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10th International Conference on Big Data and Information Analytics

作者： Zhou, Xuanfei Luo, Weicheng Yuan, Yutong Tang, Boya Qiao, Linbo Wang, Chen Chen, Xingxuan Changsha Normal Univ Presch Coll Changsha Peoples R China Shantou Univ Coll Engn Shantou Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Ludong Univ Sch Arts Yantai Peoples R China Natl Univ Def Technol Sch Comp Changsha Peoples R China

ISBN: (纸本)9798350354638;9798350354621

This study proposes a music gene expression programming method to achieve music melody feature learning of a specific singer's compositional style. The user specifies the song format, and the program automatically generates and evaluates songs. A mapping mechanism between 16-bit octal numbers and musical scores is proposed to realize the evolution of musical scores in units of measures. In the melody generation stage, the modification operations that are generally performed by the composer in the process of composing music are integrated with the evolutionary operations of gene expression programming to create a music gene expression programming operation. In the music melody evaluation stage, a long short-term memory neural network evaluation model is established and trained using the main verse and chorus of the music as well as the spectrum of the entire song. The neural network evaluates the resulting song fitness value, providing feedback for music evolution. In contrast to melody generated via other methods, comparative experiments show that the melody generated by this research method has an obvious compositional style of a specific composer.

关键词： automatic melody generation music-style learning gene expression programming long short-term memory evolutionary algorithm deep learning

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Prediction of moment redistribution capacity in reinforced concrete beams using gene expression programming

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STRUCTURES 2023年 47卷 2209-2219页

作者： Aydogan, Mehmet Safa Alacali, Sema Arslan, Guray Yildiz Tech Univ Dept Civil Engn Istanbul Turkey

Moment redistribution can play an important role in making the design of reinforced concrete (RC) structures more realistic and economical. In this paper, a new comprehensive formula has been proposed that considers four input parameters that are thought to influence moment redistribution the most in statically indeterminate RC beams using gene expression programming (GEP). For this reason, an experimental database of 108 data points was collected from experimental studies in the literature to predict the moment redistribution of the RC beams using genetic programming. All of these collected data points belong to two-span RC continuous beams. The results of the GEP formulation were statistically compared with the experimental results obtained from the literature and the results from the equations provided by the current design code provisions. The results of the comparison revealed that the proposed GEP-based formulation has the best performance and accuracy among the proposed models. Moreover, the sensitivity analysis and parametric study were also carried out to evaluate the most critical parameters affecting on moment redistribution of RC continuous beams.

关键词： Moment redistribution RC continuous beams Prediction Design codes gene expression programming

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Development of prediction models for strength properties of concrete using gene expression programming

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第3期10卷 1-29页

作者： Khan, Asad Ullah Javed, Muhammad Faisal Khan, Majid Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan GIK Inst Engn Sci & Technol Topi 23640 Khyber Pakhtunk Pakistan Western Caspian Univ Coll Engn Al Kharj Dept Civil Engn Baku Azerbaijan Southern Illinois Univ Edwardsville Dept Civil Engn Edwardsville IL 62026 USA

Accurately forecasting the strength properties of concrete is critical, especially for design code compliance. While statistical and empirical models are commonly used;however, these methods demand intensive experimentation and may yield inaccuracies in intricate relationships between concrete characteristics, mix components, and curing conditions. To address these limitations, the current study utilized the gene expression programming (GEP) method to forecast the mechanical characteristics of concrete, such as modulus of elasticity (E), modulus of rupture (MOR), and dynamic modulus (DM). For this purpose, 440 values for E and MOR and 279 values for DM were compiled to develop robust and reliable predictive models. Multiple statistical metrics were utilized to validate the predictions of the model. The GEP model exhibited excellent prediction accuracy with a correlation coefficient (R) of 0.95, 0.96, and 0.94 for MOR, E, and DM, respectively. The comparison between GEP and linear regression models revealed revealed that machine learning-based models (GEP) exhibit superior accuracy than traditional regression models. Moreover, SHapley Additive exPlanations (SHAP) revealed that age significantly influenced all three considered strength characteristics of concrete. In addition, a graphical user interface (GUI) has been created to simplify the utilization of ML-based models for predicting the strength characteristics of concrete. The outcome of this study has the potential to be utilized for smartly predicting concrete's strength properties, eliminating the need for expensive and time-consuming testing procedures.

关键词： Concrete Strength properties Machine learning gene expression programming SHAP Graphical user interface

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Correlating Binder Types and Chloride Diffusion Coefficients of Concrete Mixes Using gene expression programming

Correlating Binder Types and Chloride Diffusion Coefficients...

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Annual Conference of the Canadian-Society-for-Civil-Engineering (CSCE)

作者： Shovon, S. M. Abid Anam Islam, Md Mohaiminul Manzur, Tanvir Bangladesh Univ Engn & Technol Dept Civil Engn Dhaka Bangladesh

ISBN: (纸本)9783031615139;9783031615115;9783031615108

There is a high risk of chloride-induced corrosion of reinforced concrete (RC) in chloride-prone environment. Such corrosion of embedded rebars within concrete can drastically reduce the service life of the affected structure. Hence, evaluation of corrosion vulnerability of a RC element under vulnerable environment is necessary to ensure desired durability. The intrusion of chlorine ions in concrete occurs through the interconnected pores within concrete matrix. A high chloride diffusion coefficient of a concrete mix usually indicates a high intrusion susceptibility of chloride in the resultant RC element. One of the effective ways to reduce diffusion coefficient of concrete is through pore refinement that can be achieved by secondary hydration. The pore refinement breaks the connectivity of the pore network and eventually, hinders the intrusion of chloride ions. The secondary hydration is ensured by using supplementary cementitious materials (SCM) with active silica content. The Rapid Migration Test (RMT) is used to measure chloride diffusion coefficient of a concrete mix that requires specific test setup and preparation of test samples. The RMT is also a destructive test and time-consuming. As a result, conducting RMT experiments and finding chloride diffusion coefficients of a concrete mix is not always feasible. In this study, an attempt has been made to develop a correlation between relevant mix design parameters and chloride diffusion coefficients of the resultant concrete using gene expression programming (GEP). GEP is an adaptive evolutionary artificial intelligence algorithm that uses evolutionary approaches on provided data and fits the data to generate an equation. Iterating the GEP program over a small set of data many times gives a good approximation. A total of 60 experimental RMT data has been utilized with mixes having wide ranges of SCM contents (fly ash and slag up to 40% level of replacement) and water to binder ratios. The developed correlation a

关键词： Reinforced concrete Corrosion Chloride diffusion coefficient gene expression programming Service life

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Comparative Evaluation of Monthly Rainfall Estimates Using gene expression programming and Multiple Linear Regression 23rd

Comparative Evaluation of Monthly Rainfall Estimates Using G...

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2022 International Association for Hydraulic Environmental Engineering and Research Asia Pacific Division

作者： Pathan, Azazkhan, I Said, Saif Sardar Vallabhbhai Natl Inst Technol Dept Civil Engn Surat Gujarat India Aligarh Muslim Univ AMU Dept Civil Engn Aligarh Uttar Pradesh India

ISBN: (纸本)9789819760084;9789819760091

Predicting rainfall, which is a major component of the hydrological cycle, is critical in water resources engineering, planning, and management, as well as in irrigation scheduling. Being an agricultural area Aligarh district in Uttar Pradesh, India, has a greater interest in developing an efficient model to predict the monthly rainfall. Therefore, this paper presents a comparative analysis of two different techniques, viz., gene expression programming (GEP) and multiple linear regression (MLR), to predict the average monthly rainfall in Aligarh district. The non-spatial average monthly weather data, including rainfall, temperature, relative humidity, wind speed, and cloud cover from 2009 to 2019, were selected for calibration and validation of the models. Results revealed that the optimized GEP model produced average monthly rainfall estimates with considerably high accuracies, yielding R-2 similar to 0.91 and 0.86, RMSE of 18.63 and 19.27, and MAE of 10.6 and 11.57 for calibration and validation data, respectively. The MLR model produced rainfall estimates with moderate R-2 similar to 0.77 and 0.73, RMSE of 22.25 and 32.27, and MAE of 15.65 and 21.76 for calibration and validation data, respectively. Analysis of the results revealed that the GEP model performs substantially well with high forecast accuracies, thereby indicating significant rationality in the optimized GEP model. Further, sensitivity analysis performed on the weather parameters showed that cloud cover exhibited the highest significance of 96.7% on rainfall estimation. The developed GEP-based model can be used by town planners and agricultural professionals.

关键词： gene expression programming Multiple linear regression Rainfall forecasting Mean absolute error Root-mean-squared error

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Shear capacity assessment of perforated steel plate shear wall based on the combination of verified finite element analysis, machine learning, and gene expression programming

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Asian Journal of Civil Engineering 2024年第7期25卷 5317-5333页

作者： Bypour, Maryam Mahmoudian, Alireza Tajik, Nima Taleshi, Mostafa Mohammadzadeh Mirghaderi, Seyed Rasoul Yekrangnia, Mohammad Department of Civil Engineering Semnan University Department of Civil Engineering Shahid Rajaee Teacher Training University Department of Civil Structural and Environmental Engineering State University of New York at Buffalo School of Civil Engineering College of Engineering University of Tehran Civil and Environmental Engineering Department University of Nevada

In this study, two formulations have been suggested for the calculation of the shear capacity of stiffened steel plate shear wall (SSPSW) containing two rectangular openings by integrating verified finite element results, machine learning (ML) models, and gene expression programming. In this regard, a comprehensive nonlinear finite element analysis was conducted, which included 200 records with various values. Considered variables are the thickness and aspect ratio of the steel infill plate, yield strength of the infill plate and boundary frame as well as the ratio of opening area to the total area of the infill plate. Three machine learning (ML) models were employed namely Stochastic Gradient Descent (SGD), Decision Tree (DT), and Random Forest (RF). These models were evaluated on the test data, resulting in scores of 0.96, 0.90, and 0.95, respectively. Among these models, SGD demonstrated superior performance and was identified as the best model for this dataset. Based on the SGD model, an equation was derived to predict the shear capacity of the shear wall. Furthermore, using gene expression programming (GEP) model, an accurate formulation was proposed to calculate the shear capacity of SSPSW system, which led to of 0.98 on the same test data used in the ML models. In addition, by employing the SHapley values technique, the contribution of each characteristic to the final prediction values was explained. This technique showed that the prediction values were significantly influenced by the feature (L/h), while the mechanical characteristics of steel plate and boundary frame had the least impact. Overall, the study underscored the efficacy of the SGD model in predicting the shear capacity of the studied shear walls and provided insights into the relative importance of different features in the prediction process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision tree Finite element gene expression programming Machine learning Random forest Shapley values Steel plate shear wall Stochastic gradient descent

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Mining Implicit Equations From Data Using gene expression programming

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2022年第2期10卷 1058-1074页

作者： Zhong, Jinghui Yang, Jiaquan Chen, Yongliang Liu, Wei-Li Feng, Liang South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Guangdong Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Symbolic regression is an active research topic that has various applications in data mining and knowledge discovery. Existing methods for symbolic regression mainly focus on mining explicit equations. In contrast, implicit equations are more flexible and powerful than explicit equations with regard to describing the relationships between variables in the given dataset. However, evaluating the quality of an implicit equation is more difficult than evaluating an explicit equation. The traditional method for implicit equation evaluation is based on derivative calculation, which requires much time consumption and dense training data. To address the above issues, this article proposes a new evolutionary framework to efficiently mine implicit equations from data. In the proposed framework, a new mechanism (named CL-FEM) is proposed to evaluate implicit equations, and it can efficiently evaluate the accuracy and validity of implicit equations. In addition, a multiple sub-chromosome encoding method based on a least-squares estimator is proposed in the framework to further improve its search efficiency. Based on the proposed evolutionary framework, an efficient algorithm named gene expression programming with a least-squares estimator (LSE-GEP) is developed to mine implicit equations from data. Experimental results demonstrate that the proposed LSE-GEP method performs much better than the recently published methods, in terms of its success rate, accuracy and readability.

关键词： Mathematical model programming gene expression Encoding Data mining Biological cells Time complexity gene expression programming symbolic regression problem implicit equation evaluation mechanism multiple sub-chromosomes encoding method least-squares estimator

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Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP)

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 3581-3597页

作者： Zhang, Jun Shi, Ruoli Shi, Shaohua Alzo'ubi, A. K. Roco-Videla, Angel Hussein, Mohamed M. A. Khan, Afrasyab Yunnan Univ Sch Architecture & Planning Kunming 650091 Yunnan Peoples R China Sichuan Univ Arts & Sci Sch Architectural Engn Dazhou 635000 Sichuan Peoples R China Abu Dhabi Univ Civil Engn Dept Abu Dhabi U Arab Emirates Univ Bernardo OHiggins Fac Ciencias Salud Programa Magister Ciencias Quim Biol Santiago Chile Univ Catolica Santisima Concepcion Fac Ingn Dept Ingn Civil Concepcion Chile Sohag Univ Fac Engn Civil Engn Dept Sohag Egypt South Ural State Univ Inst Engn & Technol Dept Hydraul & Hydraul & Pneumat Syst Lenin Prospect 76 Chelyabinsk 454080 Russia

Rectangular tunnel boring machine (TBM) is applied for the tunnels' excavation including a cross section of circular and rectangular shape within the various rocks and soil strata. Excessive structural forces, which is produced within tunnel linings, might affect the serviceability and safety of tunnels whose forces acting on tunnels linings during the initial design period have to be accurately calculated. Few numerical studies have been conducted on different soil-rectangular tunnel systems to clarify the critical response characteristics of cut-and-cover (rectangular) tunnels adjusted to transversal ground shaking. In this case, predicting the soil dynamic shear stresses developed around the tunnel is an elaborate task due to the interaction of TBM in the rectangular form and the rock. Despite doing the empirical studies in analyzing the rectangular tunnels systems, using artificial intelligence (AI) methods could significantly develop the optimization of TBM tunneling and decreasing the cost, error percentages, disturbance, and time-consuming complications related to tunneling. In this study, two algorithms, namely, support vector machine (SVM) and gene expression programming (GEP), were used to accurately predict the soil dynamic shear stresses developed around the tunnel. The models were developed and measured resulting that SVM can indicate a high-performance capacity in predicting the soil dynamic shear stresses developed around the tunnel through the rectangular TBM excavation machine.

关键词： Rectangular TBM Artificial intelligence Excavation Support vector machine gene expression programming

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Estimation of local scour depth around twin piers using gene expression programming (local scour around twin piers)

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WATER SUPPLY 2022年第6期22卷 5915-5932页

作者： Devi, Geeta Kumar, Munendra Delhi Technol Univ New Delhi 110042 India

The scour around the bridge piers has been estimated using conventional empirical formulae;however, these formulas are unable to predict the scour depth precisely. The present study is conducted in two parts (a) experimental investigation for evaluating the behaviour of local scour around twin piers positioned in the transverse direction of flow and (b) an empirical equation to estimating scour depth is proposed utilising new evolutionary Artificial Intelligence technique gene expression programming (GEP). Experimental results of the present study demonstrate the influence of the rate of flow and clear spacing between the piers on the scour depth. Additionally the results of the soft computing technique GEP during testing and training of proposed modelling, fitness function Root Mean Square Error is observed as 0.00133 and 0.00113, with the coefficient of determination as 0.950 and 0.955, respectively. Furthermore, in order to find out the role of each variable for scour depth sensitivity analysis has been conducted. The findings of the sensitivity analysis show that the pier spacing and rate of flow play the most significant role in scour depth estimation. Results of this study demonstrate good agreement with the proposed GEP model and conclude that it is a better approach for forecasting scour depth.

关键词： bridge piers bridge scour gene expression programming maximum scour depth sensitivity analysis

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Optimization of the Length of Silicon Nanowires Prepared from MACE Method Using gene expression programming and genetic Algorithm

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SILICON 2022年第13期14卷 7633-7642页

作者： Zeraati, Malihe Abbasi, Hossein Safinejad, Moosareza Kazemzadeh, Parya Chauhan, Narendra Pal Singh Sargazi, Ghasem Shahid Bahonar Univ Kerman Dept Mat Engn Kerman 761694111 Iran Azad Univ Tangestan Ahram Dept Comp Engn Tangestan Bushehr Iran Shahid Bahonar Univ Kerman Dept Elect Engn RF MEMS & Micro Bionano Elect MBNE Lab Kerman Iran Lorestan Univ Dept Chem Korramabad Iran Bhupal Nobles Univ Fac Sci Dept Chem Udaipur 313002 Rajasthan India Bam Univ Med Sci Noncommunicable Dis Res Ctr Bam Iran

Metal-assisted chemical etching (MACE) of silicon is used in this study to fabricate silicon nanowire arrays (SiNWs) of different length. This method was modeled using gene expression programming (GEP), and GEP was optimized using genetic algorithms (GA). The length of SiNWs has been predicted as a function of parameters like Ag nucleation on Si and etching solution parameters. The SiNWs in the experimental test have a minimum length of 1 mu m. According to FE-SEM images, SiNWs are uniformly shaped. It could be used to fabricate electrode materials for micro-supercapacitor.

关键词： Silicon nanowire Metal-assisted chemical etching gene expression programming genetic algorithm Optimization

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