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Damage detection in truss bridges using transmissibility and machine learning algorithm: Application to Nam O bridge

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SMART STRUCTURES AND SYSTEMS 2020年第1期26卷 35-47页

作者： Duong Huong Nguyen Tran-Ngoc, H. Bui-Tien, T. De Roeck, Guido Wahab, Magd Abdel Univ Ghent Fac Engn & Architecture Dept Elect Energy Met Mech Construct & Syst Ghent Belgium Natl Univ Civil Engn Fac Bridge & Rd Dept Bridge & Tunnel Engn Hanoi Vietnam Univ Transport & Commun Fac Civil Engn Dept Bridge & Tunnel Engn Hanoi Vietnam Dept KU Leuven Dept Civil Engn Struct Mech B-3001 Leuven Belgium Ton Duc Thang Univ Div Computat Mech Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used;one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

关键词： transmissibility machine learning algorithm Artificial Neural Networks (ANNs) Structural Health Monitoring (SHM) large-scale truss bridge

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Bioethanol production optimization through machine learning algorithm approach: biomass characteristics, saccharification, and fermentation conditions for enzymatic hydrolysis

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BIOMASS CONVERSION AND BIOREFINERY 2023年第8期13卷 7287-7299页

作者： Vinitha, Nithianantharaj Vasudevan, Jaikumar Gopinath, Kannappan Panchamoorthy Sri Sivasubramaniya Nadar Coll Engn Dept Chem Engn Thiruporur 603110 Tamil Nadu India

In this study, the optimized decision-making system (OD-MS) algorithm in machine learning for optimizing the enzymatic hydrolysis saccharification, fermentation conditions, and its yield was studied. Two hundred fifty datasets were collected as training data from various studies and experimental results. Initially, the data of various biomass and product conditions were collected, and their correlation coefficient values were determined using the Pearson correlation matrix. Test datasets were analyzed in an optimized decision-making system to predict the data value, and the process was repeated until the desired data was achieved. 3-D surface analysis was performed to determine the product yield ranges based on biomass characteristics and process conditions. Maximum glucose yield (>50 g/L) and ethanol yield (>40 g/L) were achieved with the increase in cellulose (>73%), S-temp (55-60 degrees C), S-pH (7-9), S-shaking speed (180-200 rpm), F-pH (4.5), F-time (<40 h), and F-shaking speed (120-150 rpm) and decrease in hemicellulose (<10%), lignin (<10%), S-time (<20 h), and F-time (20 h). Weighted rank was assigned to biomass characteristics and process conditions to find the optimum parameter conditions using correlation values for obtaining a better yield. Two-step validation was done to find the accuracy of biomass characteristics and process conditions. A 95% of accuracy was found while comparing the actual dataset with the data predicted using the OD-MS algorithm for various biomass characteristics and process conditions. The R 2 value of this model was found to be 0.9762.

关键词： Glucose Ethanol yield Optimized decision-making system algorithm machine learning algorithm Support vector machine

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Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 951卷 175830页

作者： Fu, Songzhe Zhang, Yixiang Li, Yinghui Zhang, Ziqiang Du, Chen Wang, Rui Peng, Yuejing Yue, Zhijiao Xu, Zheng Hu, Qinghua Northwest Univ Sch Med Key Lab Resource Biol & Biotechnol Western China Minist Educ Xian 710069 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci SIBS CAS Ctr Excellence Mol Plant Sci Shanghai Peoples R China Shenzhen Ctr Dis Control & Prevent Shenzhen Peoples R China Dalian Ocean Univ Coll Marine Sci & Environm Dalian 116023 Peoples R China Southern Univ Sci & Technol Yantian Hosp Shenzhen 518081 Peoples R China Univ Chinese Acad Sci Shanghai Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Inst Biomed & Biotechnol Shenzhen 518055 Guangdong Peoples R China

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT-qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARSCoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

关键词： SARS-CoV-2 Influenza A virus machine learning algorithm Epidemic trajectories Prediction

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Co-firing characteristic prediction of solid waste and coal for supercritical CO2 power cycle based on CFD simulation and machine learning algorithm

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WASTE MANAGEMENT 2024年 190卷 74-87页

作者： Cui, Ying Wang, Xinwang Jiang, Shujun Gu, Xiaoyong Yao, Zhongran Wuxi Inst Technol Sch Automot & Transportat Wuxi 214000 Jiangsu Provinc Peoples R China Southeast Univ Sch Energy & Environm Key Lab Energy Convers & Proc Measurement & Contro Minist Educ Xuanwu Dist Nanjing 210096 Jiangsu Peoples R China Monash Univ Dept Chem Engn ARC Res Hub Computat Particle Technol Clayton Vic 3800 Australia Wuxi Inst Technol Sch Integrated Circuits Wuxi 214000 Jiangsu Provinc Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210018 Peoples R China

The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO2 (S-CO2) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, and reduce pollutant emission. This study focuses on the co-firing characteristics of CSW and coal under S-CO2 power cycle, and simulations are conducted by employing Multiphase Particle-in-cell (MP-PIC) method integrated with the comprehensive chemical reaction models in a 300 MW S-CO2 CFB boiler. Effects of operating parameters including fuel mixture proportion and first stage stoichiometry on the gas emission characteristics are further analyzed. Based on training and testing database based on the simulation results, a novel Improved Whale Optimization algorithm and Bi-dictionary Long Short-Term Memory (IWOA-BiLSTM) algorithm model is established to predict CFB temperature, NOx emission concentration, and SO2 emission concentration, respectively. Results show that CO and SO2 decrease with the coal mass ratio of the fuel mixture increasing, while NOx increases. With the increase of first stage stoichiometry, CO increases, NOx declines, and the change of SO2 is not obvious. Compared with two other basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.032 %, 0.231 %, and 0.157 %, respectively, which can meet the prediction requirements with acceptable accuracy.

关键词： Supercirtical CO (2 )power cycle Combustible solid waste MP-PIC simulation machine learning algorithm Co-firing characteristic prediction

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Terahertz spectroscopy and machine learning algorithm for non-destructive evaluation of protein conformation

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OPTICAL AND QUANTUM ELECTRONICS 2020年第4期52卷 225-225页

作者： Cao, Can Zhang, Zhaohui Zhao, Xiaoyan Zhang, Tianyao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100081 Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100081 Peoples R China

Given the condition that protein conformation and activity are highly susceptible to environment factors such as temperature and pH, evaluation of protein conformation and activity is urgently needed in many fields. For example, most protein drugs need a stable and proper environment during production, storage and transportation, and it's an enormous challenge to maintain protein activity throughout the whole process. Therefore, it's necessary to ensure the safety and effectiveness of protein drugs by monitoring their activity before use. In our study, we presented an improved method for non-destructive evaluation of protein conformation and biological activity by terahertz spectroscopy combined with t-SNE-XGBoost. Firstly, bovine serum albumin (BSA) samples heated to different temperature were measured with THz-TDS. The obtained results indicated that native-conformation BSA will undergo transient states in the process of temperature induced denaturation. However, for any single given sample, it's difficult to identify its conformation and activity directly by using the measured raw terahertz data. Therefore, we applied several different algorithms to the raw data for recognition of BSA samples with different conformation and activity induced by temperature. Finally, the models obtained by different algorithms were evaluated by calculating the root mean standard error of prediction (RMSEP) and the correlation coefficient of prediction (Rp). The THz-TDS plus t-SNE-XGBoost proved to be an effective non-destructive and label-free method for evaluation of protein conformation and activity. It can provide a new technique in many applications, such as pharmaceutical industry, clinical diagnosis and quality control.

关键词： Terahertz time-domain spectroscopy (THz-TDS) Dimension reduction algorithm machine learning algorithm Non-destructive evaluation Protein conformation

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Application of machine learning algorithm and Carrera unified formulation in thermal buckling analysis of a functionally graded graphene origami enabled auxetic metamaterial sandwich plate with an auxetic concrete foundation

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年

作者： Lu, Qiang Yang, Qing Atif, M. El-Meligy, Mohammed Chongqing Vocat Inst Engn Sch Civil Engn Chongqing 402260 Peoples R China King Saud Univ Coll Sci Dept Phys & Astron Riyadh Saudi Arabia Middle East Univ MEU Res Unit Amman Jordan Jadara Univ Res Ctr Irbid Jordan

This study presents a comprehensive thermal buckling analysis of sandwich plates composed of functionally graded graphene origami-enabled auxetic metamaterial (FG-GOEAM) face sheets on an auxetic concrete foundation, using Carrera's unified formulation (CUF) as the theoretical framework. FG-GOEAM materials are emerging as advanced composites, combining exceptional mechanical resilience, tunable auxetic behavior, and high thermal stability, making them suitable for extreme environments. By employing CUF, a powerful and adaptable modeling approach, this work accurately captures the complex mechanical interactions within the FG-GOEAM sandwich structure under thermal loads, incorporating both material gradation and auxetic properties. To further enhance the precision and efficiency of this thermal buckling analysis, a deep neural network (DNN) is developed as a machine learning algorithm to predict critical temperature differences, based on a dataset generated through mathematics simulation. The DNN model demonstrates excellent predictive capability, validated by close alignment between its estimates and CUF results, thus reducing computational costs while maintaining high accuracy. Parametric studies are conducted to assess the effects of material gradation, aspect ratios, and foundation properties on thermal buckling performance. The results highlight the superior thermal stability of FG-GOEAM structures and the potential of DNNs to serve as reliable, computationally efficient tools for advanced structural analysis. This study provides a novel, integrated framework for high-fidelity thermal buckling prediction in complex auxetic composites, paving the way for broader applications in engineering fields requiring lightweight, thermally stable structures.

关键词： Carrera unified formulation thermal buckling FG-GOEAM face sheets auxetic concrete foundation machine learning algorithm

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Privacy in process mining: Logs conversion to images using machine learning algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 1-12页

作者： Agordzo, George K. Fang, Xianwen Li, Juan Anhui Univ Sci & Technol Coll Math & Big Data Huainan Peoples R China

In today's digital age, log files are crucial. However, the conversion of text log files into images has only recently been developed. The security of log files is a major source of concern, and the security of the systems in which the logs are stored determines the safety of the log file in process mining. This calls for the first conversion of a text log file into an image file. Thus, this research aims to convert the log files into images in a mugshot database and detect illegal activity and criminals from the converted images employing a novel Convolutional Neural Network (CNN). The developed model has three stages: pre-processing, feature extraction, and detection and matching. The pre-processing was performed by min-max normalization, and in feature extraction, the deep learning method was used. Moreover, in the detection phase, CNN is employed for detecting illegal activities, and the matching process is performed for detecting illegal activities from converted images and criminals in the mugshot database. The model's performance is evaluated in terms of precision, F1-score, recall, and accuracy values of 99.6%, 98.5%, 98.7%, and 99.8%, respectively. A further comparison has been performed to show the effectiveness of the suggested model over other methods.

关键词： Privacy log file convolutional neural network process mining machine learning algorithm

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Classification of Foot Pressure Images Using machine learning algorithm

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Computer Systems Science & Engineering 2022年第4期41卷 187-196页

作者： P.Ramya B.Padmapriya S.Poornachandra Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatore641107Tamil NaduIndia Department of Biomedical Engineering PSG College of TechnologyCoimbatore641004Tamil NaduIndia Department of Electronics and Communication Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062Tamil NaduIndia

Arthritis is an acute systemic disease of a joint accompanied by *** developed countries,it mainly causes disability among people over 50 years of *** Arthritis is a type of arthritis that occurs commonly among *** incidence of arthritis is higher in females than in *** is no permanent diagnosis method for arthritis,but if it was identiﬁed in the early stages based on the foot pressure,it can be diagnosed before attaining the critical stage of Rheumatoid *** analysis and study of arthritis patients were done using design thinking *** thinking is a problem-solving methodology that is used toﬁnd a solution for the identiﬁcation of the early stage of *** process consists ofﬁve stages follows Empathy,Deﬁne,Ideate,Prototype,and *** deﬁne the problem statement,the Empathy was done with the arthritis patients to know the difﬁculties faced by *** paper proposes a measurement technique of early measurement of arthritis using a non-invasive *** helps us to detect arthritis using a foot pressure pad that was designed with piezoresistive material and the feature classiﬁcation was done using Weka.

关键词： Piezoresistive material velostat carbon loaded piezo resistiveﬁlm machine learning algorithm SVM MLP classiﬁcation design thinking

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Wind speed forecast model for wind farm based on a hybrid machine learning algorithm

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 2015年第1期34卷 38-51页

作者： Ul Haque, Ashraf Mandal, Paras Meng, Julian Negnevitsky, Michael Montana State Univ Dept Elect & Comp Engn Bozeman MT 59717 USA Univ Texas El Paso Dept Ind Mfg & Syst Engn El Paso TX 79968 USA Univ New Brunswick Dept Elect & Comp Engn Fredericton NB E3B 5A3 Canada Univ Tasmania Sch Engn Hobart Tas 7001 Australia

This paper presents a newstrategy for wind speed forecasting based on a hybrid machine learning algorithm, composed of a data filtering technique based onwavelet transform (WT) and a soft computing model based on the fuzzyARTMAP (FA) network. The prediction capability of the proposed hybrid WT + FA model is demonstrated by an extensive comparison with some other existing wind speed forecasting methods. The results show a significant improvement in forecasting error through the application of a proposed hybrid WT + FA model. The proposed wind speed forecasting strategy is applied to real data acquired from the North Cape wind farm located in PEI, Canada.

关键词： Fuzzy ARTMAP network machine learning algorithm short-term wind speed forecasting wavelet transform

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Development of a machine learning algorithm to predict complications of total laparoscopic anterior resection and natural orifice specimen extraction surgery in rectal cancer

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EJSO 2023年第7期49卷 1258-1268页

作者： Wei, Ran Guan, Xu Liu, Enrui Zhang, Weiyuan Lv, Jingfang Huang, Haiyang Zhao, Zhixun Chen, Haipeng Liu, Zheng Jiang, Zheng Wang, Xishan Chinese Acad Med Sci & Peking Union Med Coll Canc Hosp Natl Canc Ctr Natl Clin Res Ctr CancDept Colorectal Surg Beijing Peoples R China Harbin Med Univ Affiliated Hosp 2 Dept Colorectal Surg Harbin Peoples R China

Background: Total laparoscopic anterior resection (tLAR) and natural orifice specimen extraction surgery (NOSES) has been widely adopted in the treatment of rectal cancer (RC). However, no study has been performed to predict the short-term outcomes of tLAR using machine learning algorithms to analyze a national ***: Data from consecutive RC patients who underwent tLAR were collected from the China NOSES Database (CNDB). The random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), deep neural network (DNN), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to develop risk models to predict short-term complications of tLAR. The area under the receiver operating characteristic curve (AUROC), Gini coefficient, specificity and sensitivity were calculated to assess the performance of each risk model. The selected factors from the models were evaluated by relative ***: A total of 4313 RC patients were identified, and 667 patients (15.5%) developed postoperative complications. The machine learning model of XGBoost showed more promising results in the prediction of complication than other models (AUROC 0.90, P < 0.001). The performance was similar when internal and external validation was used. In the XGBoost model, the top four influential factors were the distance from the lower edge of the tumor to the anus, age at diagnosis, surgical time and comorbidities. In risk stratification analysis, the rate of postoperative complications in the high-risk group was significantly higher than in the medium-and low-risk groups (P < 0.001).Conclusion: The machine learning model shows potential benefits in predicting the risk of complications in RC patients after tLAR. This novel approach can provide reliable individual information for surgical treatment recommendations.& COPY;2023 Published by Elsevier Ltd.

关键词： machine learning algorithm Postoperative complications Natural orifice specimen extraction surgery Rectal cancer Totally laparoscopic anterior resection

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