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The Coupled Effect of Temperature Changes and Damage Depth on Natural Frequencies in Beam-Like Structures

Structural Durability & Health Monitoring 2022年第1期16卷 15-35页

作者： Tareq Al-hababi nizar faisal alkayem Li Cui Shixiang Zhang Cong Liu Maosen Cao Anhui Provincial International Joint Research Center of Data Diagnosis and Smart Maintenance on Bridge Structures Chuzhou UniversityChuzhou239000China Department of Engineering Mechanics Hohai UniversityNanjing210098China College of Civil and Transportation Engineering Hohai University Nanjing210098China College of Civil and Architecture Engineering Chuzhou UniversityChuzhou239000China China Design Group Co. Ltd.Nanjing210014China Jiangsu Zhongqiao Technology Research Co. Ltd.Nantong226000China

A significant amount of research is concerned with dynamic modal parameters for damage detection of structural conditions due to their simplicity in use and ***,their use for damage detection should be performed with special attention,particularly in operational and environmental conditions subjected to temperature *** in construction industries experience different loading types,such as temperature changes leading to crack initiation and *** physical and dynamic properties such as natural frequencies and mode shapes indicate that damage has occurred within the *** this study,vibration analysis of cantilever and cantilever simply supported beams has been carried out on intact and damaged beams to investigate the coupled effect of temperature changes and damage depth on natural frequencies.A numerical analysis of beams is completed using ANSYS *** results of numerical simulation are validated using two other studies from *** results revealed that in order to perform a successful damage assessment using the frequency shift,the vibration modes should be selected *** addition,an increase in temperature results in a decrease in structural *** assessment of the effect of damage depth on natural frequencies also confirms that when damage depth is increased,there is a significant decrease in natural frequency responses.

关键词： Temperature effect damage depth natural frequency beam damage detection modal analysis

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Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

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Computers, Materials & Continua 2021年第5期67卷 2029-2047页

作者： Mohammed A.Aleid Khaled A.Z.Alyamani Mohieddine Rahmouni Theyazn H.H.Aldhyani nizar Alsharif Mohammed Y.Alzahrani College of Education King Faisal UniversityAl-Ahsa31982Saudi Arabia Community College in Abqaiq King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Economics and Quantitative Methods ESSECTUniversity of TunisTunisTunisia Department of Computer Engineering and Science Al-Baha UniversityAl-BahahSaudi Arabia

This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 *** other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new *** focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)*** to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and *** health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age *** average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic *** results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety.

关键词： COVID-19 health habits safety behaviors anxiety support vector machine regression regression trees

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Data Analytics for the Identification of Fake Reviews Using Supervised Learning

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Computers, Materials & Continua 2022年第2期70卷 3189-3204页

作者： Saleh Nagi Alsubari Sachin N.Deshmukh Ahmed Abdullah Alqarni nizar Alsharif Theyazn H.H.Aldhyani Fawaz Waselallah Alsaade Osamah I.Khalaf Department of Computer Science&Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabadIndia Department of Computer Sciences and Information Technology Albaha UniversitySaudi Arabia Department of Computer Engineering and Science Albaha UniversitySaudi Arabia Community College of Abqaiq King Faisal UniversityAl-AhsaSaudi Arabia College of Computer Science and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia Al–Nahrain University BagdadIraq

Fake reviews,also known as deceptive opinions,are used to mislead people and have gained more importance *** is due to the rapid increase in online marketing transactions,such as selling and purchasing.E-commerce provides a facility for customers to post reviews and comment about the product or service when *** customers usually go through the posted reviews or comments on the website before making a purchase ***,the current challenge is how new individuals can distinguish truthful reviews from fake ones,which later deceives customers,inflicts losses,and tarnishes the reputation of *** present paper attempts to develop an intelligent system that can detect fake reviews on ecommerce platforms using n-grams of the review text and sentiment scores given by the *** proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency(TF-IDF)approach for extracting features and their *** detection and classification,n-grams of review texts were inputted into the constructed models to be classified as fake or ***,the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor *** classification results of these experiments showed that na飗e Bayes(NB),support vector machine(SVM),adaptive boosting(AB),and random forest(RF)received 88%,93%,94%,and 95%,respectively,based on testing accuracy and tje *** obtained results were compared with existing works that used the same dataset,and the proposed methods outperformed the comparable methods in terms of accuracy.

关键词： E-commerce fake reviews detection methodologies machine learning hotel reviews

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