Parameterized fragility analysis of steel frame structure subjected to blast loads using Bayesian logistic regression method

2020 ◽  
Vol 87 ◽  
pp. 102000
Author(s):  
Xiaoran Song
Keyword(s):  
Logistic Regression ◽  
Regression Method ◽  
Steel Frame ◽  
Frame Structure ◽  
Fragility Analysis ◽  
Blast Loads ◽  
Logistic Regression Method ◽  
Bayesian Logistic Regression ◽  
Steel Frame Structure

Experimental Research and Finite Element Analysis on Behavior of Steel Frame with Semi-Rigid Connections

2010 ◽  
Vol 168-170 ◽  
pp. 553-558
Author(s):  
Feng Xia Li ◽  
Bu Xin
Keyword(s):  
Architectural Design ◽  
Plastic Hinge ◽  
Steel Frames ◽  
Seismic Energy ◽  
Internal Force ◽  
Steel Frame ◽  
Frame Structure ◽  
Element Analysis ◽  
Rigid Deformation ◽  
Steel Frame Structure

Most steel beam-column connections actually show semi-rigid deformation behavior that can contribute substantially to overall displacements of the structure and to the distribution of member forces. Steel frame structure with semi-rigid connections are becoming more and more popular due to their many advantages such as the better satisfaction with the flexible architectural design, low inclusive cost and environmental protect as well. So it is very necessary that studying the behavior of those steel frame under cyclic reversal loading. On the basics of connections experiments the experiment research on the lateral resistance system of steel frame structure has been completed. Two one-second scale, one-bay, two-story steel frames with semi-rigid connections under cyclic reversal loading. The seismic behavior of the steel frames with semi-rigid connections, including the failure pattern, occurrence order of plastic hinge, hysteretic property and energy dissipation, etc, was investigated in this paper. Some conclusions were obtained that by employing top-mounted and two web angles connections, the higher distortion occurred in the frames, and the internal force distributing of beams and columns was changed, and the ductility and the absorbs seismic energy capability of steel frames can be improved effectively.

Linear and non-linear analysis on two-dimensional steel frame under different temperatures

2019 ◽  
Vol 10 (1) ◽  
pp. 48-55
Author(s):  
Parthasarathi N. ◽  
Satyanarayanan K.S. ◽  
Prakash M. ◽  
Thamilarasu V.
Keyword(s):  
High Temperatures ◽  
Progressive Collapse ◽  
Transient State ◽  
Steel Frame ◽  
Frame Structure ◽  
Two Dimensional ◽  
Content Type ◽  
Frame Building ◽  
Different Temperatures ◽  
Steel Frame Structure

Purpose Progressive collapse because of high temperatures arising from an explosion, vehicle impact or fire is an important issue for structural failure in high-rise buildings. Design/methodology/approach The present study, using ABAQUS software for the analysis, investigated the progressive collapse of a two-dimensional, three-bay, four-storey steel frame structure from high-temperature stresses. Findings After structure reaches the temperature results like displacement, stress axial load and shear force are discussed. Research limitations/implications Different temperatures were applied to the columns at different heights of a structure framed with various materials. Progressive collapse load combinations were also applied as per general service administration guidelines. Originality/value This study covered both steady-state and transient-state conditions of a multistorey-frame building subjected to a rise in temperature in the corner columns and intermediate columns. The columns in the framed structure were subjected to high temperatures at different heights, and the resulting displacements, stresses and axial loads were obtained, analysed and discussed.

scholarly journals Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

AITI ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 42-55
Author(s):  
Radius Tanone ◽  
Arnold B Emmanuel
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Learning Approach ◽  
Know How ◽  
Independent Variables ◽  
Machine Learning Approach ◽  
Logistic Regression Method ◽  
Python Programming

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.  

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