荷载效应对油气工业混凝土机基础开采的影响

S. Demczynski, P. Ziółkowski, M. Niedostatkiewicz
{"title":"荷载效应对油气工业混凝土机基础开采的影响","authors":"S. Demczynski, P. Ziółkowski, M. Niedostatkiewicz","doi":"10.18178/ijscer.8.4.294-299","DOIUrl":null,"url":null,"abstract":"—Machine foundations is a critical topic in the gas and oil industry, which design and exploitation require extensive technical knowledge. Machine foundations are the constructions which are intended for mounting on it a specific type of machine. The foundation has to transfer dynamic and static load from machine to the ground. The primary difference between machine foundations and building foundations is that the machine foundations are a separate structure, even if they are inside the building. Failures of machine foundations can be very dangerous due to its carry loads from machines in operation. There is also an economic aspect because every break in the operation of industrial machines is expensive, especially in the gas and oil industry, where technological processes are complex and multi-stage. Repairs to concrete machine foundations are problematic, so the capability to predict what exactly affects failures seems extremely necessary. The failure of concrete machine foundations depends on many factors that are not fully understood. Modern achievements of science and technology, especially machine learning techniques may allow determining what affects the failure rate. This paper presents an analysis with the use of machine-learning techniques to predict in which way loads can affect the failure of foundations. This study examines whether and what relations exist between variables describing loads about the machine concrete failures occurrence. The analysis concerned some variables such as cross-section reinforcement amount, the grate load, measured concrete strength, motor short circuit moment load, the engine unit and rotor with shaft load, the pump unit and rotor with shaft load, the weight of the foundation, total load with foundation self-weight. The primary parameter of concern is the failure occurrence rate.","PeriodicalId":101411,"journal":{"name":"International journal of structural and civil engineering research","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Load Effect Impact on the Exploitation of Concrete Machine Foundations Used in the Gas and Oil Industry\",\"authors\":\"S. Demczynski, P. Ziółkowski, M. Niedostatkiewicz\",\"doi\":\"10.18178/ijscer.8.4.294-299\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"—Machine foundations is a critical topic in the gas and oil industry, which design and exploitation require extensive technical knowledge. Machine foundations are the constructions which are intended for mounting on it a specific type of machine. The foundation has to transfer dynamic and static load from machine to the ground. The primary difference between machine foundations and building foundations is that the machine foundations are a separate structure, even if they are inside the building. Failures of machine foundations can be very dangerous due to its carry loads from machines in operation. There is also an economic aspect because every break in the operation of industrial machines is expensive, especially in the gas and oil industry, where technological processes are complex and multi-stage. Repairs to concrete machine foundations are problematic, so the capability to predict what exactly affects failures seems extremely necessary. The failure of concrete machine foundations depends on many factors that are not fully understood. Modern achievements of science and technology, especially machine learning techniques may allow determining what affects the failure rate. This paper presents an analysis with the use of machine-learning techniques to predict in which way loads can affect the failure of foundations. This study examines whether and what relations exist between variables describing loads about the machine concrete failures occurrence. The analysis concerned some variables such as cross-section reinforcement amount, the grate load, measured concrete strength, motor short circuit moment load, the engine unit and rotor with shaft load, the pump unit and rotor with shaft load, the weight of the foundation, total load with foundation self-weight. The primary parameter of concern is the failure occurrence rate.\",\"PeriodicalId\":101411,\"journal\":{\"name\":\"International journal of structural and civil engineering research\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of structural and civil engineering research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18178/ijscer.8.4.294-299\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of structural and civil engineering research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18178/ijscer.8.4.294-299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

-机械基础是天然气和石油行业的一个重要课题,其设计和开发需要广泛的技术知识。机器基础是用于安装特定类型机器的结构。地基必须将机器的动、静荷载传递给地面。机器基础和建筑基础的主要区别在于,机器基础是一个单独的结构,即使它们在建筑物内部。机器基础在运行过程中承受着机器的载荷,因此发生故障是非常危险的。还有一个经济方面的问题,因为工业机器运行中的每次中断都是昂贵的,特别是在技术过程复杂且多阶段的天然气和石油行业。混凝土机器基础的维修是有问题的,因此预测到底是什么影响了故障的能力似乎是极其必要的。混凝土机器基础的破坏取决于许多尚未完全了解的因素。现代科学技术的成就,特别是机器学习技术可以确定影响故障率的因素。本文使用机器学习技术进行分析,以预测荷载影响基础破坏的方式。本研究探讨了机械混凝土故障发生中描述荷载的变量之间是否存在以及存在何种关系。分析涉及截面配筋量、炉排荷载、实测混凝土强度、电机短路力矩荷载、机组及转子轴载、泵机组及转子轴载、基础自重、总荷载及基础自重等变量。主要关注的参数是故障发生率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Load Effect Impact on the Exploitation of Concrete Machine Foundations Used in the Gas and Oil Industry
—Machine foundations is a critical topic in the gas and oil industry, which design and exploitation require extensive technical knowledge. Machine foundations are the constructions which are intended for mounting on it a specific type of machine. The foundation has to transfer dynamic and static load from machine to the ground. The primary difference between machine foundations and building foundations is that the machine foundations are a separate structure, even if they are inside the building. Failures of machine foundations can be very dangerous due to its carry loads from machines in operation. There is also an economic aspect because every break in the operation of industrial machines is expensive, especially in the gas and oil industry, where technological processes are complex and multi-stage. Repairs to concrete machine foundations are problematic, so the capability to predict what exactly affects failures seems extremely necessary. The failure of concrete machine foundations depends on many factors that are not fully understood. Modern achievements of science and technology, especially machine learning techniques may allow determining what affects the failure rate. This paper presents an analysis with the use of machine-learning techniques to predict in which way loads can affect the failure of foundations. This study examines whether and what relations exist between variables describing loads about the machine concrete failures occurrence. The analysis concerned some variables such as cross-section reinforcement amount, the grate load, measured concrete strength, motor short circuit moment load, the engine unit and rotor with shaft load, the pump unit and rotor with shaft load, the weight of the foundation, total load with foundation self-weight. The primary parameter of concern is the failure occurrence rate.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信