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| 1 | Application of several optimization techniques for estimating TBM advance rate in granitic rocks显示文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior. | Danial Jahed Armaghani Mohammadreza Koopialipoor Aminaton Marto Saffet Yagiz | 2019 | Journal of Rock Mechanics and Geotechnical Engineering2019，11，4： | 15 |
| 2 | Particular functions of estrogen and progesterone in establishment of uterine receptivity and embryo implantation显示文摘 | Saffet O Ramazan D | | 0，，09： | 1 |
| 3 | Utilizing rock mass properties for predicting TBM performance in hard rock condition显示文摘 | Saffet Y | 2008 | Tunnelling and Underground Space Technology2008，23，3： | 1 |
| 4 | Utilizing Rock Mass Properties for Predicting TBM Performance in Hard Rock Condition显示文摘 | Saffet Yagiz | | 0，，3： | 1 |
| 5 | Impact of Electric Vehicle Aggregator with Communication Time Delay on Stability Regions and Stability Delay Margins in Load Frequency Control System显示文摘This paper investigates the impact of electric vehicle(EV)aggregator with communication time delay on stability regions and stability delay margins of a single-area load frequency control(LFC)system.Primarily,a graphical method characterizing stability boundary locus is implemented.For a given time delay,the method computes all the stabilizing proportional-integral(PI)controller gains,which constitutes a stability region in the parameter space of PI controller.Secondly,in order to complement the stability regions,a frequency-domain exact method is used to calculate stability delay margins for various values of PI controller gains.The qualitative impact of EV aggregator on both stability regions and stability delay margins is thoroughly analyzed and the results are authenticated by time-domain simulations and quasi-polynomial mapping-based root finder(QPmR)algorithm. | Ausnain Naveed Sahin Sonmez Saffet Ayasun | 2021 | Journal of Modern Power Systems and Clean Energy2021，9，3： | 1 |
| 6 | DC Motor Speed Control Methods Using MATLAB/Simulink and Their Integration into Undergraduate Electric Machinery Courses显示文摘 | Ayasun Saffet Karbeyaz Gueltekin | 2007 | Computer Appli- cationsin Engineering Education2007，15，4： | 1 |
| 7 | Cerebellar Mutism: Report of Seven Cases and Review of the Literature显示文摘 | Yusuf Er?ahin Saffet Mutluer Sedat ?a?li Yusuf Duman | 1996 | Neurosurgery1996，，1： | 1 |
| 8 | Brain-derived neurotrophic factor, stress and depression: a minireview显示文摘 | BURAK YULU G EROL OZAN ALI SAFFET GONOL | 2009 | Brain Research Bulletin2009，78，6： | 1 |
| 9 | Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction显示文摘To date,the accurate prediction of tunnel boring machine(TBM)performance remains a considerable challenge owing to the complex interactions between the TBM and ground.Using evolutionary polynomial regression(EPR)and random forest(RF),this study devel-ops two novel prediction models for TBM performance.Both models can predict the TBM penetration rate and field penetration index as outputs with four input parameters:the uniaxial compressive strength,intact rock brittleness index,distance between planes of weakness,and angle between the tunnel axis and planes of weakness(a).First,the performances of both EPR-and RF-based models are examined by comparison with the conventional numerical regression method(i.e.,multivariate linear regression).Subsequently,the performances of the RF-and EPR-based models are further investigated and compared,including the model robustness for unknown datasets,interior relationships between input and output parameters,and variable importance.The results indicate that the RF-based model has greater prediction accuracy,particularly in identifying outliers,whereas the EPR-based model is more convenient to use by field engineers owing to its explicit expression.Both EPR-and RF-based models can accurately identify the relationships between the input and output param-eters.This ensures their excellent generalization ability and high prediction accuracy on unknown datasets. | Jie Yang Saffet Yagiz Ying-Jing Liu Farid Laouafa | 2022 | Underground Space2022，7，1： | 1 |
| 10 | Effect of local or systemic treatment prior to primary tumour removal on the production and response to a serum growth stimulating factor in mice显示文摘 | SAFFET E A RUDOCK C | 1989 | Cancer Res1989，49，： | 1 |
| 11 | Intraosseous neurinoma of the parietal bone显示文摘 | Yusuf Ersahin Saffet Mutluer Eren Demirtas | 2000 | Child''s Nerv Syst2000，16，3： | 1 |
| 12 | Effects of olanzapine and haloperidol on serum prolactin levels in male schizophrenic patients 显示文摘 | Esel E Basturk M Saffet Gonul A | 2001 | Psychoneuroenclocrinology2001，26，6： | 1 |
| 13 | Explor-atory spatial analysis of crimes against property in Turkey显示文摘 | Saffet Erdogan Mustafa Yalcin Mehmet Ali Dereli | | 0，，1： | 1 |
| 14 | DC motor speed control methods using MATLAB/Simulink and their integration into undergraduate electric machinery courses 显示文摘 | Saffet Ayastm Gultekin Karbeyaz | 2007 | Computer Applications in Engineering Education (S1061-3773)2007，15，4： | 1 |
| 15 | Utilizing rock mass properties for predicting TBM performance in hard rock condition显示文摘 | Saffet Yagiz | 2007 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2007，，3： | 1 |
| 16 | Modeling the spatial distribution of DEMerror with geographically weighted regression: An ex-perimental study 显示文摘 | SAFFET E | 2010 | Computers Geosciences2010，36，1： | 1 |
| 17 | Effect of local or systemic treatment prior to primary tumour removal on the production and response to a serum gTowth stimulating factor in mice显示文摘 | Fisher B Saffet E Rudock C | 1989 | Cancer Res1989，49，8： | 1 |
| 18 | Particular functions of estrogen and progesterone in establishment of uterine receptivity and embryo implantation显示文摘 | Saffet Ozturk Ramazan Demir | 2010 | Histology and Histopathology2010，25，9： | 1 |
| 19 | Modeling and stability analysis of a simulation-stimulation interface for hardware-in-the-loop applications显示文摘 | Saffet Ayasun robert Fischl sean Vallieu | 2007 | Simulation Modelling Practice and Theory2007，，： | 1 |
| 20 | An intelligent procedure for updating deformation prediction of braced excavation in clay using gated recurrent unit neural networks显示文摘This paper aims to establish an intelligent procedure that combines the observational method with the existing deep learning technique for updating deformation of braced excavation in clay.The gated recurrent unit(GRU) neural network is adopted to formulate the forecast model and learn the potential rules in the field observations using the Nesterov-accelerated Adam(Nadam) algorithm.In the proposed procedure,the GRU-based forecast model is first trained based on the field data of previous and current stages.Then,the field data of the current stage are used as input to predict the deformation response of the next stage via the previously trained GRU-based forecast model.This updating process will loop up till the end of the excavation.This procedure has the advantage of directly predicting the deformation response of unexcavated stages based on the monitoring data.The proposed intelligent procedure is verified on two well-documented cases in terms of accuracy and reliability.The results indicate that both wall deflection and ground settlement are accurately predicted as the excavation proceeds.Furthermore,the advantages of the proposed intelligent procedure compared with the Bayesian/o ptimization updating are illustrated. | Jie Yang Yingjing Liu Saffet Yagiz Farid Laouafa | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 1 |
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