|本期目录/Table of Contents|

[1]刘鸿儒,余丁浩,李钢,等.精细化城市RC建筑信息的快速智能获取方法[J].建筑科学与工程学报,2025,42(02):101-113.[doi:10.19815/j.jace.2023.03075]
　LIU Hongru,YU Dinghao,LI Gang,et al.Fast and intelligent acquisition method for refined urban RC building information[J].Journal of Architecture and Civil Engineering,2025,42(02):101-113.[doi:10.19815/j.jace.2023.03075]
点击复制

精细化城市RC建筑信息的快速智能获取方法(PDF)
分享到：

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:
42卷
期数:
2025年02期
页码:
101-113
栏目:
建筑结构
出版日期:
2025-03-20

文章信息/Info

Title:
Fast and intelligent acquisition method for refined urban RC building information
文章编号:
1673-2049(2025)02-0101-13
作者:
刘鸿儒,余丁浩,李钢,董志骞
(大连理工大学 海岸和近海工程国家重点实验室,辽宁 大连 116024)
Author(s):
LIU Hongru, YU Dinghao, LI Gang, DONG Zhiqian
(State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China)
关键词:
建筑信息;  模糊推理;  机器学习;  精细化有限元模型;  城市建筑
Keywords:
building information;  fuzzy inference;  machine learning;  refined finite element model;  urban building
分类号:
TU364
DOI:
10.19815/j.jace.2023.03075
文献标志码:
A
摘要:
为便于开展城市区域精细化震害模拟工作,提出一种适用于城市建筑的精细化快速信息获取方法。首先采用基于无人机倾斜摄影的低空遥测技术,通过三维重建获取区域建筑外部几何信息; 然后结合卷积神经网络和模糊推理方法,实现建筑层数和结构类型的快速识别; 最后利用AdaBoost和Random Forest两种集成算法对框架和框剪结构内部构件信息进行预测,提出城市区域常用RC框架和RC框剪结构内部隐蔽信息的确定方法,并采用该方法对两栋实际建筑进行参数预测和震害模拟。结果表明:采用该方法可对结构内部柱截面尺寸、柱距、配筋率等构件级信息进行快速精细化预测,建立的结构有限元分析模型可实现区域建筑的精细化震害模拟,有效提高城市建筑参数获取及建模的效率和精细化程度,基于该方法计算出的结构地震响应相比于传统区域建筑信息获取方法和震害模拟方法具有更高的计算精度。
Abstract:
In order to facilitate the fine-grained seismic simulation in urban areas, a refined and rapid information acquisition method applicable to urban buildings was proposed. The low-altitude telemetry based on unmanned aerial vehicle(UAV)tilt photography was adopted to obtain the external geometric information of regional buildings through 3D reconstruction. Then combining convolutional neural network and fuzzy inference methods, the fast identification of building layers and structure types were achieved. Finally, the AdaBoost and Random Forest integration algorithms were compared to predict the internal component information of the frame and frame-shear structure, and the method of determining the hidden information of the RC frame and RC frame-shear structure commonly used in urban areas was proposed. This method was used to predict the parameters and simulate the seismic damage of two actual buildings. The results show that the method can make fast and refined prediction of member-level information such as column section size, column spacing and reinforcement rate inside the structure. By using the method to establish a structural finite element analysis model, fine-grained seismic simulation of regional buildings can be realized, and the method can effectively improve the efficiency and refinement of urban building parameter acquisition and modeling. The structural seismic response calculated based on the method has higher computational accuracy compared with the traditional methods of regional building information acquisition and seismic simulation.

参考文献/References:

[1] 熊 琛.基于时程分析和三维场景可视化的区域建筑震害模拟研究[D].北京:清华大学,2016.
XIONG Chen. Study on seismic damage simulation of regional buildings based on time-history analysis and 3D scene visualization[D]. Beijing: Tsinghua University, 2016.
[2]李岩玮.四川省甘孜州木雅祖庆学校综合楼震害损伤评估及加固方案分析[D].重庆:重庆大学,2016.
LI Yanwei. Seismic damage assessment and strengthening analysis of Muyazuqing school in Sichuan province[D]. Chongqing: Chongqing University, 2016.
[3]许 镇.城市综合数字防灾:地震及次生灾害情景仿真与韧性评估[M].北京:中国建筑工业出版社,2021.
XU Zhen. Urban comprehensive digital disaster prevention:scenario simulation and resilience assessment of earthquake and secondary disaster[M]. Beijing: China Architecture & Building Press, 2021.
[4]庄钰蓥,熊 峰,吕 洋,等.基于GIS的城市建筑群动力响应分析模型建模方法研究[J].世界地震工程,2021,37(4):137-147.
ZHUANG Yuying, XIONG Feng, LU Yang, et al. Research on a GIS-based method of model establishment for seismic response analysis of urban building groups[J]. World Earthquake Engineering, 2021, 37(4): 137-147.
[5]HORI M, OGUNI K, ICHIMURA T. Integrated simulation for earthquake hazard and disaster prediction[J]. Journal of Earthquake and Tsunami, 2009, 3(3): 121-141.
[6]李 钢,张鹏程,董志骞,等.村镇砌体建筑群信息智能获取与震害预测[J].建筑结构学报,2022,43(8):196-208.
LI Gang, ZHANG Pengcheng, DONG Zhiqian, et al. Intelligent information acquisition methods and seismic damage prediction of rural masonry building groups[J]. Journal of Building Structures,2022, 43(8): 196-208.
[7]尹菲菲.城市震害模拟中的RC框架结构自动化精细建模方法研究[D].哈尔滨:中国地震局工程力学研究所,2017.
YIN Feifei. Research on automatic fine modeling method of RC frame structure in urban earthquake damage simulation[D]. Harbin: Institute of Engineering Mechanics, China Earthquake Administration, 2017.
[8]杨 辉,崔 阳,罗 盛,等.无人机倾斜摄影技术在建筑工程三维建模中应用[J].建筑技术开发,2022,49(2):88-90.
YANG Hui, CUI Yang, LUO Sheng, et al. Three-dimensional modeling application of drone tilt photography technology in architecture[J]. Building Technology Development, 2022, 49(2): 88-90.
[9]岳亚男.基于卷积神经网络的RC框架结构损伤识别研究[D].哈尔滨:哈尔滨工业大学,2020.
YUE Yanan. Research on damage identification of RC frame structure based on convolutional neural network[D]. Harbin: Harbin Institute of Technology, 2020.
[10]彭慧君.松原地区群体建筑结构抗震性能分析[D].哈尔滨:哈尔滨工业大学,2020.
PENG Huijun. Seismic performance analysis of group building structures in Songyuan area[D]. Harbin: Harbin Institute of Technology, 2020.
[11]王士同.模糊推理理论与模糊专家系统[M].上海:上海科学技术文献出版社,1995.
WANG Shitong. Fuzzy inference theory and fuzzy expert system[M]. Shanghai: Shanghai Scientific and Technological Literature Press, 1995.
[12]高层建筑混凝土结构技术规程:JGJ 3—2010[S].北京:中国建筑工业出版社,2011.
Technical specification for concrete structures of tall building: JGJ 3—2010[S]. Beijing: China Architecture & Building Press, 2011.
[13]梅清良,宋海威.浅谈建筑层数与结构的选择[J].科技信息(科学教研),2008(6):94.
MEI Qingliang, SONG Haiwei. A brief discussion on the selection of building stories and structure[J]. Science & Technology Information, 2008(6): 94.
[14]建筑抗震设计规范:GB 50011—2001[S].北京:中国建筑工业出版社,2004.
Code for seismic design of buildings: GB 50011—2001[S]. Beijing: China Architecture & Building Press, 2004.
[15]何振华,许俊杰.关于大跨度钢结构设计的一些探讨[J].中华民居,2014(1):28.
HE Zhenhua, XU Junjie. Some discussions on the design of large-span steel structures[J]. China Homes, 2014(1): 28.
[16]巩敦卫,孙晓燕.智能控制技术简明教程[M].北京:国防工业出版社,2010.
GONG Dunwei, SUN Xiaoyan. A concise tutorial on intelligent control technology[M]. Beijing: National Defence Industry Press, 2010.
[17]FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139.
[18]BREIMAN L.Random forests[J]. Machine Learning,2001, 45(1): 5-32.
[19]STEINBRUNN M, MOERKOTTE G, KEMPER A. Heuristic and randomized optimization for the join ordering problem[J]. The VLDB Journal, 1997, 6(3): 191-208.
[20]程庆乐,曾 翔,熊 琛,等.区域建筑震害模拟方法分析对比[J].工程力学,2017,34(增1):105-110,128.
CHENG Qingle, ZENG Xiang, XIONG Chen, et al. Comparison of seismic damage simulation methods for different regional buildings[J]. Engineering Mechanics, 2017, 34(S1): 105-110, 128.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期:2023-03-21
基金项目:国家自然科学基金项目(52008075,52225804); 中央高校基本科研业务费专项资金项目(DUT22RC3038); 辽宁省博士科研启动基金项目(2022-BS-089)
通信作者:李 钢(1979-),男,工学博士,教授,博士生导师,E-mail:gli@dlut.edu.cn。
Author resume: LI Gang(1979-), male, PhD, professor, E-mail: gli@dlut.edu.cn.
更新日期/Last Update: 2025-03-20