|本期目录/Table of Contents|

[1]李慧民,段品生,孟海,等.基于IPSO-BP的受损钢结构改造施工安全预警评估研究[J].中国安全生产科学技术,2019,15(8):174-180.[doi:10.11731/j.issn.1673-193x.2019.08.028]
 LI Huimin,DUAN Pinsheng,MENG Hai,et al.Study on safety earlywarning assessment of damaged steel structure reconstruction based on IPSO-BP[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(8):174-180.[doi:10.11731/j.issn.1673-193x.2019.08.028]
点击复制

基于IPSO-BP的受损钢结构改造施工安全预警评估研究
分享到:

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
15
期数:
2019年8期
页码:
174-180
栏目:
职业安全卫生管理与技术
出版日期:
2019-08-31

文章信息/Info

Title:
Study on safety earlywarning assessment of damaged steel structure reconstruction based on IPSO-BP
文章编号:
1673-193X(2019)-08-0174-07
作者:
李慧民1段品生1孟海12郭海东1
(1.西安建筑科技大学 土木工程学院,陕西 西安 710055;
2.中冶建筑研究总院有限公司,北京 100088)
Author(s):
LI Huimin1DUAN Pinsheng1MENG Hai12GUO Haidong1
(1.School of Civil Engineering,Xi’an University of Architecture and Technology,Xi’an Shaanxi 710055,China;
2.Central Research Institute of Building and Construction Co.,Ltd.,MCC,Beijing 100088,China)
关键词:
安全控制预警评估受损钢结构IPSO-BP神经网络改造施工
Keywords:
safety control earlywarning assessment damaged steel structure IPSO-BP neural network reconstruction
分类号:
X947
DOI:
10.11731/j.issn.1673-193x.2019.08.028
文献标志码:
A
摘要:
为研究受损钢结构改造施工安全预警状况,建立受损情况下钢结构改造施工安全预警指标体系,并针对BP神经网络算法易陷入局部最优的问题,提出了用改进粒子群算法(IPSO)对BP神经网络权重及阈值进行调整的IPSO-BP安全预警评估模型。通过分析某单层重钢厂房受损现状,针对其结构损伤情况和已构建的安全控制指标体系进行数值模拟分析。研究结果表明:与传统的BP模型相比,IPSO-BP模型具有更好的预测能力,构建的安全预警指标体系及预警模型可以很好地对受损钢结构改造施工过程安全状况进行综合评估,对受损钢结构改造施工安全控制具有一定的参考价值。
Abstract:
In order to study the safety earlywarning status of damaged steel structure reconstruction,a safety earlywarning index system of damaged steel structure reconstruction was established.Aiming at the problem that the BP neural network algorithm is easy to fall into the local optimum,an IPSO-BP safety earlywarning assessment model was put forward,which adopted the improved particle swarm optimization algorithm (IPSO) to adjust the weights and thresholds of BP neural network.By analyzing the damage situation of a singlelayer heavy steel plant,the numerical simulation analysis was carried out based on its structural damage conditions and the built safety control index system.The results showed that the IPSO-BP model had better predictive ability compared with the traditional BP model,and the safety earlywarning index system and earlywarning model could assess the safety status of the damaged steel structure reconstruction process comprehensively.It has a certain reference value for the safety control of damaged steel structure reconstruction.

参考文献/References:

[1]江见鲸,王元清,龚晓南,等.建筑工程事故分析与处理[M].第三版.北京:中国建筑工业出版社,2006.
[2]住房和城乡建设部.全国房屋市政工程生产安全事故信息报送及统计分析系统[EB/OL].[2019-05-16].http://sgxxxt.mohurd.gov.cn/Public/AccidentList.aspx..
[3]丁丽萍.钢结构厂房托梁拔柱改造技术与安全管理研究[D].武汉:华中科技大学,2011.
[4]熊仲明,冯成帅.大跨钢结构安全性模糊综合评估方法的应用研究[J].工程力学,2011,28(4):128-133.XIONG Zhongming,FENG Chengshuai.Application study on the method for fuzzy comprehensive evaluation of large-span steel structure safety [J].Engineering Mechanics,2011,28(4):128-133.
[5]罗永峰,叶智武,陈晓明,等.空间钢结构施工过程监测关键参数及测点布置研究[J].建筑结构学报,2014,35(11):108-115.LUO Yongfeng,YE Zhiwu,CHEN Xiaoming,et al.Research on key parameter selection and monitoring point arrangement in construction monitoring of spatial steel structures [J].Journal of Building Structures,2014,35(11):108-115.
[6]咸庆军,张猛,李天.基于神经网络的受损钢结构变电构架承载能力预测[J].钢结构,2013,28(3):9-14.XIAN Qingjun,ZHANG Meng,LI Tian.Bearing capacity forecast of damaged steel structural gantry of substation based on neural network [J].Steel Construction,2013,28(3): 9-14.
[7]罗尧治,苑佳谦.大跨度空间结构安全预警评估技术研究[J].空间结构,2011,17(3):61-68.LUO Yaozhi,YUAN Jiaqian.Safety assessment and early warning technology in long-spanspatial structures[J].Spatial Structures,2011,17(3): 61-68.
[8]XU L,ZHANG T,REN Q.Intelligent autofeedback and safety early-warning for underground cavern engineering during construction based on BP neural network and FEM[J].Mathematical Problems in Engineering,2015.
[9]LEE K P,LEE H S,PARK M,et al.A real-time location-based construction labor safety management system[J].Journal of Civil Engineering and Management,2014,20(5): 724-736.
[10]管志川,胜亚楠,许玉强,等.基于PSO优化BP神经网络的钻井动态风险评估方法[J].中国安全生产科学技术,2017,13(8):5-11.GUAN Zhichuan,SHENG Yanan,XU Yuqiang,et al.Dynamic risk assessment method of drilling based on PSO optimized BP neural network [J].Journal of Safety Science and Technology,2017,13(8):5-11.
[11]姜长元,赵曙光,沈士根,等.惯性权重正弦调整的粒子群算法[J].计算机工程与应用,2012,48(8):40-42.JIANG Changyuan,ZHAO Shuguang,SHEN Shigen,et al.Particle swarm optimization algorithm with sinusoidal changing inertiaweight [J].Computer Engineering and Applications,2012,48(8): 40-42.
[12]BASU,MOUSUMI.Improved particle swarm optimization for global optimization of unimodal and multimodal functions[J].Journal of The Institution of Engineers (India): Series B,2016,97(4): 525-535.
[13]许如锋,陈建国,赵作周,等.基于应变和PSO-BP神经网络的连续梁桥损伤识别方法[J].安全与环境学报,2017,17(6):2093-2098.XU Rufeng,CHEN Jianguo,ZHAO Zuozhou,et al.Damage identification and discrimination method for the continuous bridges based on the strain and PSO-BP neural network [J].Journal of Safety and Environment,2017,17(6):2093-2098.
[14]吴少伟.太原南站钢结构健康监测方案及安全预警研究[D].太原:太原理工大学,2017.
[15]汪永平.空间钢桁架临时支撑卸载综合预警评估法及应用[J].建筑钢结构进展,2018,20(2):110-116.WANG Yongping.A comprehensive assessment and early warning method and its application for spatial steel trusses during the unloading process of temporary supports[J].Progress in Steel Building Structures,2018,20(2): 110-116.
[16]江新,杜海文,袁轩,等.神经网络范式下硐室群施工安全风险预警研究[J].中国安全科学学报,2018,28(2):181-186.JIANG Xin,DU Haiwen,YUAN Xuan,et al.Study on early warning of safety risk in underground cavern group construction under neural network paradigm [J].China Safety Science Journal,2018,28(2):181-186.

相似文献/References:

[1]田栩静,董宝田,张正.地铁突发大客流安全控制方式设计[J].中国安全生产科学技术,2013,9(9):188.[doi:10.11731/j.issn.1673-193x.2013.09.035]
 TIAN Xu jing,DONG Bao tian,ZHANG Zheng.Safety control design on large passenger flow of subway[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(8):188.[doi:10.11731/j.issn.1673-193x.2013.09.035]
[2]张正,蒋熙,贺英松.城市轨道交通高峰时段车站协同限流安全控制研究[J].中国安全生产科学技术,2013,9(10):5.[doi:10.11731/j.issn.1673-193x.2013.10.001]
 ZHANG Zheng,JIANG Xi,HE Ying song.Study on collaborative flowlimiting safety control of urban railway station in peak hours[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(8):5.[doi:10.11731/j.issn.1673-193x.2013.10.001]
[3]刘欣,李鹏亮,屈波,等.地层条件下油气混合气安全氧含量实验研究[J].中国安全生产科学技术,2018,14(9):90.[doi:10.11731/j.issn.1673-193x.2018.09.014]
 LIU Xin,LI Pengliang,QU Bo,et al.Experimental study on safe oxygen content of oilgas mixture under reservoir conditions[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(8):90.[doi:10.11731/j.issn.1673-193x.2018.09.014]

备注/Memo

备注/Memo:
收稿日期: 2019-07-10
* 基金项目: 国家自然科学基金项目(51478384)
作者简介: 李慧民,硕士,教授,主要研究方向为土木工程建造与管理。
通信作者: 段品生,硕士研究生,主要研究方向为土木工程建造与管理。
更新日期/Last Update: 2019-09-04