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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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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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Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised machine learning algorithm Data Mining in a Secure Cloud Computing Environment through Restful API

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第5期13卷 1-4页

作者： Ige, Tosin Sikiru, Adewale Univ Texas El Paso Dept Comp Sci El Paso TX 79968 USA Virginia Technol Univ Dept Comp Sci Blacksburg VA USA

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.

关键词： Cloud computing data warehouse data mining window service Web API machine learning algorithm secure cloud computing

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Non-linearities mitigation with fuzzy neural networks using a machine learning algorithm in a CO-OFDM system

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IET OPTOELECTRONICS 2020年第1期14卷 44-51页

作者： Kaur, Gurpreet Kaur, Gurmeet Indian Inst Informat Technol ECE Una Himachal Prades India Punjabi Univ ECE Patiala Punjab India

This study presents a fuzzy neural network based non-linear equaliser to diminish the non-linearities in a coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. The numerical results show that the proposed technique based CO-OFDM system outperforms the CO-OFDM system without non-linear equaliser by 5 and 7.5% EVM performance, and 2.36 and 4.87 dB Q-factor performance after 1000 km transmission and -3 dBm input launch power at a bit rate of 40 and 80 Gbps, respectively. Moreover, it has been generally accepted in statistics that the rank-based Wilcoxon methodology provide more robust results in a contradiction of outliers. Therefore, the aim of this study is to analyse fuzzy neural network based non-linear equaliser and compare the results with that of Wilcoxon approach fuzzy neural network based non-linear equaliser.

关键词： OFDM modulation fuzzy neural nets optical fibre communication Q-factor equalisers CO-OFDM system machine learning algorithm fuzzy neural networks coherent optical orthogonal frequency division multiplexing system nonlinear equaliser Q-factor performance input launch power rank-based Wilcoxon methodology Wilcoxon approach fuzzy neural network based non-linear equaliser

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Application of machine learning algorithm in the internal and external hazards from industrial byproducts

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CLEANER ENGINEERING AND TECHNOLOGY 2023年 13卷

作者： Oyebisi, Solomon Owamah, Hilary Omeje, Maxwell Covenant Univ Dept Civil Engn PMB 1023Km 10Idiroko Rd Ota Nigeria Delta State Univ Dept Civil & Environm Engn Abraka Nigeria Covenant Univ Dept Phys PMB 1023Km 10Idiroko Rd Ota Nigeria

Natural radioactive substances that are produced because of industrial processes pose a risk to both the envi-ronment and people. An extensive analysis of the radiological properties of industrial byproducts was undertaken in this work, and the risks for both indoor and outdoor environments were assessed based on activity concen-trations. The machine learning technique of artificial neural networks was used with various training algorithms to predict the internal and external hazards from these industrial byproducts. The findings demonstrated that, with the exception of incinerated sewage sludge ash, metakaolin, marble powder, nickel slag, pyrite ash, silica fume, steel slag, and glass waste powder, every industrial byproduct examined poses potential indoor and out-door dangers. All backpropagation training algorithms that were used showed high prediction, according to the neural networks. However, when compared to the Bayesian Regularization and Scaled Conjugate Gradient backpropagation training algorithms, the Levenberg-Marquardt backpropagation technique had the best per-formance indicators for training, validation, and testing. The results can provide reference information for developing a framework for monitoring hazards and their accompanying precise management.

关键词： Cleaner production machine learning algorithm Recycling Responsible consumption and production Waste valorization

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Elevator dynamic monitoring and early warning system based on machine learning algorithm

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IET NETWORKS 2022年第0期

作者： Zhang, Shuai Yin, Qiangguo Wang, Jinlong Weifang Engn Vocat Coll Dept Informat Engn Weifang Shandong Peoples R China Weifang Engn Vocat Coll Dept Informat Engn Weifang 262500 Shandong Peoples R China

With the rapid progress of modern society, elevator, as a vertical means of transportation in modern high-rise buildings, has become an indispensable part of People's Daily life. Because the elevator is a kind of mechanical equipment with a very high frequency of use, the peak hour will reach 250 times per hour, and because the elevator in the daily operation of the running parts work many times, the operating system is complex, good quality. Different passengers lead to frequent elevator failures. The dynamic monitoring and early warning system is a system which, according to the characteristics of the elevator, monitors the change trend of risk factors in the elevator by collecting relevant information, and evaluates the degree of deviation of various risk states from the early warning line, so as to send early warning signals to the decision-making level and take pre-control countermeasures in advance. machine learning is a multi-domain interdisciplinary discipline, which specialises in studying how computers simulate or realise human learning behaviours to acquire new knowledge or skills and reorganise existing knowledge structures to continuously improve their own performance. This study aims to monitor and warn the elevator dynamics. machine learning algorithm is introduced in this work, and particle swarm optimisation algorithm is used to improve the model. The model is optimised, and the experimental comparison shows that the optimisation of model parameters can further improve the accuracy of elevator load prediction. Then, the deep network prediction model is constructed, and the number of network layers is determined through experiments. Firstly, a multi-layer network is trained in the training model, and this process can be used to extract the indicators of inducing factors, so as to avoid the difficult and time-consuming manual extraction of indicators, and improve the prediction accuracy of the system. After five experiments, the prediction errors of t

关键词： deep confidence network machine learning algorithm monitoring and early warning system particle swarm algorithm

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A modelling approach based on Bayesian networks for dam risk analysis: Integration of machine learning algorithm and domain knowledge

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2022年 71卷

作者： Tang, Xianqi Chen, Anyi He, Jinping Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Wuhan Univ Sch Water Resources & Hydropower Engn Wuhan 430072 Peoples R China

The safety of dams, especially that of earthen dams, is threatened by various uncertain and interrelated risk factors. Consequently, dam risk analysis is vital for dam safety governance and failure prevention. A Bayesian network (BN) is an effective tool for this issue as its excellent ability in representing uncertainty and reasoning. Most previous studies have relied solely on domain knowledge (DK) to establish BN models, leading to inefficient and subjective results when solving complex systems. The increasing observations has improved the viability of using machine learning (ML) to automatically model complex systems. Herein, ML algorithms are used to develop automatic BN models for risk analysis of earthen dams in the USA, which are subsequently modified using DK. The results revealed that the automatic BN models can identify some potential causal relationships that are ignored by DK, whereas some impractical causalities identified in the automatic BN models can be modified by using DK. Moreover, the modified BN model has a better performance in the prediction of earthen dam failure with an average overall accuracy of 84.6%, compared to 80.3% with the automatic BN models, and 76.5% with a manual BN model created using only DK. Using the modified BN models, the three foremost risk factors based on their influence and sensitivity analysis were identified to be extreme flood, malfunction of spillway or gate, and slope instability. Our study highlights that the integration of ML algorithms and DK is an effective approach for developing reliable BN models for dam risk analysis.

关键词： Dam risk analysis Earthen dam failure Bayesian network machine learning algorithm Domain knowledge Risk factor

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machine learning algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population

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JPAD-JOURNAL OF PREVENTION OF ALZHEIMERS DISEASE 2019年第3期6卷 185-191页

作者： Uspenskaya-Cadoz, O. Alamuri, C. Wang, L. Yang, M. Khinda, S. Nigmatullina, Y. Cao, T. Kayal, N. O'Keefe, M. Rubel, C. IQVIA Cent Nervous Syst Ctr Excellence Med Strate Therapeut Sci & Strategy Unit St Ouen France IQVIA Analyt Ctr Excellence Durham NC USA IQVIA Project Leadership 500 Brook DrGreen Pk Reading RG2 6UU Berks England

BACKGROUND: Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R&D pipeline includes around 112 agents. In the United States alone, 150 clinical trials are seeking 70,000 participants. Most people with early cognitive impairment consult primary care providers, who may lack time, diagnostic skills and awareness of local clinical trials. machine learning and predictive analytics offer promise to boost enrollment by predicting which patients have prodromal AD, and which will go on to develop AD. OBJECTIVES: The authors set out to develop a machine learning predictive model that identifies prodromal AD patients in the general population, to aid early AD detection by primary care physicians and timely referral to expert sites for biomarker confirmation of diagnosis and clinical trial enrollment. DESIGN: The authors use a classification machine learning algorithm to extract patterns within healthcare claims and prescription data three years prior to AD diagnosis/AD drug initiation. SETTING: The study focused on subjects included within proprietary IQVIA US data assets (claims and prescription databases). Patient information was extracted from January 2010 to July 2018, for cohorts aged between 50 and 85 years. PARTICIPANTS: A total of 88,298,289 subjects aged between 50 and 85 years were identified. For the positive cohort, 667,288 subjects were identified who had 24 months of medical history and at least one record with AD or AD treatment. For the negative cohort, 3,670,254 patients were selected who had a similar length of medical history and who were matched to positive cohort subjects based on the prevalence rate. The scoring cohort was selected based on availability of recent medical data of 2-5 years and included 72,670,283 subjects between the ages of 50 and 85 years. INTERVENTION (if any): None. MEASUREMENTS: A list of clinic

关键词： Alzheimer's disease prodromal AD machine learning algorithm AD clinical trial recruitment

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Hourly global solar forecasting models based on a supervised machine learning algorithm and time series principle

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INTERNATIONAL JOURNAL OF AMBIENT ENERGY 2020年第1期43卷 1707-1718页

作者： Belaid, Sabrina Mellit, Adel Boualit, Hamid Zaiani, Mohamed Ctr Dev Energies Renouvelables URAER Ghardaia Algeria Jijel Univ Renewable Energy Lab Jijel Algeria AS ICTP Trieste Italy

In this paper, predictive models of hourly global solar radiation (HGSR) at one-hour step ahead have been developed by adopting a new methodology. It consists of the association of a supervised machine learning algorithm named Support Vector machine (SVM) with time series principle. A dataset of HGSR, collected at Ghardaia city in the south of Algeria, has been used. In order to evaluate the accuracy of the developed models, we have used the Kolmogorov-Smirnov Integral (KSI) in addition to the conventional metrics (RMSE, NRMSE, NMBE, MAPE, and R). The results showed that accurate forecasts of HGSR at one-hour step ahead have been obtained by considering only its previous values at two-step forward. The predictive performances of the selected SVM model have been compared with those of some models available in the literature. This work proved the ability of the investigated machine learning algorithm with time series principle in predicting HGSR with good accuracy.

关键词： Solar radiation hourly forecasting time series machine learning algorithm persistent forecast new metrics

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Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2020年第1期20卷 276-276页

作者： Burdick, Hoyt Pino, Eduardo Gabel-Comeau, Denise Gu, Carol Roberts, Jonathan Le, Sidney Slote, Joseph Saber, Nicholas Pellegrini, Emily Green-Saxena, Abigail Hoffman, Jana Das, Ritankar Cabell Huntington Hosp Huntington WV USA Marshall Univ Sch Med Huntington WV USA Dascena Inc POB 156572 San Francisco CA 94115 USA

Background Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. Methods Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. Results 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001;DOR 4.358), SOFA (0.716;P < .001;DOR 3.720), and SIRS (0.655;P < .001;DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. Conclusions The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

关键词： machine learning algorithm Sepsis prediction Severe sepsis Diagnostic

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