Safety factor determining for space trusses by non-linear analysis and artificial neural network method

2013 ◽  
Vol 20 (3) ◽  
pp. 277-284
Author(s):  
Mehrzad Mohabbi Yadollahi ◽  
Fatma Karagöl ◽  
Mehmet Akif Kaygusuz ◽  
Rıza Polat ◽  
Ramazan Demirboga
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bearing Capacity ◽  
Safety Factor ◽  
Load Bearing Capacity ◽  
Geometric Imperfections ◽  
Load Bearing ◽  
Non Linear ◽  
Initial Geometric Imperfections ◽  
Space Trusses

AbstractDetermining a feasible safety factor for space trusses is an important phase in structural analysis that could have economic benefits. We know there are many kinds of imperfections in structural elements, which include both material and geometric flaws. Predicting factual behavior of structures is very difficult and occasionally impossible. Elements with initial geometric imperfections in space trusses are a common phenomenon, in addition, equivalent initial geometric imperfections can be applied for modeling of residual stresses or eccentric loading effect. The number of members in the space structures is usually high as is the diversity in the kind of initial imperfection. Therefore, there is a high likelihood that models must be analyzed. The structure must be analyzed with non-linear methods, making these approaches time consuming, and potentially uneconomical. In this study, we selected 30 cases for random analysis based on Monte Carlo methods to find the bearing capacity of the space truss. We attained results from the LUSAS program LUSAS Modeller, Version 13, UK program and these were then exported as input data to the Artificial Neural Network (ANN) program. A reasonable neural network has been found of predicting another 30 cases for load bearing capacity without any analysis and only based on the neural network program. Finally, a new approach for determining the load capacity of the space trusses was extracted and we predicted the occurrence possibility of the convenience load bearing capacity in 60 cases.

scholarly journals Numerical modelingof a composite section element from thin-walled profiles taking into account the initial geometric imperfections

2020 ◽  
Vol 17 (1) ◽  
pp. 82-86
Author(s):  
D. A. Prostakishina ◽  
◽  
N. D. Korsun ◽  
Keyword(s):  
Bearing Capacity ◽  
Compressive Force ◽  
Stability Loss ◽  
Load Bearing Capacity ◽  
Geometric Imperfections ◽  
Load Bearing ◽  
Composite Section ◽  
Initial Geometric Imperfections ◽  
Thin Walled ◽  
The Stability

The article describes the process of numerical simulation of a composite symmetric section element made of thin-walled Sigma profiles operating under conditions of longitudinal compressive force with bending, taking into account the initial geometric imperfections. At numerical modeling, the main criterion of the load-bearing capacity exhaustion in case of eccentric compression is the stability loss in one of the forms. However, for thin-walled elements, the loss of local stability does not mean that the load-bearing capacity is completely exhausted, since the element continues to carry the load, but to a lesser extent. Therefore, simulation was carried out in two stages: initially, in the elastic formulation, the possible buckling modes were determined, afterwards, there was made calculation on the deformed pattern taking into account possible imperfections.

scholarly journals Application of Artificial Neural Network to Predict Load Bearing Capacity and Stiffness of Perforated Masonry Walls

CivilEng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-67
Author(s):  
Mohsen Khaleghi ◽  
Javid Salimi ◽  
Visar Farhangi ◽  
Mohammad Javad Moradi ◽  
Moses Karakouzian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bearing Capacity ◽  
Structural Damage ◽  
Masonry Walls ◽  
Initial Stiffness ◽  
Load Bearing Capacity ◽  
Load Bearing ◽  
Artificial Neural ◽  
Mp Method

Perforations adversely affect the structural response of unreinforced masonry walls (UMW) by reducing the wall’s load bearing capacity, which can cause serious structural damage. In the absence of a reliable procedure to accurately predict the load bearing capacity and stiffness of perforated masonry walls subjected to in-plane loadings, this study presents a novel approach to measure these parameters by developing simple but practical equations. In this regard, the Multi-Pier (MP) method as a numerical approach was employed along with the application of an Artificial Neural Network (ANN). The simulated responses of centrally perforated UMW by the MP method were validated utilizing full-scale experimental walls. The validated MP model was used to generate a simulated database. The simulated database includes results of analyses for 49 different configurations of perforated masonry walls and their corresponding solid masonry walls. The effect of the area and shape of the perforations on the UMW’s behavior was evaluated by the MP method. Following the outcomes of the verified MP method, the ANN is trained to develop empirical equations to accurately predict the reduction in the load bearing capacity and initial stiffness due to the perforation of UMW. The results of this study indicate that the perforations have a significant effect on the structural capacity of the UMW subjected to in-plane loadings.

Assessment of Load-Bearing Capacity of Bridges

2017 ◽  
Vol 259 ◽  
pp. 113-118 ◽  
Author(s):  
Jaroslav Navrátil ◽  
Michal Drahorád ◽  
Petr Ševčík
Keyword(s):  
Bearing Capacity ◽  
Prestressed Concrete ◽  
Iterative Solution ◽  
Concrete Bridges ◽  
Load Bearing Capacity ◽  
Load Bearing ◽  
Prestressed Concrete Bridges ◽  
Existing Structures ◽  
Non Linear

The paper aims to the determination of load-bearing capacity of reinforced/prestressed concrete bridges subjected to the combination of all components of internal forces according to Eurocode standards for assessment of existing structures. Undoubtedly bridge load rating is laborious hand-iterative process, especially when it comes to reinforced and/or prestressed concrete bridges. The engineer can spend days and weeks trials and errors in the estimation of bridge load-carrying capacity. The problem lies in the determination of load-bearing capacity of cross-section subjected to the combination of normal and shear forces, bending and torsional moments. Due to the different effects of permanent and variable loads and the non-linear behavior of structural materials, the problem becomes non-linear and its solution requires the use of suitable iterative method. Optimized iterative solution was implemented into IDEA StatiCa software and the results are presented in this paper.

scholarly journals A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns

Molecules ◽  
2020 ◽  
Vol 25 (15) ◽  
pp. 3486 ◽  
Author(s):  
Quang Hung Nguyen ◽  
Hai-Bang Ly ◽  
Van Quan Tran ◽  
Thuy-Anh Nguyen ◽  
Viet-Hung Phan ◽  
...  
Keyword(s):  
Neural Network ◽  
Bearing Capacity ◽  
Hybrid Model ◽  
Steel Tube ◽  
Feedforward Neural Network ◽  
Load Bearing Capacity ◽  
Load Bearing ◽  
Concrete Filled Steel Tube ◽  
Model Based ◽  
One Step

In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of the FNN for developing a hybrid model (FNN-OSS). For achieving this goal, an experimental database containing 422 instances was firstly gathered from the literature and used to develop the FNN-OSS algorithm. The input variables in the database contained the geometrical characteristics of CFST columns, and the mechanical properties of two CFST constituent materials, i.e., steel and concrete. Thereafter, the selection of the appropriate parameters of FNN-OSS was performed and evaluated by common statistical measurements, for instance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In the next step, the prediction capability of the best FNN-OSS structure was evaluated in both global and local analyses, showing an excellent agreement between actual and predicted values of the load-bearing capacity. Finally, an in-depth investigation of the performance and limitations of FNN-OSS was conducted from a structural engineering point of view. The results confirmed the effectiveness of the FNN-OSS as a robust algorithm for the prediction of the CFST load-bearing capacity.

scholarly journals Influence of Bimoment Restraints on the Load-Bearing Capacity of a Steel I-Beam

2020 ◽  
Vol 30 (4) ◽  
pp. 33-47
Author(s):  
Krzysztof Wierzbicki
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Bearing Capacity ◽  
The Finite Element Method ◽  
Load Bearing Capacity ◽  
Geometric Imperfections ◽  
Load Bearing ◽  
Hinged Beams ◽  
Hot Rolled ◽  
Abaqus Software

Abstract The study presents an analysis of steel I-beam warping. The calculations were made for hot-rolled IPE200 hinged beams with different lengths. After determining load-bearing capacity using the GMNIA method, the beams were strengthened with bimoment restraints at each end. The changes in critical moment and load-bearing capacity were then evaluated. The study presents the manner in which the material and geometric imperfections have been determined. The GMNIA calculations were conducted using the Finite Element Method in Abaqus software. The results were then compared to results obtained with traditional methods and acquired from LT Beam software.

scholarly journals Evaluation of Progressive Collapse Performance in Double layer Diamatic Domes

2019 ◽  
Vol 5 (10) ◽  
pp. 2167-2179
Author(s):  
Javid Rezania ◽  
Peyman Torkzadeh
Keyword(s):  
Bearing Capacity ◽  
Static Analysis ◽  
Double Layer ◽  
Progressive Collapse ◽  
Initial Imperfection ◽  
Local Area ◽  
Load Bearing Capacity ◽  
Load Bearing ◽  
Non Linear ◽  
Linear Static Analysis

Double-layer spatial domes are one of the most common spatial structures, the stability and progressive collapse of which are of great importance in design, construction and maintenance of such special structures. In this paper considering three loading cases and two types of support conditions, the collapse behaviour of double layer Diamatic dome has been investigated utilizing non-linear static analysis and alternate path method usage. In order to modelling compressive member behaviour, effective buckling modes have been obtained by eigenvalue buckling analysis for all of the members. Behaviour of compressive members has been obtained via definition of initial imperfection and non-linear static analysis. Riks arc-length method has been utilized for non-linear static analysis. The numerical results have indicated that reducing the number of the supports and focusing  of load in a local area of the dome extremely impact on its vulnerability to failure, as in similar loading condition, decreasing the number of the supports reduces the capacity of damage resistance in spatial domes up to 50 percent. Investigating some models has shown that removing the critical members of the top layer has little effect on load-bearing capacity of the dome and it causes a slight failure in the structure. In this condition, structural redundancy can be considered equal to static indeterminacy. Load bearing capacity of the structure decreased up to 39 percent when compressive members of the web and bottom layers were removed. In this condition, the structure failure is considered moderate.

scholarly journals Surrogate Neural Network Model for Prediction of Load-Bearing Capacity of CFSS Members Considering Loading Eccentricity

2020 ◽  
Vol 10 (10) ◽  
pp. 3452 ◽  
Author(s):  
Tien-Thinh Le
Keyword(s):  
Neural Network ◽  
Bearing Capacity ◽  
Steel Tube ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Load Bearing Capacity ◽  
Load Bearing ◽  
Hollow Section ◽  
Following Error ◽  
Loading Eccentricity

In this study, a surrogate Machine Learning (ML)-based model was developed, to predict the load-bearing capacity (LBC) of concrete-filled steel square hollow section (CFSS) members, considering loading eccentricity. The proposed Artificial Neural Network (ANN) model was trained and validated against experimental data using the following error measurement criteria: coefficient of determination (R2), slope of regression, root mean square error (RMSE) and mean absolute error (MAE). A parametric study was conducted to calibrate the parameters of the ANN model, including the number of neurons, activation function, cost function and training algorithm, respectively. The results showed that the ANN model can provide reliable and effective prediction of LBC (R2 = 0.975, Slope = 0.975, RMSE = 294.424 kN and MAE = 191.878 kN). Sensitivity analysis showed that the geometric parameters of the steel tube (width and thickness) and the compressive strength of concrete were the most important variables. Finally, the effect of eccentric loading on the LBC of CFSS members is presented and discussed, showing that the ANN model can assist in the creation of continuous LBC maps, within the ranges of input variables adopted in this study.

Improving double-layer space trusses collapse behavior by strengthening compression layer and weakening tension layer members

2017 ◽  
Vol 20 (11) ◽  
pp. 1757-1767 ◽  
Author(s):  
Saman Rashidyan ◽  
Mohammad-Reza Sheidaii
Keyword(s):  
Bearing Capacity ◽  
Double Layer ◽  
Progressive Collapse ◽  
Load Bearing Capacity ◽  
Load Bearing ◽  
Space Truss ◽  
Geometrical Imperfections ◽  
Collapse Behavior ◽  
Compression Layer ◽  
Space Trusses

Progressive collapse is a chain of local failures leading to the collapse of either the entire or a part of the structure. The double-layer space trusses are susceptible to progressive collapse due to sudden buckling of compression members. The method of strengthening the compression layer members along with weakening the tension layer members is an effective method for retrofitting the double-layer space truss behavior against progressive collapse. In this study, the method is applied on offset double-layer space truss models with different support conditions, members’ geometrical imperfections, height, and shapes, and the effectiveness in increasing the structure’s ductility and load-bearing capacity is demonstrated. The results show that the method converted the sudden collapse of the structures into a beneficial gradual (progressive) collapse. More specifically, for double-layer space trusses comprising members with similar geometrical imperfection, strengthening the compression layer chords along with weakening the tension layer chords within 30%–40% will significantly improve the ductility and load-bearing capacity. In addition, the results show that the method can decrease the weight of the structures and consequently provide more economical structures.

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