scholarly journals Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

2018 ◽  
Author(s):  
Miguel Abambres ◽  
Komal Rajana ◽  
Konstantinos Tsavdaridis ◽  
Tiago Ribeiro
Keyword(s):  
Neural Network ◽  
Buckling Load ◽  
Steel Beams ◽  
Load Prediction

scholarly journals Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Computers ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 2 ◽  
Author(s):  
Miguel Abambres ◽  
Komal Rajana ◽  
Konstantinos Tsavdaridis ◽  
Tiago Ribeiro
Keyword(s):  
Neural Network ◽  
Computing Time ◽  
Design Guidelines ◽  
Design Tool ◽  
Buckling Load ◽  
Steel Beams ◽  
Efficient Design ◽  
Load Prediction ◽  
Element Analysis ◽  
Parametric Finite Element Analysis

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

scholarly journals Neural Network-based formula for the buckling load prediction of I-section cellular steel beams

2018 ◽  
Author(s):  
Miguel Abambres ◽  
Komal Rajana ◽  
Konstantinos Tsavdaridis ◽  
Tiago Ribeiro
Keyword(s):  
Neural Network ◽  
Computing Time ◽  
Design Guidelines ◽  
Design Tool ◽  
Buckling Load ◽  
Steel Beams ◽  
Efficient Design ◽  
Load Prediction ◽  
Element Analysis ◽  
Parametric Finite Element Analysis

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localised and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to estimate the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

scholarly journals Research on Buckling Load Prediction of Composite Stiffened Plates Based on BP Neural Network

2020 ◽  
Vol 1576 ◽  
pp. 012032
Author(s):  
Chengcheng Zhao ◽  
Junqing Yin ◽  
Yongdang Chen ◽  
Jinyu Gu ◽  
Haotian Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Buckling Load ◽  
Stiffened Plates ◽  
Load Prediction

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

Prediction of compression buckling load and buckling mode of hat-stiffened panels using artificial neural network

2021 ◽  
Vol 242 ◽  
pp. 112275
Author(s):  
Zhenya Sun ◽  
Zhenkun Lei ◽  
Ruixiang Bai ◽  
Hao Jiang ◽  
Jianchao Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Buckling Load ◽  
Buckling Mode ◽  
Stiffened Panels ◽  
Artificial Neural

scholarly journals The Prediction of Buckling Load of Laminated Composite Hat-Stiffened Panels Under Compressive Loading by Using of Neural Networks

2018 ◽  
Vol 12 (1) ◽  
pp. 468-480 ◽  
Author(s):  
Shashi Kumar ◽  
Rajesh Kumar ◽  
Sasankasekhar Mandal ◽  
Atul K. Rahul
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Network Architecture ◽  
Laminated Composite ◽  
Buckling Load ◽  
Stiffened Panel ◽  
Stiffness Ratio ◽  
Data Set ◽  
Stiffened Panels ◽  
Structural Problems

Background:Stiffened panels are being used as a lightweight structure in aerospace, marine engineering and retrofitting of building and bridge structure. In this paper, two efficient analytical computational tools, namely, Finite Element Analysis (FEA) and Artificial Neural Network (ANN) are used to analyze and compare the results of the laminated composite 750-hat-stiffened panels.Objective:Finite Element (FE) is an efficient and versatile method for the analysis of a complex problem. FE models have been used to generate data set of four different parameters. The four parameters are extensional stiffness ratio of skin in the longitudinal direction to the transverse direction, orthotropy ratio of the panel, the ratio of twisting stiffness to transverse flexural stiffness and smeared extensional stiffness ratio of stiffeners to that of the plate.Results and Conclusion:For training of ANN, multilayer feedforward back-propagation has been used as a network function with two-hidden layers in the neural network. The good network architecture is achieved after several iterations to predict the buckling load of the stiffened panel. ANN prediction for unknown new data set is in good agreement with FEA results of different cases, which show that ANN tool can be used for the design of complex structural problems in civil engineering and optimization of the laminated composite stiffened panel.

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

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