Deep learning-based brace damage detection for concentrically braced frame structures under seismic loadings

2019 ◽  
Vol 22 (16) ◽  
pp. 3473-3486 ◽  
Author(s):  
Heng Liu ◽  
Yunfeng Zhang
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Learning Model ◽  
Frame Structures ◽  
Braced Frame ◽  
Concentrically Braced Frame ◽  
Seismic Force ◽  
Concentrically Braced ◽  
Deep Learning Model

Automated and robust damage detection tool is needed to enhance the resilience of civil infrastructures. In this article, a deep learning-based damage detection procedure using acceleration data is proposed as an automated post-hazard inspection tool for rapid structural condition assessment. The procedure is investigated with a focus on application in concentrically braced frame structure, a commonly used seismic force-resisting structural system with bracing as fuse members. A case study of six-story concentrically braced frame building was selected to numerically validate and demonstrate the proposed method. The deep learning model, a convolutional neural network, was trained and tested using numerically generated dataset from over 2000 sets of nonlinear seismic simulation, and an accuracy of over 90% was observed for bracing buckling damage detection in this case study. The results of the deep learning model were also discussed and extended to define other damage feature indices. This study shows that the proposed procedure is promising for rapid bracing condition inspection in concentrically braced frame structures after earthquakes.

Effect of Beam-Column Connection Fixity on Collapse Performance of a Six-Storey Special Concentrically Braced Frame

2018 ◽  
Vol 763 ◽  
pp. 157-164
Author(s):  
Vahid Mohsenzadeh ◽  
Lydell D.A. Wiebe
Keyword(s):  
Large Earthquake ◽  
Lateral Force ◽  
Concentrically Braced Frames ◽  
Gusset Plate ◽  
Braced Frame ◽  
Soft Storey ◽  
Concentrically Braced Frame ◽  
Seismic Force ◽  
Concentrically Braced ◽  
Strength And Stiffness

Concentrically braced frames are one of the most common seismic force resisting systems because of their high strength and stiffness. In current practice, the gusset plates that connect the brace to the adjacent beams and columns can increase the strength and stiffness of the connection significantly. This strength and stiffness can provide a reserve of lateral force resisting capacity during a large earthquake, which may play a role in the seismic collapse behaviour of the braced frame. An alternative connection has recently been proposed as a means of ensuring that brace buckling occurs only in-plane, that no field welding is required, and that all damage is confined to a replaceable brace module. However, the proposed connection does not include a gusset plate that can provide a similar stiffness and reserve capacity. In order to investigate the potential influence of the range of possible beam-column-gusset plate designs, this paper assesses the effect of the fixity of these connections on the behavior of a six-storey special concentrically braced frame. Nonlinear dynamic analyses have been conducted to determine the seismic performance of the frame with this connection modelled using three different assumptions (pinned, shear tab and fixed), and the collapse risk is assessed using the FEMA P695 methodology. The results show that when the gravity framing is not modelled, the fixity of the beam-column connections is important in avoiding the formation of a soft storey under extreme earthquakes, thereby reducing the probability of collapse of the building.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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