检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Zheng, Zhibao Dai, Hongzhe Wang, Yuyin Wang, Wei Key Lab of Structures Dynamic Behavior and Control Harbin Institute of Technology Ministry of Education Harbin150090 China School of Civil Engineering Harbin Institute of Technology Harbin150090 China

This paper presents a new methodology for structural reliability analysis via stochastic finite element method (SFEM). A novel sample-based SFEM is firstly used to compute structural stochastic responses of all spatial points at the same time, which decouples the stochastic response into a combination of a series of deterministic responses with random variable coefficients, and solves corresponding stochastic finite element equation through an iterative algorithm. Based on the stochastic response obtained by the SFEM, the limit state function described by the stochastic response and the multidimensional integral encountered in reliability analysis can be computed without any difficulties, and failure probabilities of all spatial points are calculated once time. The proposed method can be applied to high-dimensional stochastic problems, and one of the most challenging issues encountered in high-dimensional reliability analysis, known as Curse of Dimensionality, can be circumvented without expensive computational costs. Three practical ex- Copyright © 2020, The Authors. All rights reserved.

关键词： Reliability analysis

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Experimental evaluation of mechanical properties and microstructure for recycled aggregate concrete collaboratively modified with nano-silica and mixed fibers” [Constr. Build. Mater. 403 (2023) 133125]

引用

Construction and Building Materials 2023年 404卷

作者： Hang Sun Liang Luo Huan Yuan Xi Li School of Transportation and Science Engineering Harbin Institute of Technology Harbin 150090 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin 150090 China School of Civil Engineering Harbin Institute of Technology Harbin 150090 China

来源：评论

学校读者我要写书评

暂无评论

Three-Dimensional Reconstruction and Monitoring of Large-Scale structures via Real-Time Multi-vision System 11th

Three-Dimensional Reconstruction and Monitoring of Large-Sca...

引用

11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Tang, Yunchao Chen, Mingyou Wu, Xiangguo Huang, Kuangyu Wu, Fengyun Zou, Xiangjun He, Yuxin College of Urban and Rural Construction Zhongkai University of Agriculture and Engineering Guangzhou510225 China Key Laboratory of Key Technology on Agricultural Machine and Equipment College of Engineering South China Agricultural University Guangzhou510642 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education and School of Civil Engineering Harbin Institute of Technology Harbin150001 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China

ISBN: (纸本)9789811555763

A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) materials under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST, then point cloud acquisition, filtering, and stitching algorithms are applied to obtain 3D point cloud of the specimen surface. Novel point cloud correction algorithms based on geometric features and deep learning are proposed to correct the coordinates of the stitched point cloud. The proposed algorithms center on the stitching error of the multi-view point cloud and the geometric and spatial characteristics of the targets for error compensation, which makes them highly adaptive and efficient. A high-accuracy multi-view 3D model for the purposes of real-time complex surface monitoring can be obtained via this method. Performance indicators of the two algorithms were evaluated on actual tasks. The cross-section diameters at specific heights in the reconstructed models were calculated and compared against laser range finder data to test the performance of the proposed method. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after point cloud correction and meets the requirements for dynamic monitoring. The proposed method is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Vibrations (mechanical)

来源：评论

学校读者我要写书评

暂无评论

Machine-learning-based methods for output-only structural modal identification

arXiv

引用

arXiv 2020年

作者： Bao, Yuequan Liu, Dawei Tang, Zhiyi Li, Hui Key Lab of Structures Dynamic Behavior Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Intelligent Disaster Mitigation Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China

In this study, we propose a machine-learning-based approach to identify the modal parameters of the output-only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network become the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex cost function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach's good capability in blindly extracting modal information from system responses. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimisation route of embodied carbon emissions and cost of prefabricated buildings: A case study of prefabricated shear wall residential structures

引用

Energy and Buildings 2025年 343卷

作者： Yifan Dong Qingpeng Man Yadi Zhang Yuan Chang School of Civil Engineering Harbin Institute of Technology Harbin 150001 China Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin 150009 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin 150009 China School of Management Science and Engineering Central University of Finance and Economics Beijing 100081 China

Prefabricated buildings are increasingly replacing traditional cast-in-place concrete structures to reduce construction time and environmental impacts. However, prefabricated components frequently struggle to simultaneously achieve economic cost-effectiveness and carbon emissions reduction. Thus, the construction industry urgently requires component-level technical guidance on prefabricated construction to ensure economic feasibility while also supporting carbon neutrality goals. This study used a typical prefabricated shear wall residential building as a case study to improve the economic and environmental performance of prefabricated building construction. Seven types of structural components are examined, including interior shear walls (ISW) and exterior shear walls (ESW), composite floor slabs (CFS), air-conditioning panels/canopies (A/C), stair landings (SL), beams (BM), and stair flights (SF). By systematically replacing cast-in-place components with their prefabricated counterparts using Revit and Glodon soft tools, the study quantifies and analyses the differences in carbon emissions and costs. The key results are: (i) The optimal replacements for minimising carbon emissions are “BM → SL → SF → CFS → ESW → ISW → A/C,” whereas the most economical cost-effective sequence is “BM → SL → SF → A/C → ISW → ESW → CFS.” (ii) Prefabrication does not universally result in lower carbon emissions. Among the seven types of components, four exhibit higher carbon emissions per unit volume of concrete than their cast-in-place counterparts, with the highest value of CFS (86.2 kg/m 3 ). (iii) To achieve carbon emissions reduction through prefabrication, an optimal prefabrication rate of 3.8–10.6 % is recommended. Within this range, increasing the prefabrication rate results in cost increases while enabling carbon reduction. However, exceeding the 10.6 % cap rate results in both higher costs and carbon emissions compared to traditional cast-in-place methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of uneven distribution of snow load based on generalized regression neural network 10

Prediction of uneven distribution of snow load based on gene...

引用

10th International Structural Engineering and Construction Conference, ISEC 2019

作者： Ren, Zhicheng Zhang, Qingwen Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters Ministry of Industry and Information Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9780996043762

At present, there are mainly three research ways on the distribution of snow load: field measurement, experimental simulation, and numerical simulation. They all need expensive equipment, specimen model used only once, and slight defective simulation software. Artificial Neural Networks (ANNs) rely on a large number of input functions to estimate unknown or known occurrences. This paper applies ANNs to predict the uneven distribution of snow loads. First, the snow load database is collected and set up, including all the data from field measurement and experimental simulation. Second, part of the data from the 1m3 model is used as a training set to train Backpropagation Neural Networks (BPNN) and Generalized Regression Neural Networks (GRNN). The parameters of the two neural networks are determined (such as the smoothing factor in the GRNN), and the rest of the data is used to examine the model;the prediction and measurement results of the two models are compared. Finally, based on different wind direction angles and wind speed, a GRNN model is established to predict the uneven snow load distribution. Through comparative analysis, GRNN is more suitable for predicting the uneven distribution of snow loads, especially in predicting snow thickness. In particular, the prediction effect of the model on the snow load distribution trend is good, and the prediction ability of the neural network model is displayed. Copyright © 2019 ISEC Press.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Crack pattern prediction of laterally loaded panels with openings based on ANN method 10

Crack pattern prediction of laterally loaded panels with ope...

引用

10th International Structural Engineering and Construction Conference, ISEC 2019

作者： Wang, Yongfei Zhang, Yu Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters Ministry of Industry and Information Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9780996043762

In this paper, a Back Propagation Neural Network (BPNN) is used to predict crack pattern for masonry panels with opening subjected to lateral loading. The cellular automata method is used to digitalize the panels, including two steps - dividing a panel into a certain number of cells and calculating cell state values by use of a Von Neumann neighborhood model. These digitalized values are used as input data of NN model, respectively. All the experimental data is collected, including panel configuration, material property, opening ratio and location, state values, and crack pattern. The NN model is trained repeatedly, taking part of the data as a training set, to determine parameters, and the rest of the data is taken to check the model. Well-trained NN models can predict the crack pattern of any other panel. The results show that NN method is suitable for prediction of crack pattern. Comparing the two ways of prediction, the Fragility Coefficient Method gets a more precise pattern. The predicted cracks are distributed successively in some specific areas, especially in high similarity, compared with experimental crack pattern. Copyright © 2019 ISEC Press.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Machine learning for bridge wind engineering

Advances in Wind Engineering

引用

Advances in Wind Engineering 2024年第1期1卷

作者： Zeyu Zhang Shanwu Li Hui Feng Xuxi Zhou Nan Xu Hui Li Shujin Laima Wenli Chen School of Civil Engineering Harbin Institute of Technology Harbin 150090 China Department of Mechanical Engineering-Engineering Mechanics Michigan Technological University Houghton MI 49931 USA Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin 150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin 150090 China

Modeling and control are primary domains in bridge wind engineering. The natural wind field characteristics (e.g., non-stationary, non-uniform, spatial-temporal changing characteristics) and the wind-bridge interaction processes are physically complex and exhibit strong nonlinearity. The lack of analyzing these complex physical processes based on first principles makes it difficult for traditional modeling and control methods to accurately characterize or control wind field and wind-induced vibration of bridges. Data-driven automatic modeling and control is a new expansion direction in bridge wind engineering due to the extensive valuable historical data available and the complex, high-dimensional character of the state space and action space of the agent in active control. Machine Learning (ML) has been shown versatile for various modeling and control tasks in bridge wind engineering due to its excellent ability to automatically extract space features efficiently and make wonderful decisions facing complicated tasks without manual interval. Starting from an overview of the basic concepts and methods of ML, this review will comprehensively analyze and summarize the application of ML methods in various aspects of bridge wind engineering, including wind fields, static aerodynamic reconstruction, wind-induced vibrations and control based on reinforcement learning, in order to enable readers to have a full knowledge of the potential of ML in various subfields. The essence of these tasks is to rely on data-driven minds to solve tricky problems that cannot be solved based on first principles. ML will continue to integrate and coexist with wind engineering of bridges and generate new revolutions in both theories and applications owing to its powerful information-processing and modeling-learning ability.

关键词： Artificial intelligence Machine learning Bridge wind engineering

来源：评论

学校读者我要写书评

暂无评论

Study on the calculation method of ice load of icebreaker in straight line state 25

Study on the calculation method of ice load of icebreaker in...

引用

25th International Conference on Port and Ocean Engineering under Arctic Conditions, POAC 2019

作者： Li, Liang Wang, Wei Ma, Haichong Meng, Xiangbin Han, Guangchun Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China

Ice action is the primary factor that affecting the navigation of icebreaker in the ice area. The ice-breaking load also directly impacts on the construction and design of the icebreaker. Therefore, reasonable calculation of ice-breaking load has become the most important issue at present. Focusing on ice cover, this paper presented a theoretical model for calculating the ice-breaking resistance of the icebreaker. The presented model is based on the combination of semi-infinite beam and infinite beam along and perpendicular to the moving direction of the icebreaker on elastic foundation, respectively. The influence factors such as buoyancy and ice failure criterion are considered in this model. Then, authors compared the theoretical calculation formula with the existing empirical formula and theoretical formula to verify the feasibility of the presented calculation model in this paper. This model can provide theoretical reference for the design and construction of polar region icebreakers. © 2019, Lulea University of Technology. All rights reserved.

关键词： Icebreakers

来源：评论

学校读者我要写书评

暂无评论

Static and fatigue performances of clip anchorage system for FRP rod used in cable

引用

Engineering structures 2025年 339卷

作者： Guijun Xian Yanzhao Niu Jingwei Tian Chenggao Li Qingrui Yue Rui Guo Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology) Ministry of Education Harbin Heilongjiang 150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin 150090 China School of Civil Engineering Harbin Institute of Technology Harbin Heilongjiang 150090 China Research Institute of Urbanization and Urban Safety School of Civil and Resource Engineering University of Science and Technology Beijing Beijing China

To obtain the static and fatigue performances and provide the design parameters of FRP rod for the application in cable structures, the design and preparation method of steel clip anchor, steel clip anchor with aluminum sleeve and adhesive layer are presented in the present paper. The effect of taper difference, preload and anchor type on static and fatigue performances is studied by tensile and fatigue test, and the optimal design and application parameters of anchor are obtained. The results show that the peak shear stress of the rod at the front end of anchor can be reduced effectively by setting 0.1 ° taper difference, and the bearing capacity is increased by 40.7 %, 12.1 % and 5.3 % compared with 0 ° and 0.2 ° taper difference for steel clip anchor, steel clip anchor with aluminum sleeve and adhesive layer. The slippage of anchor can be reduced effectively with the increase of preload, which improves the critical slippage load of anchor. In addition, the stress concentration of rod inside the anchor can be reduced with the addition of low modulus adhesive layer between steel clip and rod, which reduces the sensitivity of tensile performances to taper difference and delays the fatigue damage accumulation process of rod inside the anchor, the anchoring efficiency and fatigue resistance increases by 10.9 % and 61.4 % compared with steel clip anchor, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：