Intelligent design method for beam and slab of shear wall structure based on deep learning

被引:57
作者
Zhao, Pengju [1 ]
Liao, Wenjie [1 ]
Xue, Hongjing [1 ,2 ]
Lu, Xinzheng [1 ]
机构
[1] Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China
[2] Beijing Inst Architectural Design, Beijing 100045, Peoples R China
来源
JOURNAL OF BUILDING ENGINEERING | 2022年 / 57卷
关键词
Beam and slab design; Intelligent structural design; Deep neural network; Layout plan; Component size; OPTIMIZATION; ALGORITHM;
D O I
10.1016/j.jobe.2022.104838
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Beam and slab design is a critical component of shear wall structure design. Currently, conventional manual design is time-consuming, and defining objective functions and design variables of an optimization design is challenging. In contrast, deep learning methods can learn high-dimensional image features and generate new designs, providing new solutions for efficient and intelligent structural design. Therefore, based on deep neural networks, this study proposes an intelligent layout design method for beams of reinforced concrete shear-wall structures using the input of fused building space and element attributes. This method learned the implicit laws of existing designs and realized the inferential generation of new layout schemes. Subsequently, based on mathematical statistics, methods to determine the type and size of coupling and frame beams are proposed. A typical case study shows that the structural performance of the beam and slab designed by this method was comparable to that of competent engineers. The maximum inter-story drift ratio of the result designed by the proposed method differs from that designed by engineers by no more than 5 x 10(-5). The differences in the maximum vertical typical-floor-slab displacement, the concrete consumption, and the steel consumption between the design result of the proposed method and the engineer's design result are 0.8%, 2.88%, and 6.20%, respectively. Moreover, the design efficiency was significantly improved by more than 30 times.
引用
收藏
页数:19
相关论文
共 48 条
[21]   Automated optimization of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm [J].
Mangal, Mohit ;
Cheng, Jack C. P. .
AUTOMATION IN CONSTRUCTION, 2018, 90 :39-57
[22]   Robust detection of lines using the progressive probabilistic Hough transform [J].
Matas, J ;
Galambos, C ;
Kittler, J .
COMPUTER VISION AND IMAGE UNDERSTANDING, 2000, 78 (01) :119-137
[23]  
Nauata N., 2021, P IEEE CVF C COMPUTE, P13632
[24]  
Nauata N, 2020, Img Proc Comp Vis Re, V12346, P162, DOI 10.1007/978-3-030-58452-8_10
[25]   A genetic algorithm for beam-slab layout design of rectilinear floors [J].
Nimtawat, Anan ;
Nanakorn, Pruettha .
ENGINEERING STRUCTURES, 2010, 32 (11) :3488-3500
[26]   Automated layout design of beam-slab floors using a genetic algorithm [J].
Nimtawat, Anan ;
Nanakorn, Pruettha .
COMPUTERS & STRUCTURES, 2009, 87 (21-22) :1308-1330
[27]   Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net [J].
Pan, Zhuokun ;
Xu, Jiashu ;
Guo, Yubin ;
Hu, Yueming ;
Wang, Guangxing .
REMOTE SENSING, 2020, 12 (10)
[28]  
Perez R., 2019, CTBUH J
[29]   Use of convolutional networks in the conceptual structural design of shear wall buildings layout [J].
Pizarro, Pablo N. ;
Massone, Leonardo M. ;
Rojas, Fabian R. ;
Ruiz, Rafael O. .
ENGINEERING STRUCTURES, 2021, 239
[30]   Structural design of reinforced concrete buildings based on deep neural networks [J].
Pizarro, Pablo N. ;
Massone, Leonardo M. .
ENGINEERING STRUCTURES, 2021, 241