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Toward ecological environmental risk for spoil ground group management in mega projects

ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第9期31卷 3706-3726页

作者： Li, Yulong Yao, Ziwen Wu, Jing Zeng, Saixing Wu, Guobin Cent Univ Finance & Econ Sch Management Sci & Engn Beijing Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China

PurposeThe numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental ***/methodology/approachBased on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and k-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed *** results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive ***/valueThis paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

关键词： Spoil grounds Ecological environment Risk assessment Categorization Projection pursuit clustering method k-means clustering algorithm

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kBMP: kubernetes-Orchestrated IoT Online Battery Monitoring Platform

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IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 25358-25370页

作者： Chen, Qingyang He, Yinghui Yu, Guanding Xu, Chong Liu, Mingyang Li, Zhenming Zhejiang Univ Coll Informat & Elect Engn Hangzhou 310027 Zhejiang Peoples R China China Elect Power Res Inst Co Ltd Energy Storage & Novel Technol Elect Engn Dept Beijing 100192 Peoples R China

The rise in renewable energy has driven the widespread use of large-scale energy storage batteries, which makes the risk of overheating more threatening. To ensure battery safety, it is essential to build a monitoring system with a comprehensive evaluation of large quantities of batteries. However, existing battery management systems exhibit significant limitations in terms of monitoring scope, analytical precision, and transmission efficiency. As an applicable solution, cloud-edge technology is an advanced integrated method that provides low-latency data access, accurate analysis capabilities, and adjustable monitoring ranges. In this work, the kubernetes-orchestrated battery monitoring platform (kBMP), which integrates kubernetes and cloud-edge technology, is proposed to provide comprehensive battery management. Specifically, kubernetes is used to ensure low latency in data transmission and analysis, while the k-means clustering algorithm is applied to provide accurate thermal runaway (TR) warnings. To validate the performance of kBMP, four sets of real battery TR data are fed to test its accuracy and latency. The experimental findings reveal that kBMP is capable of providing battery TR warnings in advance within 30 min. Additionally, the platform concurrently decreases data transmission latency by up to 20% and reduces replica scaling latency by 50% compared to the platform without integrating kubernetes.

关键词： Batteries Monitoring Cloud computing Data visualization Servers Data analysis Data communication Battery monitoring platform cloud-edge technology k-means clustering algorithm kubernetes thermal runaway (TR) prediction

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Integrity-Management Characteristics and Efficiency Evaluation of Oil and Gas Pipelines

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China Petroleum Processing & Petrochemical Technology 2023年第4期25卷 139-150页

作者： Zhang Xiaodong Sun Jiazheng Fu Yong Lei Shaojuan School of Economics and Management University of Science and Technology BeijingBeijing 100083China PetroChina Planning and Engineering Institute Beijing 100083 China Nuclear and Radiation Safety Center Beijing 102444China

Pipeline integrity is a cornerstone of the operation of many industrial systems, and maintaining pipeline integrity is essential for preventing economic losses and ecological damage caused by oil and gas leaks. Based on integritymanagement data published by the US Pipeline and Hazardous Materials Safety Administration, this study applied the k-means clustering and data envelopment analysis(DEA) methods to both explore the characteristics of pipeline-integrity management and evaluate its efficiency. The k-means clustering algorithm was found to be scientifically valid for classifying pipeline companies as either low-, medium-, or high-difficulty companies according to their integrity-management requirements. Regardless of a pipeline company's classification, equipment failure was found to be the main cause of pipeline failure. In-line inspection corrosion and dent tools were the two most-used tools for pipeline inspection. Among the types of repair, 180-day condition repairs were a key concern for pipeline companies. The results of the DEA analysis indicate that only three out of 34 companies were deemed to be DEA-effective. To improve the effectiveness of pipeline integrity management, we propose targeted directions and scales of improvement for non-DEA-effective companies.

关键词： Integrity management k-means clustering algorithm data envelopment analysis safety management

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Portable measurement system for the objective evaluation of the spasticity of hemiplegic patients based on the tonic stretch reflex threshold

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MEDICAL ENGINEERING & PHYSICS 2011年第1期33卷 62-69页

作者： kim, keo Sik Seo, Jeong Hwan Song, Chul Gyu Chonbuk Natl Univ Sch Elect & Informat Engn Ctr Adv Image & Informat Technol Jeonju 561756 Jeonbuk South Korea Chonbuk Natl Univ Dept Rehabil Med Sch Med Jeonju 561712 Jeonbuk South Korea

The clinical scales used for the evaluation of the spasticity have some drawbacks, in spite of their simplicity and ease of assessment, and their inter- and intra-rater reliability remains controversial. The aim of this study is to develop a portable system for the objective and reliable evaluation of the spasticity based on the k-means clustering of the tonic stretch reflex threshold (TSRT) and to compare the discrimination performance of the level of spasticity determined by our method with that by the conventional modified Ashworth scale (MAS). Fifteen hemiplegic patients (7 males and 8 females, age: 63.5 +/- 15.6) participated in the study. The average and standard deviation values of the TSRTs were 127.9 +/- 1.6, 121.8 +/- 1.5 and 117.9 +/- 1.3 in groups G1, G2 and G3, respectively, and there were significant differences between the TSRTs of each group (p<0.05). Also, our groups classified by the criteria of the TSRT had a strong negative correlation (r=-0.95, r(2) = 0.90, p<0.05) between the level of spasticity and TSRTs. These results demonstrated that our system could be clinically more useful for the quantitative and reliable discrimination of the spasticity than the conventional MAS. Crown Copyright (C) 2010 Published by Elsevier Ltd on behalf of IPEM. All rights reserved.

关键词： k-means clustering algorithm Portable system Spasticity Tonic stretch reflex threshold

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Production planning and scheduling problem of continuous parallel lines with demand uncertain and different production capacities

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2020年第6期7卷 761-774页

作者： Bhosale, kailash Changdeorao Pawar, Padmakar Jagannath Nasik Savitribai Phule Pune Univ KK Wagh Inst Engn Educ & Res Dept Prod Engn Pune Maharashtra India

Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of (sic)2 23 324 for weeks 1-4 in overall cost compared with the results obtained by using CPLEX software.

关键词： Integrated production planning and scheduling problem discrete artificial bee colony (DABC) k-means clustering algorithm Monte Carlo simulation

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Location of the critical slip surface based on monitoring data and genetic algorithm

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PHYSICA SCRIPTA 2024年第6期99卷 066009-066009页

作者： Fan, Zhiyong Lu, Xiaobing Zhao, Ying Liu, Tianping Liu, Xiaoyu Inst Mech Chinese Acad Sci Key Lab Mech Fluid Solid Coupling Syst Beijing 100190 Peoples R China

The non-circular search limit equilibrium method (LEM) is a significant methodology in the analysis of slope stability, aimed at identifying the critical slip surface (CSS) and the safety factor (SF). However, the existing LEM does not consider monitoring data, such as the subsurface displacement, in the CSS search process for the actual slopes. In this paper, a new approach is proposed that combines LEM with subsurface displacement data to more accurately identify the CSS. The k-means clustering algorithm is utilized to determine the recognizing slip positions corresponding to each subsurface displacement curve. Additionally, the Spencer's method is employed to calculate the SF. By treating the recognizing slip positions as constraint conditions, the genetic algorithm (GA) is applied to search for the location of the CSS. To validate the accuracy and usefulness of the proposed method, four numerical examples and an experimental case are presented.

关键词： slope stability non-circular search critical slip surface subsurface displacement k-means clustering algorithm genetic algorithm

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Combined relevance vector machine technique and subset simulation importance sampling for structural reliability

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APPLIED MATHEMATICAL MODELLING 2023年 113卷 129-143页

作者： Xie, Bin Peng, Chong Wang, Yanzhong Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China

Evaluating the failure probability of engineering structures involving implicit performance functions generally requires time-consuming finite element simulations. The computational effort is usually unbearable, especially for small failure probability. This paper proposes a new reliability analysis method combining relevance vector machine and subset simu-lation importance sampling, which guarantees the accuracy of results and improves the computational efficiency. In this method, relevance vector machine is first applied to ap-proximate relatively coarse limit states. Then, subset simulation importance sampling is executed based on the constructed relevance vector machine. To improve the prediction accuracy, samples from first and last level are used to refine relevance vector machine. Since only the samples of last level are involved in the calculation of the failure probabil-ity, and the updated relevance vector machine has a very high prediction accuracy for the samples of last level, the obtained failure probability is extremely accurate. In addition, the samples are predicted by the well-constructed relevance vector machine rather than eval-uated with real performance functions, which drastically decreases the calculation time. To reduce redundant iterations in the update process, a novel learning function based on the current design of experiment position and a stopping condition based on error estimation of reliability prediction are employed. Furthermore, the k-means clustering algorithm is utilized to implement the ability of surrogate model-based subset simulation importance sampling to handle multiple failure mode problems. The efficiency and accuracy of the proposed method are verified by four examples involving different types of features.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Reliability analysis Relevance vector machine Subset simulation importance sampling k-means clustering algorithm Small failure probability

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Data and analytics for heating energy consumption of residential buildings: The case of a severe cold climate region of China

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ENERGY AND BUILDINGS 2018年第Aug.期172卷 104-115页

作者： Chang, Chen Zhu, Neng Yang, kun Yang, Fan Tianjin Univ Sch Environm Sci & Engn Key Lab Efficient Utilizat Low & Medium Grade Ene MOE Tianjin 300072 Peoples R China

Building energy conservation has been a globally important issue not only because of the greenhouse gas emissions that occur during the construction and operation of a building, but also due to the increasing emphasis on indoor thermal quality, which is directly related to the quality of life, especially in northern China. The behaviors of heating companies and building users are considered to play important roles in the reduction of building energy consumption. However, it is necessary to understand the relationship between the heating fee, which represents the heating policy, and the energy performance, which considers both the actual energy consumption and thermal ability of a building. With this goal in sight, this study focuses on residential buildings in a climate zone that experiences quite low temperatures, and the heating energy consumption of 40 residential buildings from five typical regions in Inner Mongolia were monitored and analyzed. The thermal comfort, occupant behavior, and heating energy consumption of typical households and buildings in these regions were quantified. In view of the monitored results, building models were built and validated, with the results treated as training samples in clustering and classification procedures. For the sake of the further formulation of a heating energy cost allocation policy, the k-means clustering algorithm and discriminant analysis (DA) were used in this work to evaluate and group residential buildings according to their heating energy consumption and thermal-physical properties of the envelopes The results of this study indicated the existence of regional heating energy consumption differences. Moreover, the main influential factors for grouping residential buildings and the number of clusters were suggested based on the k-means clustering algorithm and DA method. Meanwhile, the classification accuracy rate obtained from the DA method not only verified the clustering results, but also confirmed the further

关键词： Severe cold climate zone of China Residential buildings Heating energy consumption k-means clustering algorithm Discriminant analysis

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A categorization tool for fabric systems used in firefighters' clothing based on their thermal protective and thermo-physiological comfort performances

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TEXTILE RESEARCH JOURNAL 2019年第16期89卷 3244-3259页

作者： Mandal, Sumit Annaheim, Simon Capt, Andre Greve, Jemma Camenzind, Martin Rossi, Rene M. Empa Swiss Fed Labs Mat Sci & Technol Lab Biomimet Membranes & Text St Gallen Switzerland DuPont Int Operat Sarl Le Grand Saconnex Switzerland

Fabric systems used in firefighters' thermal protective clothing should offer optimal thermal protective and thermo-physiological comfort performances. However, fabric systems that have very high thermal protective performance have very low thermo-physiological comfort performance. As these performances are inversely related, a categorization tool based on these two performances can help to find the best balance between them. Thus, this study is aimed at developing a tool for categorizing fabric systems used in protective clothing. For this, a set of commercially available fabric systems were evaluated and categorized. The thermal protective and thermo-physiological comfort performances were measured by standard tests and indexed into a normalized scale between 0 (low performance) and 1 (high performance). The indices dataset was first divided into three clusters by using the k-means algorithm. Here, each cluster had a centroid representing a typical Thermal Protective Performance Index (TPPI) value and a typical Thermo-physiological Comfort Performance Index (TCPI) value. By using the ISO 11612:2015 and EN 469:2014 guidelines related to the TPPI requirements, the clustered fabric systems were divided into two groups: Group 1 (high thermal protective performance-based fabric systems) and Group 2 (low thermal protective performance-based fabric systems). The fabric systems in each of these TPPI groups were further categorized based on the typical TCPI values obtained from the k-means clustering algorithm. In this study, these categorized fabric systems showed either high or low thermal protective performance with low, medium, or high thermo-physiological comfort performance. Finally, a tool for using these categorized fabric systems was prepared and presented graphically. The allocations of the fabric systems within the categorization tool have been verified based on their properties (e.g., thermal resistance, weight, evaporative resistance) and construction paramete

关键词： fabric systems firefighters' clothing thermal protective performance thermo-physiological comfort performance k-means clustering algorithm fabric categorization tool

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Railway accidents analysis based on the improved algorithm of the maximal information coefficient

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INTELLIGENT DATA ANALYSIS 2016年第3期20卷 597-613页

作者： Shao, Fubo Li, keping Xu, Xiaoming Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Beijing Jiaotong Univ Beijing Lab Urban Rail Transit Beijing Peoples R China

The maximal information coefficient (MIC), a measure of dependence for two-variable relationships, can be used to discover the relationships between two variables in big data. This paper proposes a new mathematical program model for calculating the value of MIC. A corresponding efficient algorithm is designed to solve the model in big data environment. In order to illustrate the validity of the proposed algorithm, the proposed algorithm is applied into the analysis of railway accidents data. Experimental results show that the proposed algorithm could find important relationships between two variables from big data. And some factors influencing accidents are identified from many factors. In addition, compared with the algorithm proposed by Reshef et al. in 2011, the proposed algorithm has lower time complexity and needs less computation time. Hence the proposed algorithm is more suitable for big data environment.

关键词： Maximal information coefficient k-means clustering algorithm big data railway accidents analysis

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