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Review of analytical methods for stress and deformation analysis of buried water pipes considering pipe-soil interaction

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Underground Space 2023年第6期13卷 205-227页

作者： Yingxu Huo sherif mohsen mohamed hassan gomaa Tarek Zayed mohamed Meguid Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomHong Kong 999077China Department of Building and Real Estate The Hong Kong Polytechnic UniversityHung HomHong Kong 999077China Structural Engineering Department Faculty of EngineeringMansoura UniversityMansoura 35516Egypt Department of Civil Engineering McGill UniversityMontrealQuebec H3A 0G4Canada

Buried water pipelines are vulnerable to fail or break due to excessive loading or ground *** evaluation of pipe performance and serviceability relies on the proper understanding of pipe-soil interactions(PSI).Analytical methods are important approaches to studying ***,a systematic and thorough literature review to analyze the existing research trends,technological achievements and future research opportunities is not *** work investigates analytical methods that analyze the stress and deformation of pipes in terms of cross-sectional,transverse and longitudinal PSI ***,scientometric analysis is performed to acquire relevant research works from online databases and analyze the existing data of influential authors,productive research sources and frequent key word occurrence in the fields of ***,a qualitative discussion is performed in the three categories of PSI:(1)cross-sectional,including ovalization and circumferential behaviours;(2)transverse,including seismic fault crossing,weak soil zones,ground settlement and pipe uplift;and(3)***,six research opportunities are discussed,including the role of friction in cross-sectional deformation,combined effects of bending and compression,choice of soil reaction models and calibration of key parameters,effect of pipe flaws,soil spatial variability and behaviours of curved *** study helps beginners familiarize themselves with PSI analytical methods and provides experienced researchers with ideas for future research directions.

关键词： Pipeline Analytical methods Pipe-soil interactions Seismic fault Settlement Uplift Buckling Tunnelling

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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Computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed mohsen sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf mohamed Ali hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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