept Civil Engn Islamabad Pakistan

To meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consuming and resource intensive. To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques-including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network-for predicting the peak response of RC beams under impact loads. A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. The GPR model is the most effective with R2 value of 0.95. Moreover, Shapely analysis identified beam depth, impact velocity, and beam breadth as critical factors. Overall, this study demonstrates the efficacy of machine learning in accurately predicting the behavior of RC structures under impact loads, providing valuable tools for civil engineers in design and analysis.

关键词： Machine learning Peak response gene expression programming Shapley

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A novel gene selection algorithm for cancer classification using microarray datasets

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BMC MEDICAL GENOMICS 2019年第1期12卷 10-10页

作者： Alanni, Russul Hou, Jingyu Azzawi, Hasseeb Xiang, Yong Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia

BackgroundMicroarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification *** innovative gene Selection programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on gene expression programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the *** results on ten microarray cancer datasets demonstrate that gene Selection programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection *** subset selected by GSP can achieve a higher classification accuracy with less processing time.

关键词： gene selection gene expression programming Support vector machine Microarray cancer dataset

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Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

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JOURNAL OF EARTH SYSTEM SCIENCE 2017年第2期126卷 18-18页

作者： Moeeni, Hamid Bonakdari, Hossein Ebtehaj, Isa Razi Univ Dept Civil Engn Kermanshah Iran

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA-GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years' worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA-GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA-ANN models. The results indicate that the SARIMA-GEP model (R-2=78.8, VAF=78.8, RMSE=0.89, MAPE=43.4, CRM=0.053) outperforms SARIMA and GEP and SARIMA-ANN (R-2=68.3, VAF=66.4, RMSE=1.12, MAPE=56.6, CRM=0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA-GEP over the SARIMA-ANN model.

关键词： Forecasting inflow time series hybrid gene expression programming

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Research on Music Emotion Cognition Model Based on Linguistic Value Computing

Research on Music Emotion Cognition Model Based on Linguisti...

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7th World Congress on Intelligent Control and Automation

作者： Zhu, Bin Liu, Tao Tang, Yongchuan Zhejiang Shuren Univ Coll Informat Sci Technol Hangzhou 310015 Zhejiang Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9781424421138

Research on music's affective computing model plays an important role in digital entertainment and harmonious human-machine interactive, and belongs to the field of artificial emotion. For modeling the music affective. There are two major insufficients in current researches such as: lack of knowledge expression And generalization. This paper presents a general model building peocess, and designs a cognizing test based on semantic difference for Chinese folk music. According to the studying about the data of experiment and relied on the GEP based on dynamic mutation operator, a model tally with the linguistic modeling theory is constructed, which is consistent with psychological consistent pattern of music emotion.

关键词： music emotional vector linguistic value computing semantic difference method gene expression programming

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Shape Optimization of Marine Propeller Based On P-System Diffusion Algorithm and Approximate Model Technology 2021

Shape Optimization of Marine Propeller Based On P-System Dif...

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2021 International Conference on Mechanical, Aerospace and Automotive Engineering

作者： JunJun Wu Long Zheng ShunHuai Chen Hao Wang Wuhan Rules & Research Institute CCS China Wuhan University of Technology China

ISBN: (纸本)9781450385206

Based on a novel optimization algorithm called Diffusion Algorithm Based on P Systems (DAPS) and a new type of approximate called gene expression programming (GEP), we present an optimization method of marine propeller. In this work, the hydrodynamic performance of the marine propeller under different geometrical and physical conditions are evaluated by GEP, the final model is found to be of reasonable accuracy between observed and GEP-predicted values. On the basis of obtaining an approximate model with good response, we use the DAPS algorithm to optimize the shape of the propeller, the profile shape of the propeller is considered as an optimization variable. Diffusion Algorithm Based on P Systems (DAPS) is a novel optimization algorithm, this algorithm utilizes four different search strategies and a P system to explore the search space and find the global minima, through the benchmark functions test, DAPS is highly competitive and can provide better solution. Through calculation verification, it can be seen that the final GEP model has a good prediction of the propeller hydrodynamic performance, and the proposed optimization method can improve the open water efficiency of the propeller.

关键词： Optimization Diffusion Algorithm Based on P Systems Propeller gene expression programming

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Review of genetic programming in Modeling of Machining Processes

Review of Genetic Programming in Modeling of Machining Proce...

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2012 International Conference on Modelling,Identification and Control(建模、辨别与控制国际会议 ICMIC 2012)

作者： A. Garg K. Tai School of Mechanical and Aerospace Engineering NanyangTechnological University50 Nanyang AveSingap School of Mechanical and Aerospace Engineering Nanyang Technological University50 Nanyang AveSinga

The mathematical modeling of machining processes has received immense attention and attracted a number of researchers because of its significant contribution to the overall cost and quality of *** literature study demonstrates that conventional approaches such as statistical regression, response surface methodology, *** physical understanding of the process for the erection of precise and accurate *** statistical assumptions of such models induce ambiguity in the prediction ability of the *** limitations do not prevail in the nonconventional modeling approaches such as genetic programming (GP), Artificial Neural Network (ANN), Fuzzy Logic (FL), genetic Algorithm (GA), *** therefore ensures trustworthiness in the prediction ability of the *** present work discusses about the notion, application, abilities and limitations of genetic programming for modeling of machining *** characteristics of GP uncovered from the current review are compared with features of other modeling approaches applied to machining processes.

关键词： Machining genetic programming gene expression programming Regression Artificial Neural Network

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A New Type of GEP Algorithm and Its Application in Function Modeling

A New Type of GEP Algorithm and Its Application in Function ...

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2014 International Conference on Advanced Computer Science and Engineering(ACSE 2014)

作者： Meng-li RUAN Information Technology School of Shandong Management University

gene expression programming is new member of the genetic computing family which is developed from the gene expression rules of biogenetics. This paper aim to solve the function modeling through applying the improved GEP algorithm and provide valuable forecasting function and instructional data for the practical work. The paper will introduce the application background of GEP, basic theories of GEP, at the same time, it proposes the corrective method of both evolutionary rate and numerical constants in order to realize the function modeling. Furthermore, experiment proves that GEP is more effective than the traditional methods.

关键词： gene expression programming Function modeling Data mining

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