Advanced inelastic analysis of steel structures at elevated temperatures by SCM/RPHM coupling

Barros, Rafael C.; Pires, Dalilah; Silveira, Ricardo Azoubel da Mota; Lemes, Ígor José Mendes; Rocha, Paulo Anderson Santana

doi:10.1016/j.jcsr.2018.03.001

Cited by 17 publications

(18 citation statements)

References 10 publications

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“…The present study applied the CS-ASA/FA and CS-ASA/FSA computational modules to composite steel-concrete structures in fire condition. This modules efficiency had already been tested with steel frames and reinforced concrete frames under elevated temperature situations, where good results were observed in both cases [13,14]. This work expand the fire analysis to composite structures.…”

Section: Composite Steel-concrete Framementioning

confidence: 63%

Advanced Numerical Study of Composite Steel‐Concrete Structures at High Temperature

Barros¹,

Silveira²,

Pires³

et al. 2021

ce papers

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The composite steel‐concrete structures use has several advantages, such as the reduction of cross‐sectional dimensions and weight of the structure, which is one of the main reasons for it is use today. However, under fire situation, the material and mechanical properties changes, causing significant strength and stiffness loss as a result of temperature rise. In this work, the temperature influence on the behavior of composite steel‐concrete structures is studied through an inelastic second order (ISO) numerical investigation. For this, two computational modules, CS‐ASA/FA and CS‐ASA/FSA are developed and adapted for the study of composite structures in fire. The first module calculates the temperature field in any cross‐section. The second module performs the ISO analysis through the coupling between the Refined Plastic Hinge Method (RPHM) and the Strain Compatibility Method (MCD). In this way, the evolution of the temperature in cross‐sections, the interaction diagrams between axial force and bending moment and the structures equilibrium path as a function of the time in fire are presented for composite steel‐concrete beams, columns and frames. The proposed numerical methodology success is proved by comparison with experimental and numerical responses available in the literature.

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Section: Composite Steel-concrete Framementioning

confidence: 63%

Advanced Numerical Study of Composite Steel‐Concrete Structures at High Temperature

Barros¹,

Silveira²,

Pires³

et al. 2021

ce papers

Self Cite

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“…...... w 11 (1) w 11 (2) w IJ (1) w JK (2) w KH (3) w 11 (3) Input layer Hidden layer 1 Output layer Hidden layer 2 Figure 2. An FNN with two hidden layers layer to the output layer.…”

Section: Feedforward Neural Networkmentioning

confidence: 99%

“…Obviously, these methods do not consider directly structural nonlinear behaviors and their member separate check cannot make sure the compatibility between the members and whole structure. Therefore, although these methods yield acceptable solutions for design of structure and save lots of computational efforts, they have been gradually being replaced by advanced analysis methods [1][2][3][4] which can account for geometric and material nonlinearities directly and model complex contact conditions. Advanced analysis methods can also predict = asymptotic lower stress limit.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based procedure for estimation of ultimate load carrying of steel trusses using advanced analysis

Truong

Viet

Thuat

2019

STCE

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In the present study, Deep Learning (DL) algorithm or Deep Neural Networks (DNN), one of the most powerful techniques in Machine Learning (ML), is employed for estimation of ultimate load factor of nonlinear inelastic steel truss. Datasets consisting of training and test data are created based on advanced analysis. In datasets, input data are the member cross-sections of the truss members and output data is the ultimate load factor of the whole structure. An example of a planar 39-bar steel truss is studied to demonstrate the efficiency and accuracy of the DL method. Five optimizers such as Adadelta, Adam, Nadam, RMSprop and SGD and five activation functions such as ELU, LeakyReLU, Sigmoid, Softplus, and Tanh are considered. Based on analysis results, it is proven that DL algorithm shows very high accuracy in the regression of the ultimate load factor of the planar 39-bar nonlinear inelastic steel truss. The number of layers can be selected with a small value such as 1, 2 or 3 layers and the number of neurons in each layer can be chosen in the range [N i , 3N i ] with N i is the number of input variables of the model. The activation functions ELU and LeakyReLU have better convergence speed of the training process compared to Sigmoid, Softplus and Tanh. The optimizer Adam works well with all activation functions considered and produces better MSE values regarding both training and test data. 113Hung, T. V., et al. / Journal of Science and Technology in Civil Engineering the load-carrying capacity of whole structure that allows elimination of the tedious individual member check approach used in the classical methods. However, advanced analysis methods are excessive computing times to solve the design problems which require lots of structural analyses such as optimization or reliability analysis of the structure [5][6][7][8]. In such cases, using metamodels based on machine learning (ML) techniques are considered as an efficient solution.Metamodel is an approximate mathematical representation used to perform the complicated relationship between input and output data. In light of this, nonlinear inelastic responses of the structure are predicted without performing advanced analysis. Some popular ML methods are Support Vector Machine (SVM) [9], Kriging [10], Random Forest (RF) [11], Gradient Tree Boosting (GTB) [12], Decision Tree (DT) [13], and so on. The applications of ML methods into structural design are quite diverse but focused primarily on damage detection [14,15] and health monitoring [16,17]. Besides, researchers have been applying ML methods for structural optimization [18], reliability analysis [19], prediction of structural ultimate strength [20], etc.The performance of traditional ML methods largely depends on the data representation choice of the users since these methods cannot automatically detect the representations or features needed for classification or detection from the raw input data. The pattern-recognition often requires complex techniques with high expertise. Therefore, using ML methods is c...

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“…It is also worth mentioning that the thermal and mechanical properties of steel and concrete in fire conditions are adopted according to normative specifications. 17,18 Further details of this computa-tional module are in Maximiano 19 and Barros et al 14…”

Section: Computational System For Advanced Structural Analysis/fire Analysismentioning

confidence: 99%

“…The first one, called CS-ASA/Fire Analysis (CS-ASA/FA) 12,13 is able to determine the temperature field in the cross-section of structural elements through thermal analysis via Finite Element Method (FEM) in permanent and transient regimes. The second module, CS-ASA/Fire Structural Analysis (CS-ASA/FSA), [14][15][16] was developed to perform a second order inelastic analysis of structures under high temperatures. Thus, a SCM-based approach is proposed for the evaluation of the cross-sectional strength, axial stiffness and flexural strength of reinforced concrete beams under high temperatures.…”

Section: Introductionmentioning

confidence: 99%

Thermo-structural analysis of reinforced concrete beams

et al. 2019

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The objective of this study is to simulate the behavior of reinforced concrete beams in fire situation. In order to achieve this objective, advanced numerical formulations were developed, implemented and evaluated. When exposed to high temperatures, the properties of the material deteriorate, resulting in the loss of strength and stiffness. To achieve the goal, two new modules within the Computational System for Advanced Structural Analysis were created: Fire Analysis and Fire Structural Analysis. The first one aims to determine the temperature field in the cross section of structural elements through thermal analysis by using the Finite Element Method (FEM). The second was designed to perform the second-order inelastic analysis of structures under fire using FEM formulations based on the Refined Plastic Hinge Method coupled with the Strain Compatibility Method. The results obtained of the nonlinear analyses of two reinforced concrete beams under high temperature were compared with the numerical and experimental solutions available in literature and were highly satisfactory. These results also showed that the proposed numerical approach can be used to study the progressive collapse of other reinforced concrete structures in fire situation and extended to the numerical analysis of composite structures under fire condition.

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Advanced inelastic analysis of steel structures at elevated temperatures by SCM/RPHM coupling

Cited by 17 publications

References 10 publications

Advanced Numerical Study of Composite Steel‐Concrete Structures at High Temperature

Advanced Numerical Study of Composite Steel‐Concrete Structures at High Temperature

A deep learning-based procedure for estimation of ultimate load carrying of steel trusses using advanced analysis

Thermo-structural analysis of reinforced concrete beams

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