Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques

Author(s):  
Tien-Thinh Le ◽  
Panagiotis G. Asteris ◽  
Minas E. Lemonis
2021 ◽  
Vol 1016 ◽  
pp. 618-623
Author(s):  
Jaksada Thumrongvut ◽  
Apichat Tipcharoen ◽  
Kamonwan Prathumwong

This paper presents experimental studies on the post-fire performance of concrete-filled steel tube (CSFT) columns under uni-axial load. The structural responses and axial load capacity of CSFT columns after exposure to elevated temperatures are investigated and discussed. All of the specimens are 750 mm in height, the nominal cross-section of the specimen is 150 mm x 150 mm, and have cylinder compressive strength of 18 MPa. The primary test parameters to be measured during the uni-axial compression test are wall thicknesses of the square tube (3.0 mm, 4.5 mm and 6.0 mm) and three different exposure to elevated temperatures (400°C, 600°C and 800°C). The results showed that the load-axial shortening relationship of the CSFT columns have a linear elastic response up to 80-90% of axial load capacity. After the axial load capacity is reached, the load-axial shortening curves are rarely becoming a nonlinear manner. It is also shown that the axial load capacity and ductility of the post-fire test columns are decreased significantly compared to the columns at ambient temperature, depending mainly on the elevated temperature. In addition, by comparing the axial load capacity of the test results with those obtained from the ACI design equation, the comparison results indicate that calculation formula in ACI code unconservative predicts the axial load capacity of the CSFT columns after exposure to elevated temperatures. Finally, the residual strength ratios are modified to both strength of concrete and steel tube under ambient temperature, and analyzed to evaluate the effect of post-fire behavior on the axial capacity of CFST columns.


Author(s):  
Soner Güler ◽  
Fuat Korkut ◽  
Namik Yaltay ◽  
Demet Yavuz

Concrete-filled steel tubular (CFST) columns are widely used in construction of high-rise buildings and peers of bridges to increase the lateral stiffness of the buildings, the axial load capacity, ductility, toughness, and resistance of corrosion of the columns. The CFST columns have much superior characteristics compared with traditionally reinforced concrete columns. The position of the concrete and steel tube in the cross-section of the CFST column is the most appropriate solution in terms of the strength and ductility. The steel tube, which is placed outside of the cross-section of the column, withstand the bending moment effectively. The concrete that is placed into the steel tube delay the local buckling of the steel tube and increase the axial load capacity of the column due to continually lateral confining. This paper presents a review on experimental results of the axial behavior of CFST columns performed by various researchers.


2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  

Author(s):  
Anantvir Singh Romana

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.


Author(s):  
Padmavathi .S ◽  
M. Chidambaram

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.


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