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Publisher Correction:Explicit modeling of mechanical property of hot-rolled strip steel based on data-driven and gene expression programming 认领 引用
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作者 Li Wang Qi-ning Zhu +2 位作者 Shun-hu Zhang Lei Zhang Jin-ping Zhang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2025年第12期4531-4531,共1页
Correction to:J.Iron Steel Res.Int.http://gffzzd3cc09b8251d45dfsn5n0oo9c5bwf6o6k.ffgz.tsg.suse.edu.cn/10.1007/s42243-025-01545-x The publication of this article unfortunately contained mistakes.Equation(14)was not correct.The corrected equation is given below.
关键词 mechanical property data driven hot rolled strip steel gene expression programming
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Explicit modeling of mechanical property of hot-rolled strip steel based on data-driven and gene expression programming 认领 引用
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作者 Li Wang Qi-ning Zhu +2 位作者 Shun-hu Zhang Lei Zhang Jin-ping Zhang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2025年第12期4281-4293,共13页
In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on t... In order to solve the black-box modeling problem and improve the prediction accuracy of model,two distinguished models for tensile strength(Ts)and yield strength(Ys)of hot-rolled strip steel are established based on the industrial hot-rolled data and the algorithm of gene expression programming(GEP).Firstly,the industrial data of hot-rolled strip steel are preprocessed using the Pauta criterion,so as to eliminate outliers.The key input variables that affect Ys and Ts are selected by using the method of the maximal information coefficient(MIC).Secondly,the explicit prediction models of Ys and Ts are established using GEP.Subsequently,the model results based on GEP are compared with those based on the support vector regression(SVR)and the back propagation neural network(BPNN).Finally,the mathematical expression models for Ys and Ts obtained by GEP are used to further analyse the specific relationships between the chemical composition and mechanical property.It is shown that the errors of Ys and Ts based on GEP are less than 4%,and the coefficient of determination(R2)of Ys and Ts based on GEP is above 0.9,which has strong prediction performance.The prediction accuracy of GEP can achieve the same level with SVR and BPNN.It is worth mentioning that the proposed model can not only show the explicit relationship between the chemical composition,production process,and mechanical property of strip steel,but also occupy high prediction accuracy,which can make reliable reference for strip steel product design and optimisation. 展开更多
关键词 Gene expression programming Hot-rolled steel Predication of mechanical property Back propagation neural network Support vector regression
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Evaluation of underground hard rock mine pillar stability using gene expression programming and decision tree-support vector machine models 认领 引用
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作者 Mohammad H.Kadkhodaei Ebrahim Ghasemi +1 位作者 Jian Zhou Melika Zahraei 《Deep Underground Science and Engineering》 EI CAS CSCD 2025年第1期18-34,共17页
Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing... Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain. 展开更多
关键词 decision tree-support vector machine(DT-SVM) gene expression programming(GEP) hard rock pillar stability underground mining
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Advanced Machine Learning and Gene Expression Programming Techniques for Predicting CO2-Induced Alterations in Coal Strength 认领 引用
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作者 Zijian Liu Yong Shi +3 位作者 ChuanqiLi Xiliang Zhang Jian Zhou Manoj Khandelwal 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期153-183,共31页
Given the growing concern over global warming and the critical role of carbon dioxide(CO2)in this phenomenon,the study of CO2-induced alterations in coal strength has garnered significant attention due to its im... Given the growing concern over global warming and the critical role of carbon dioxide(CO2)in this phenomenon,the study of CO2-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO2 interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO2-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO2-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO2 saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO2-induced strength prediction,providing valuable insights for geological storage safety and environmental applications. 展开更多
关键词 CO2-induced coal strength meta-heuristic optimization algorithms XGBoost gene expression programming model interpretability
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A true triaxial strength criterion for rocks by gene expression programming 认领 引用 被引量：6
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作者 Jian Zhou Rui Zhang +1 位作者 Yingui Qiu Manoj Khandelwal 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第10期2508-2520,共13页
Rock strength is a crucial factor to consider when designing and constructing underground projects.This study utilizes a gene expression programming(GEP)algorithm-based model to predict the true triaxial strength of r... Rock strength is a crucial factor to consider when designing and constructing underground projects.This study utilizes a gene expression programming(GEP)algorithm-based model to predict the true triaxial strength of rocks,taking into account the influence of rock genesis on their mechanical behavior during the model building process.A true triaxial strength criterion based on the GEP model for igneous,metamorphic and magmatic rocks was obtained by training the model using collected data.Compared to the modified Weibols-Cook criterion,the modified Mohr-Coulomb criterion,and the modified Lade criterion,the strength criterion based on the GEP model exhibits superior prediction accuracy performance.The strength criterion based on the GEP model has better performance in R2,RMSE and MAPE for the data set used in this study.Furthermore,the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types.Compared to the existing strength criterion based on the genetic programming(GP)model,the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength(s1)with intermediate principal stress(s2).Finally,based on the Sobol sensitivity analysis technique,the effects of the parameters of the three obtained strength criteria on the true triaxial strength of the rock are analysed.In general,the proposed strength criterion exhibits superior performance in terms of both accuracy and stability of prediction results. 展开更多
关键词 Gene expression programming(GEP) True triaxial strength Rock failure criteria Intermediate principal stress
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Application of gene expression programming,artificial neural network and multilinear regression in predicting hydrochar physicochemical properties 认领 引用 被引量：1
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作者 Jibril Abdulsalam Abiodun Ismail Lawal +2 位作者 Ramadimetja Lizah Setsepu Moshood Onifade Samson Bada 《Bioresources and Bioprocessing》 SCIE EI 2020年第1期24-45,共22页
Globally,the provision of energy is becoming an absolute necessity.Biomass resources are abundant and have been described as a potential alternative source of energy.However,it is important to assess the fuel characte... Globally,the provision of energy is becoming an absolute necessity.Biomass resources are abundant and have been described as a potential alternative source of energy.However,it is important to assess the fuel characteristics of the various available biomass sources.Soft computing techniques are presented in this study to predict the mass yield(MY),energy yield(EY),and higher heating value(HHV)of hydrothermally carbonized biomass using Gene Expression Programming(GEP),multiple-input single output-artificial neural network(MISO-ANN),and Multilinear regression(MLR).The three techniques were compared using statistical performance metrics.The coefficient of determination(R2),mean absolute error(MAE)and mean bias error(MBE)were used to evaluate the performance of the models.The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models(R2=0.976,0.955,0.996;MAE=2.24,2.11,0.93;MBE=0.16,0.37,0.12)for MY,EY and HHV,respectively.The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory,while MLR provided an unsatisfactory predictive model.Sensitivity analysis was conducted,and the analysis revealed that volatile matter(VM)and temperature(Temp)have more influence on the MY,EY,and HHV. 展开更多
关键词 Artificial neural network Biomass Gene expression programming Higher heating value Hydrochars Hydrothermal carbonization
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Scheduling Rules Based on Gene Expression Programming for Resource-Constrained Project Scheduling Problem 认领 引用 被引量：3
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作者 贾艳 李晋航 《Journal of Donghua University(English Edition)》 EI CAS 2015年第1期91-96,共6页
In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select... In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances. 展开更多
关键词 resource-constrained project scheduling problem(RCPSP) gene expression programming(GEP) scheduling rules(SRs)
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A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method 认领 引用 被引量：7
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作者 Maral Goharzay Ali Noorzad +1 位作者 Ahmadreza Mahboubi Ardakani Mostafa Jalal 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2017年第4期683-693,共11页
In this context,two different approaches of soil liquefaction evaluation using a soft computing technique based on the worldwide standard penetration test(SPT) databases have been studied.Gene expression programming(G... In this context,two different approaches of soil liquefaction evaluation using a soft computing technique based on the worldwide standard penetration test(SPT) databases have been studied.Gene expression programming(GEP) as a gray-box modeling approach is used to develop different deterministic models in order to evaluate the occurrence of soil liquefaction in terms of liquefaction field performance indicator(LI) and factor of safety(FS) in logistic regression and classification concepts.The comparative plots illustrate that the classification concept-based models show a better performance than those based on logistic regression.In the probabilistic approach,a calibrated mapping function is developed in the context of Bayes’ theorem in order to capture the failure probabilities(PL) in the absence of the knowledge of parameter uncertainty.Consistent results obtained from the proposed probabilistic models,compared to the most well-known models,indicate the robustness of the methodology used in this study.The probability models provide a simple,but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction triggering thresholds. 展开更多
关键词 Liquefaction Soft computing technique Gene expression programming(GEP) Deterministic model Bayes' theorem
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Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming 认领 引用 被引量：12
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作者 Bijan Afrasiabian Mosleh Eftekhari 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第5期1421-1432,共12页
Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to p... Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors. 展开更多
关键词 Mode I fracture Toughness Critical stress intensity factor Linear multiple regression(LMR) Gene expression programming(GEP)
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Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem 认领 引用 被引量：2
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作者 Min Hu Zhimin Chen +2 位作者 Yuan Xia Liping Zhang Qiuhua Tang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第9期2815-2840,共26页
Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a r... Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules. 展开更多
关键词 Project scheduling multi-skill gene expression programming dispatching rules
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Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques 认领 引用 被引量：1
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作者 Farzaneh Rezaei Saeed Jafari +1 位作者 Abdolhossein Hemmati-Sarapardeh Amir H.Mohammadi 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2021年第4期431-445,共15页
Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high... Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated. 展开更多
关键词 Gas Viscosity High pressure high temperature Group method of data handling Gene expression programming
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Hybrid Gene Expression Programming-Based Sensor Data Correlation Mining 认领 引用
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作者 Lechan Yang Zhihao Qin +1 位作者 Kun Wang Song Deng 《China Communications》 SCIE CSCD 2017年第1期34-49,共16页
This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality ... This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality reduction algorithm of hyperspectral data based on dependence degree(DRNDDD) is proposed to reduce the redundant hyperspectral band. DRND-DD solves the selection of suitable hyperspectral band via rough set theory. Furthermore, to improve the computation speed and accuracy of the model, based on DRND-DD, this paper proposes reflectance estimation model mining of leaf nitrogen concentration(LNC) for hyperspectral data by using hybrid gene expression programming(REMLNC-HGEP). Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis(PCA), singular value decomposition(SVD), a dimensionality reduction algorithm based on the positive region(AR-PR) and a dimensionality reduction algorithm based on a discernable matrix(ARDM), and REMLNC-HGEP has low average time-consumption, high model mining success ratio and estimation accuracy. It was concluded that the REMLNC-HGEP performs better than the regression methods. 展开更多
关键词 reflectance estimation dimensionality reduction gene expression programming model mining
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Predicting the minimum horizontal principal stress using genetic expression programming and borehole breakout data 认领 引用
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作者 Rui Zhang Jian Zhou 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第7期4227-4240,共14页
As a critical component of the in situ stress state,determination of the minimum horizontal principal stress plays a significant role in both geotechnical and petroleum engineering.To this end,a gene expression progra... As a critical component of the in situ stress state,determination of the minimum horizontal principal stress plays a significant role in both geotechnical and petroleum engineering.To this end,a gene expression programming(GEP)algorithm-based model,in which the data of borehole breakout size,vertical principal stress,and rock strength characteristics are used as the inputs,is proposed to predict the minimum horizontal principal stress.Seventy-nine(79)samples with seven features are collected to construct the minimum horizontal principal stress dataset used for training models.Twenty-four(24)GEP model hyperparameter sets were configured to explore the key parameter combinations among the inputs and their potential relationships with the minimum horizontal principal stresses.Model performance was evaluated using root mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2).By comparing predictive performance and parameter composition,two models were selected from 24 GEP models that demonstrated excellent predictive performance and simpler parameter composition.Compared with prevalent models,the results indicate that the two selected GEP models have better performance on the test set(R2=0.9568 and 0.9621).Additionally,the results conducted by SHapley Additive exPlanations(SHAP)sensitivity analysis and Local Interpretable Model-agnostic Explanations(LIME)demonstrate that the vertical principal stress is the most influential parameter in both GEP models.The two GEP models have simple parameter compositions as well as stable and excellent prediction performance,which is a viable method for predicting the minimum horizontal principal stresses. 展开更多
关键词 Gene expression programming(GEP) In situ stresses Minimum horizontal principal stresses Borehole breakout
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Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar,India 认领 引用
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作者 Pangam Heramb Pramod Kumar Singh +1 位作者 K.V.Ramana Rao A.Subeesh 《Information Processing in Agriculture》 EI CSCD 2023年第4期547-563,共17页
Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource planning.Its quantification is helpful in irrigation scheduling,water balance studies,water allo... Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource planning.Its quantification is helpful in irrigation scheduling,water balance studies,water allocation,etc.Modelling of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 models.The model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the models.All the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing phase.It was found that ANN models were superior to GEP models for the estimation purpose.It was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP models.The ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP models.The comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models. 展开更多
关键词 Artificial Neural Networks Evolutionary algorithms Gene Expression Programming Machine Learning Regression Analysis Reference evapotranspiration models
Rapid Prototype Development Approach for Genetic Programming 认领 引用
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作者 Pei He Lei Zhang 《Journal of Computer and Communications》 2024年第2期67-79,共13页
Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of ... Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals. 展开更多
关键词 Genetic Programming Grammatical Evolution Gene Expression Programming Regression Analysis Mathematical Modeling Rapid Prototype Development
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Artificial intelligence-based prediction of shear modulus and damping ratio of recycled tire rubber-soil mixtures for sustainable engineering applications 认领 引用
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作者 Ahmed Yar Akhtar Hing-Ho Tsang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期2158-2176,共19页
Frugal innovation stands as an imperative cog in the wheel of sustainable development.In the pursuit of simplicity,cost-effectiveness,and environmental compatibility,waste tire rubber and polyurethane-coated rubber(PU... Frugal innovation stands as an imperative cog in the wheel of sustainable development.In the pursuit of simplicity,cost-effectiveness,and environmental compatibility,waste tire rubber and polyurethane-coated rubber(PUcR)emerge as pivotal components in sustainable practices.These materials are advocated for various purposes,including protecting utility tunnels,serving as railway subgrades,and enhancing structural resilience through geotechnical seismic isolation(GSI).Their inherent characteristics,such as modest shear modulus(G)and robust damping ratio(D),make them well-suited for such endeavors,contributing to sustainability goals by repurposing substantial quantities of non-biodegradable waste.For practicality,leveraging artificial intelligence(AI)-based modern computing techniques for recycled material applications is imperative.In this regard,gene expression programming(GEP)was utilized to develop models for predicting the G and D of rubber–soil mixtures(RSMs)and polyurethane-coated RSMs(PUcRSMs).Employing laboratory testing data from 63 samples across three soil types,the newly proposed models demonstrated exceptional accuracy,with correlation coefficient(R2)values of 0.91 and 0.97 for G-prediction of RSM and PUcRSM,and 0.9 and 0.86 for D-prediction,respectively.Using AI-based methods,such as GEP to predict mixtures’dynamic response can cut laboratory costs and optimize mix designs,thereby advancing sustainable material applications. 展开更多
关键词 Frugal innovation Artificial intelligence(AI) Sustainability Gene expression programming(GEP) Recycled waste tire rubber Geotechnical seismic isolation(GSI)
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Double-strand break-free epigenetic programming:a safer path for T-cell therapies 认领 引用
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作者 Lanxin Deng Yujia Yang Assam El-Osta 《Signal Transduction and Targeted Therapy》 SCIE CSCD 2026年第2期526-528,共3页
In a recent study published by Nature Biotechnology,1 Goudy et al.described an all-RNA CRISPRoff/CRISPRon platform that programs endogenous gene expression in primary human T-cells without introducing double-strand br... In a recent study published by Nature Biotechnology,1 Goudy et al.described an all-RNA CRISPRoff/CRISPRon platform that programs endogenous gene expression in primary human T-cells without introducing double-strand breaks,offering a solution to the longstanding safety limitations of nuclease-based multiplex editing.This platform demonstrates durable and locus-specific gene silencing or activation across multiple targets,improved in vivo tumor control,and a scalable,low-toxicity path for nextgeneration T-cell engineering. 展开更多
关键词 programs endogenous gene expression vivo tumor controland epigenetic programming gene activation gene silencing CRISPROFF CRISPRON platform double strand break free primary human T cells
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Smart prediction of liquefaction-induced lateral spreading 认领 引用 被引量：8
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作者 Muhammad Nouman Amjad Raja Tarek Abdoun Waleed El-Sekelly 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第6期2310-2325,共16页
The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(... The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T15)was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively. 展开更多
关键词 Lateral spreading Intelligent modeling Gene expression programming(GEP) Closed-form solution Feature importance
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An Evolutionary Algorithm for Non-Destructive Reverse Engineering of Integrated Circuits 认领 引用 被引量：2
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作者 Huan Zhang Jiliu Zhou Xi Wu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第6期1151-1175,共25页
In hardware Trojan detection technology, destructive reverse engineering can restore an original integrated circuitwith the highest accuracy. However, this method has a much higher overhead in terms of time, effort, a... In hardware Trojan detection technology, destructive reverse engineering can restore an original integrated circuitwith the highest accuracy. However, this method has a much higher overhead in terms of time, effort, and cost thanbypass detection. This study proposes an algorithm, called mixed-feature gene expression programming, whichapplies non-destructive reverse engineering to the chip with bypass detection data. It aims to recover the originalintegrated circuit hardware, or else reveal the unknown circuit design in the chip. 展开更多
关键词 Hardware Trojans Trojan detection mixed-feature gene expression programming
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Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure 认领 引用 被引量：14
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作者 Alireza BIGDELI Aydin SHISHEGARAN +3 位作者 Mohammad Ali NAGHSH Behnam KARAMI Arshia SHISHEGARAN Gholamreza ALIZADEH 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2021年第8期632-656,共25页
In the present study,the performance of reinforced concrete tunnel(RCT)under internal water pressure is evaluated by using nonlinear finite element analysis and surrogate models.Several parameters,including the compre... In the present study,the performance of reinforced concrete tunnel(RCT)under internal water pressure is evaluated by using nonlinear finite element analysis and surrogate models.Several parameters,including the compressive and tensile strength of concrete,the size of the longitudinal reinforcement bar,the transverse bar diameter,and the internal water pre ssure,are considered as the input variables.Based on the levels of variables,36 mix designs are selected by the Taguchi method,and 12 mix designs are proposed in this study.Carbon fiber reinforced concrete(CFRC)or glass fiber reinforced concrete(GFRC)is considered for simulating these 12 samples.Principal component regression(PCR),Multi Ln equation regression(MLnER),and gene expression programming(GEP)are employed for predicting the percentage of damaged surfaces(PDS)of the RCT,the effective tensile plastic strain(ETPS),the maximum deflection of the RCT,and the deflection of crown of RCT.The error terms and statistical parameters,including the maximum positive and negative errors,mean absolute percentage error(MAPE),root mean square error(RMSE),coefficient of determination,and normalized square error(NMSE),are utili zed to evaluate the accuracy of the models.Based on the results,GEP performs better than other models in predicting the outputs.The results sh ow that the internal water pressure and the mechanical properties of concrete have the most effect on the damag e and deflection of the RCT. 展开更多
关键词 Gene expression programming(GEP) Taguchi method Finite element(FE)analysis Effective tensile plastic strain(ETPS) Deflection Damage
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