Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering最新文献_第4页

Influence of corrosion rate on rust penetration in concrete 腐蚀速率对混凝土锈蚀渗透的影响

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03320

Shuxian HONG, Fan ZHENG, Feng XING, Biqin DONG

{"title":"Influence of corrosion rate on rust penetration in concrete","authors":"Shuxian HONG, Fan ZHENG, Feng XING, Biqin DONG","doi":"10.3724/sp.j.1249.2023.03320","DOIUrl":"https://doi.org/10.3724/sp.j.1249.2023.03320","url":null,"abstract":"HONG Shuxian, ZHENG Fan, XING Feng, and DONG Biqin College of Civil and Transportation Engineering, Guangdong Province Key Laboratory of Durability for Marine Civil Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P. R. China Abstract: Rust penetration in pore structure of concrete cover happens after the reinforcement corrosion, which impedes the cracking development. To improve the accuracy of deducing the corrosion level of reinforcement from the crack width, reinforcements are designed to be corroded with different corrosion rates using different impressed current densities. Based on an inverse modeling method that consists of a combination of X-ray micro-computed tomography and the finite element method, the development of the modification coefficient of the rust volume is calculated to quantify the rust penetration and the influence of the corrosion rates. The results indicate that as the corrosion rate increases, the rust penetration decreases, and the modification coefficient of the rust volume increases. As the corrosion process develops, a decrease in the modification coefficient of the rust volume is found, which is independent of the corrosion rate. The modification coefficient of the rust volume decreases exponentially with the increase of corrosion rate. This study could provide a basis for the evaluation of reinforcement corrosion of reinforced concrete structures.","PeriodicalId":35396,"journal":{"name":"Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135517074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of big data technology in financing costs of enterprises 大数据技术在企业融资成本中的应用

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03284

Dexin CHE, Baoer LI, Hailing XIANG, Fei WU

引用次数: 0

Intelligent lithologic identification of sandy conglomerate reservoirs in District No.7 of Karamay oilfield 克拉玛依油田七区砂砾岩储层智能岩性识别

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03361

Ji LU, Botao LIN, Can SHI, Jiahao ZHANG

{"title":"Intelligent lithologic identification of sandy conglomerate reservoirs in District No.7 of Karamay oilfield","authors":"Ji LU, Botao LIN, Can SHI, Jiahao ZHANG","doi":"10.3724/sp.j.1249.2023.03361","DOIUrl":"https://doi.org/10.3724/sp.j.1249.2023.03361","url":null,"abstract":"LU Ji 1, , LIN Botao 1, , SHI Can 1, , and ZHANG Jiahao 1, 2 1) College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, P. R. China 2) College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, P. R. China Abstract: The sandy conglomerate reservoirs in Karamay are characterized by diverse lithology and interlayers. The cost of the conventional coring methods is high, and the identification accuracy in non-coring section is low, which leads to difficulty in reservoir classification. In order to achieve rapid and accurate identification of lithology, the lithology of the target area is classified into mudstone, coarse sandstone, medium-fine sandstone, coarse conglomerate, medium-fine conglomerate, and coal seam based on geological data. Firstly, the principal component analysis method is adopted to establish the lithology identification cross plot based on sensitivity analysis of well log data with an accuracy rate of 81. 37%. Secondly, a lithology identification model is proposed based on the combination of k-means synthetic minority oversampling technique (KMSMOTE) and random forest to improve minority identification accuracy. The model improves the identification accuracy to achieve the accuracy of about 92. 94% for oversampling minority samples. The two methods are applied to adjacent wells for comparative analysis of lithology identification. The accuracy of the KMSMOTE random forest is 95. 71%, better than that of the cross plot method of 82. 91%. The accuracy of minority sample identification is higher than that of the traditional random forest","PeriodicalId":35396,"journal":{"name":"Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135517071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The impact of digital economy on urban-rural income gap under government intervention 政府干预下数字经济对城乡收入差距的影响

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03296

Yang LE, Dongxi GAO, Yi ZHANG, Wei GUO, Yi CHEN

引用次数: 0

Effect of low impact development retrofitting in Shenzhen Xincheng Park 深圳新城公园低影响发展改造效果

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03370

Kun TIAN, Yongwei GONG, Shijie CHEN, Xinxin REN, Junqi LI, Wenliang WANG

引用次数: 0

Analysis of bus-stop operating state based on GPS data 基于GPS数据的公交站点运行状态分析

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03326

Hongtao HUANG, Mei XIAO, Qian LIU, Xiuling MING, Haoyi BIAN

{"title":"Analysis of bus-stop operating state based on GPS data","authors":"Hongtao HUANG, Mei XIAO, Qian LIU, Xiuling MING, Haoyi BIAN","doi":"10.3724/sp.j.1249.2023.03326","DOIUrl":"https://doi.org/10.3724/sp.j.1249.2023.03326","url":null,"abstract":"中国西安市公交车全球定位系统轨迹数据为例,建立平均服务时间和服务车数特征参数反映公交站的运行状态,并通过分析站点内公交车辆速度、里程及加速度之间关系计算站台服务时间.使用Hopkins统计量和轮廓系数分析可聚性和聚类数,结合高斯混合模型(Gaussian mixture model, GMM)对公交站运行状态进行识别分类.构建 SMOTEENN-XGBoost(synthetic minority oversampling technique edited nearest neighbours extreme gradient boosting)站点运行状态预测模型 , 引入可解释机器学习框架 SHAP(Shapley additive explanation)分析站台属性、道路及环境对模型的影响.结果表明,公交站运行状态可分为3类,类型I的平均服务时间最长,类型II的平均服务时间和服务车数最少,类型III的服务车数最多;所建立 SMOTEENN-XGBoost 模型的准确率为 94. 68%,精确率为 94. 69%,召回率为 91. 04%,F1分数为 92. 26%, 与极限梯度提升(extreme gradient boosting, XGBoost)、逻辑回归(logistic regression, LR)、随机森林 (random forest, RF)、梯度提升决策树(gradient boosting decision tree, GBDT)和 k 近邻(k-nearest neighbors, KNN)5种模型对比,本模型能够精准预测站点运行状态;对站点运行状态具有影响作用的因素按照重要程","PeriodicalId":35396,"journal":{"name":"Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135517082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of formation pressure in underground gas storage based on data-driven method 基于数据驱动方法的地下储气库地层压力预测

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03353

Gulei SUI, Yujiang FU, Hongxiang ZHU, Zunzhao LI, Xiaolin WANG

{"title":"Prediction of formation pressure in underground gas storage based on data-driven method","authors":"Gulei SUI, Yujiang FU, Hongxiang ZHU, Zunzhao LI, Xiaolin WANG","doi":"10.3724/sp.j.1249.2023.03353","DOIUrl":"https://doi.org/10.3724/sp.j.1249.2023.03353","url":null,"abstract":"SUI Gulei 1, , FU Yujiang 1, , ZHU Hongxiang 1, , LI Zunzhao , and WANG Xiaolin 1, 2 1) SINOPEC Dalian Research Institute of Petroleum & Petrochemicals Co. Ltd. , Dalian 116045, Liaoning Province, P. R. China 2) Artificial Intelligence Innovation Center, SINOPEC, Dalian 116045, Liaoning Province, P. R. China Abstract: Formation pressure is a significant parameter for establishing the working system of injector-producer well and monitoring the operation of underground gas storage (UGS). In view of the complex geological modeling and highquality historical fitting involved in numerical simulation to understand the change of formation pressure, a datadriven method for forecasting formation pressure of UGS is proposed. The optimal warping path is weighted by the proportion of gas injection-production to screen pressure monitoring wells. The supervised learning model of forma⁃ tion pressure forecasting is established by three kinds of machine learning algorithms including extreme gradient boosting (XGBoost), support vector regression (SVR), and long short-term memory network (LSTM). The experimen⁃ tal results show that predictive performances of three predictive models are ranked from high to low: SVR, XGBoost, LSTM, among which the predictive performance of SVR is the most stable. Introducing the proportion of gas injection-production to screen pressure monitoring wells can improve the predictive performance of the data-driven model. Research shows that the purely data-driven method can directly interpret routine surface measurements to intuitive subsurface pressure parameters of UGS, which is very suitable for the field application of UGS.","PeriodicalId":35396,"journal":{"name":"Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135517070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial of special issue on artificial intelligence and digital economy 《人工智能与数字经济》特刊社论

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03253

Fei YU, Yulin HE, Ying HE

{"title":"Editorial of special issue on artificial intelligence and digital economy","authors":"Fei YU, Yulin HE, Ying HE","doi":"10.3724/sp.j.1249.2023.03253","DOIUrl":"https://doi.org/10.3724/sp.j.1249.2023.03253","url":null,"abstract":"在人工智能产业化浪潮席卷全球的今天,越来越多的企业开始采用人工智能解决方案来替代传统的人力资源工作,据中国国际发展知识中心 2022 年发布的《全球发展报告》预测,2020—2025 年全球约 8 500万个工作岗位将被机器替代.该趋势不仅会带来生产力和生产效率的提高,还会对整个社会的变革带来深刻影响.从 2012 年“谷歌猫” 的轰动全球到 2016 年 AlphaGo 的横空出世,从 2017 年世界首位被授予公民身份的机器人,再到如今火爆的语言模型 ChatGPT(chat generative pretrained transformer),每一次人工智能技术的革新必然会促进产业的转型与升级,对经济发展和社会进步带来巨大影响. 人工智能已成为国家综合实力的一部分,人工智能技术越发达,国家在世界就越有竞争力.中国共产党第十八次全国代表大会以来,面对新一轮科技革命和产业变革形势,党中央和国务院高瞻远瞩、审时度势,制定和实施了人工智能发展国家战略,从国家层面对人工智能发展进行了统筹规划和顶层设计. 2017-07-08,国务院印发《新一代人工智能发展规划》(以下简称“人工智能规划”),指出人工智能发展进入新阶段,将成为国际竞争的新焦点和经济发展的新引擎,并带来社会建设的新机遇,进而提出到 2020年人工智能产业成为新的重要经济增长点,到 2025年人工智能成为带动国家产业升级和经济转型的主要动力,到 2030年智能经济、智能社会取得明显成效,跻身创新型国家前列和经济强国的3步走战略目标.该规划明确了发展人工智能的重点任务之一就是培育高端且高效的智能经济. “人工智能规划”对人工智能的发展方向做出了明确指引,即以人工智能为代表的新一代信息技术,将成为中国“十四五”期间推动经济高质量发展,实现新型工业化、信息化、城镇化和农业现代化的重要技术保障和核心驱动力之一.归根结底, 人工智能要为中国经济的高质量发展服务,尤其是要成为数字经济发展的重要驱动引擎.","PeriodicalId":35396,"journal":{"name":"Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135517073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solidification behavior of Fe3O4 coated multi-wall carbon nanotube cool storage medium Fe3 0 4包覆多壁碳纳米管蓄冷介质

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03313

Meibo XING, Yuchen WANG, Dongliang JING, Hongfa ZHANG, Ruixiang WANG

引用次数: 0

Research and performance evaluation of channeling-plugging agent under high temperature 高温下堵漏剂的研究与性能评价

Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering Pub Date : 2023-05-01 DOI: 10.3724/sp.j.1249.2023.03344

Hongjie YUAN, Long HE, Zijia LI, Mingkai LI, Xueli HUANG, Wen ZHANG

引用次数: 0