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Modeling the temperature gradient for a wide steel box girder based on monitoring data

STRUCTURE AND INFRASTRUCTURE ENGINEERING 2025年

作者： Hou, Ning Sun, Limin Chen, Lin Zhejiang Sci Tech Univ Sch Civil Engn & Architecture 928 Second Ave Hangzhou 310018 Zhejiang Provin Peoples R China Tongji Univ Sch Civil Engn Shanghai Peoples R China

This study examined the vertical and lateral temperature gradients in a wide steel box girder using one-year monitoring data from a cable-stayed bridge. Models for these temperature gradients were developed through data analysis, theoretical exploration, and finite-element simulations. The model for the vertical temperature gradient is represented by a triangle with a height of 500 mm, featuring a positive temperature difference of 27 degrees C and a negative temperature difference of -7 degrees C. In case of a steel box girder without flange plates, the lateral temperature gradient spans the entire width of the girder, with a temperature difference of 4 degrees C. According to the temperature gradient model developed in this research, the calculated stresses in the steel box girder exceeded the design values by 19 MPa and 12 MPa. The findings of this study are instrumental in providing a benchmark for further analysis of the monitoring data of the bridge and offer valuable insights for the design of similar structures in the future.

关键词： Finite element method lateral temperature gradient monitoring data steel box girder temperature statistical characteristics vertical temperature gradient without concrete deck

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Integrated STL-DBSCAN algorithm for online hydrological and water quality monitoring data cleaning

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ENVIRONMENTAL MODELLING & SOFTWARE 2025年 183卷

作者： Song, Chenyu Cui, Jingyuan Cui, Yafei Zhang, Sheng Wu, Chang Qin, Xiaoyan Wu, Qiaofeng Chi, Shanqing Yang, Mingqing Liu, Jia Chen, Ruihong Zhang, Haiping Tongji Univ Shanghai 200092 Peoples R China Zhejiang Univ Water Resources & Elect Power Inst Water Sci Hangzhou 310018 Peoples R China Acad Forens Sci Shanghai 200092 Peoples R China Fuzhou Urban & Rural Construction Grp Co Ltd Fuzhou 350007 Peoples R China Shanghai Invest Design & Res Inst Co Ltd Room 1101556 Haiyang West Rd Shanghai 200092 Peoples R China Three Gorges Smart Water Technol Co Ltd Room 1101556 Haiyang West Rd Shanghai 200092 Peoples R China

Online hydrological and water quality monitoring data has become increasingly crucial for water environment management such as assessment and modeling. However, online monitoring data often contains erroneous or incomplete datasets, consequently affecting its operational use. In the study, we developed an automated data cleaning algorithm grounded in Seasonal-Trend decomposition using Loess (STL) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). STL identifies and corrects more obvious anomalies in the time series, followed by DBSCAN for further refinement, in which the reverse nearest neighbor method was employed to enhance the clustering accuracy. To improve anomaly detection, a two-level residual judgment threshold was applied. The algorithm has been successfully applied to three reservoirs in Shanghai, China, achieving the precision rate of 0.91 and recall rate of 0.81 for dissolved oxygen and pH. The proposed algorithm can be potentially applied for cleaning of environment monitoring data with high accuracy and stability.

关键词： data cleaning STL DBSCAN Online hydrological and water quality monitoring data

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Understanding Microservice Runtime monitoring data for Anomaly Detection with Structural Equation Modeling 25th

Understanding Microservice Runtime Monitoring Data for Anoma...

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25th International Conference on Product Focused Software Process Improvement

作者： Steidl, Monika Leitner, Michael Urbanke, Pirmin Gattringer, Marko Felderer, Michael Ristov, Sashko Univ Innsbruck Innsbruck Austria Gepardec IT Serv GmbH Linz Austria Software Competence Ctr Hagenberg GmbH Hagenberg Austria German Aerosp Ctr DLR Inst Software Technol Cologne Germany Univ Cologne Cologne Germany

ISBN: (纸本)9783031783852;9783031783869

Microservices reliability is critical, but runtime anomalies are increasingly common due to system complexity. Rule-based and AI-based anomaly detection methods assist practitioners in analyzing runtime monitoring data (logs, traces, metrics) to identify anomalies. However, these methods rely on high-quality datasets and deep domain knowledge to deliver accurate results. Thus, a significant challenge lies in the lack of consensus on which runtime monitoring parameters effectively represent the system and microservices, reliably indicate anomalies, or distinguish deviations that genuinely signal anomalies. A thorough understanding of the dataset, key monitoring parameters, and microservice dependencies is crucial to minimize bias and false positives, ultimately improving the effectiveness of anomaly detection methods. Thus, we investigate whether structural equation modeling can describe the system's or microservices' behavior via indicators extracted from runtime monitoring data and identify their causal relationships. We used EvoMaster to simulate user behavior in TrainTicket and extract runtime monitoring data to test our model. Our results show that the identified indicators effectively describe microservices' behavior, but network indicators alone are insufficient for describing the whole system's behavior. The model can also identify microservices that significantly influence the whole system's behavior.

关键词： Anomaly detection during runtime monitoring data Logs Traces Metrics Microservice Structural equation model PLS-SEM

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Research Status of monitoring data Cleaning Technology in Substation monitoring System 19th

Research Status of Monitoring Data Cleaning Technology in Su...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zheng, Xiang Wu, Jiayi Zhang, Yikang Yu, Yiting Zheng, Suisheng Xue, Ancheng State Grid Quzhou Power Supply Company Zhejiang Quzhou324100 China State Grid Zhejiang Electric Power Co. Ltd Hangzhou310007 China Beijing102206 China Guodian Nanrui Nanjing Control System Co. Ltd Nanjing210000 China

ISBN: (纸本)9789819613823

The rapid development of intelligent substation makes the monitoring data of substation monitoring system constantly increase, and data anomalies occur frequently, which seriously affects the safe and stable operation of substation. Therefore, it is urgent to clean the monitoring data and improve the data quality. In view of this, the research status of data cleaning technology is summarized. Specifically, firstly, the substation monitoring system and its data problems are described. Then, the common data cleaning methods are summarized and compared from three aspects: correlation analysis, abnormal data detection and data repair. Finally, the problems encountered in the research are analyzed and the outlook is made. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： data Cleaning Technology monitoring data Substation monitoring System

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monitoring data Management Information System for Securities Market

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WIRELESS PERSONAL COMMUNICATIONS 2018年第1期103卷 319-326页

作者： Li, Yong China Univ Polit Sci & Law Sch Business 27 FuXue Rd Beijing 102249 Peoples R China

Long-term monitoring the key indicators of the securities market could detect market risk structure and risk behaviors in real time and ensure the safe and smooth operation of the securities system. In view of the large number monitoring indicators, high frequency sampling data acquisition in the online monitoring system, it is necessary to use advanced information management tools to efficiently manage and utilize the massive data, as the scale of securities market grows geometrically. Based on the online monitoring of BP neural network, this paper develops a management information system for the monitoring data of the securities market of China to realize the structure analysis and prediction of market performance and market risk structure and carry out automatically early warning and alarm with reference to pre-set threshold parameters, which provides a technical platform for real-time query, analysis statistics, prediction and alarm of market risk structures.

关键词： Securities market monitoring data Management information system

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Analyzing immediate settlement of Hong Kong-Zhuhai-Macao Bridge immersed tunnel based on monitoring data

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SHIPS AND OFFSHORE STRUCTURES 2021年第Sup2期16卷 100-109页

作者： Li, Bin Hou, Jinfang Min, Kaiyi Zhang, Jiaying CCCC First Harbor Engn Co Ltd Tianjin Port Engn Inst Co Ltd Tianjin Peoples R China Minist Commun Key Lab Port Geotech Engn Tianjin Peoples R China Key Lab Tianjin Port Geotech Engn Tianjin Peoples R China CCCC First Harbor Engn Co Ltd Tianjin Peoples R China Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg Xian Peoples R China

The monitored settlement data can reflect the safety condition of tunnel construction. The monitored settlement data, the experimental results and theoretical calculation outcomes of settlements of the immersed tunnel of Hong Kong-Zhuhai-Macao Bridge (HZMB) were compared and the contributing factors to the settlement and the underlying mechanism of settlement were discussed. The immediate settlement is the major form of settlement of the HZMB tunnel. The immediate settlement accounts for about 70%-80% of the total settlement of the tunnel, which is mainly caused by the gravel cushion and foundation soil. The settlement is 7.2-11.2 mm about the natural foundation section, and that of clay section is larger than sandy soil section. The immediate settlement can be calculated by the formula specified in the Foundation Code of Japan. The results are expected to provide a basis for the design and construction of immersed tunnels in the future.

关键词： Immersed tunnel Hong Kong-Zhuhai-Macao Bridge monitoring data immediate settlement rebound and recompression settlement

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An Initial Residual Stress Inference Method by Incorporating monitoring data and Mechanism Model

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Chinese Journal of Mechanical Engineering 2022年第5期35卷 47-65页

作者： Shuguo Wang Yingguang Li Changqing Liu Zhiwei Zhao Nanjing University of Aeronautics and Astronautics Nanjing 210016China

Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality *** inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation *** to the principle error of existing residual stress detection methods,there are still challenges in practical *** at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this *** data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism ***,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism *** results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

关键词： Initial residual stress Inference monitoring data Mechanism model Policy gradient

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Advanced Visualization and Accessibility to Heterogeneous monitoring data

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2014年第5期29卷 382-398页

作者： Glisic, Branko Yarnold, Matthew T. Moon, Franklin L. Aktan, A. Emin Princeton Univ Dept Civil & Environm Engn Princeton NJ 08544 USA Tennessee Technol Univ Dept Civil & Environm Engn Cookeville TN 38505 USA Drexel Univ Dept Civil Architectural & Environm Engn Philadelphia PA 19104 USA

Structural health monitoring (SHM) is a promising tool for better management of infrastructure. However, failure to manage large amounts of SHM data properly is a big barrier for its widespread application. data management is especially challenging when heterogeneous data are involved and combined with camera images. Various sensors based on different technologies can measure many parameters such as strain, tilt, weather, etc., whereas live cameras can visualize traffic response. In addition, all the data streams can be registered both statically and dynamically. data management is even more complex if multiple users access the data and have diverse backgrounds and interests (e.g., the owner/manager of the structure, operator, responsible engineer, and academic). In this article, general principles for SHM data management are researched, established, and proposed by the authors, and an original solution for data management based on these principles is presented. For validation purposes, the proposed principles were implemented in novel data management software and applied to a signature bridge. Feedback from interested groups including the managers, operators, engineers of record, and academics is used for validation. Thus, this article details the researched and proposed accessibility and visualization principles, and conceptual development of novel solution for data management, which combines computer science, internet omnipresence, and structural engineering.

关键词： Simple harmonic motion Interest groups monitoring data STRUCTURE ENGINEERING Accessibility data management Heterogeneous

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monitoring data Reduction in data Centers: A Correlation-Based Approach 5th

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5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS) / 2nd International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Peng, Xuesong Pernici, Barbara Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza Leonardo Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9783319637129;9783319637112

monitoring data are collected and stored in a wide range of domains, especially in data centers, which integrate myriads of services and massive data. To handle the inevitable challenges brought by increasing volume of monitoring data, this paper proposes a correlation-based reduction method for streaming data that derives quantitative formulas between correlated indicators, and reduces the sampling rate of some indicators by replacing them with formulas predictions. This approach also revises formulas through iterations of the reduction process to find an adaptive solution in dynamic environments of data centers. One highlight of this work is the ability to work on upstream side, i.e., it can reduce volume requirements for data collection of monitoring systems. This work also tests the approach with both simulated and real data, showing that our approach is capable of data reduction in complex data centers.

关键词： monitoring data data reduction Time-series prediction data center

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Load Loss Rate Evaluation Method of Sensitive Users Under Voltage Sag Based on monitoring data

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IEEE TRANSACTIONS ON POWER DELIVERY 2022年第6期37卷 5156-5168页

作者： Wang, Ying Yang, Yixuan Hu, Wenxi Xiao, Xianyong Ma, Xiaoyang Yang, Hang Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China

Voltage sag can result in extensive losses of sensitive users. However, such losses have yet to be quantified based on power quality monitoring data. The load loss rate (LLR) is a practical index for evaluating the consequences of voltage sag, providing vital information for mitigation decisions. This study proposes a data-driven LLR evaluation method of sensitive users under voltage sag, which can be used as a basis for configuring mitigation devices. First, the LLR calculation index is proposed, based on the sensitive load response characteristics and the active power trajectory under voltage sags. Second, a feature indices system is established for the active power trajectory, based on the basic attributes and load response characteristics of sensitive load, to characterize the change of active power under voltage sag. Furthermore, a voltage sag data augmentation model is constructed to generate sample data, considering the characteristic calculation model of voltage sag and the probability density distribution of characteristics. Finally, LLR evaluation model is proposed, based on random forest regression for sensitive users under voltage sags. The effectiveness and accuracy of the proposed method are verified through a case study.

关键词： Voltage sag monitoring data load loss rate feature indices system data augmentation

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