Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings

Almasabha, Ghassan; Alshboul, Odey; Shehadeh, Ali; Almuflih, Ali Saeed

doi:10.3390/buildings12060775

Cited by 35 publications

(12 citation statements)

References 47 publications

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“…It should be noted that for the empirical correlation equation of yield strength, the coefficient β 2 may not necessarily exist, depending on the differences in the mechanical properties of the material and the influence of the test apparatus. Table 7 combined with Equations (17) and (18) shows that the values of β 1 determined by the Mao and CEN methods are close to the values in the literature [ 5 , 32 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Therefore, by combining the two perspectives above, F y_Mao and F y_CEN may be the more desirable characteristic forces for the three materials of interest in this study.…”

Section: Discussionsupporting

confidence: 79%

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Mechanical Properties Study of Miniature Steel Specimens Based on the Small Punch Test and Simulation Methods

Zhang

Guo²,

Liu³

2022

Materials

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The small punch test (SPT) can be very convenient to obtain mechanical properties due to its unique advantages from small-volume samples, and has gained wide popularity and appreciation among researchers. In this paper, the SPT test and finite element (FE) simulations were performed for three alloys, and the yield stresses (σYS) and ultimate tensile strengths (σUTS) from the uniaxial tensile test (UTT) were correlated with the yield force (Fy) and maximum force (Fm) of the small punch test (SPT) before and after compliance calibration. Finally, the effect of specimen size on the SPT curves was discussed. The results showed that the deviation between SPT test and FE simulation was due to the loading system stiffness, which was confirmed by the loading system compliance calibration test. The SPT curves before and after calibration have less influence on the empirical correlation results for σUTS, while the correlation results for σYS depend on the method used to determine Fy in the SPT curve. Finally, the simulation results indicated that the effect of specimen size on the force–displacement curve in the SPT is slight. This work also provides a reference for subsequent researchers to conduct empirical correlation studies using different specimen sizes.

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Section: Discussionsupporting

confidence: 79%

“…The mesh refinement was performed to ensure the accuracy of simulation results. The mesh sizes of square and round specimens in the 3D model were consistent to avoid the impact of mesh sensitivity on simulation results [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Mechanical Properties Study of Miniature Steel Specimens Based on the Small Punch Test and Simulation Methods

Zhang

Guo²,

Liu³

2022

Materials

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“…In this study, the tansig and purelin functions were employed for making a smooth transition during training the network [107], expressed by Equations ( 7) and ( 8). This approach is also consistent with studies elsewhere [108][109][110][111][112][113][114][115][116][117].…”

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confidence: 91%

Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

et al. 2022

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The determination of shear strength and the identification of potential failure modes are the crucial steps in designing and evaluating the structural performance of reinforced concrete (RC) columns. However, the current design codes and guidelines do not clearly provide a detailed procedure for governing failure types of RC columns. This study predicted the shear strength and identified the failure modes of rectangular RC columns using various Machine Learning (ML) models. Six ML models, including Multivariate Adaptive Regression Splines (MARSs), Naïve Bayes (NBs), K-nearest Neighbors (KNNs), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to calculate the shear strength and to classify the failure modes of rectangular RC columns. A total of 541 experimental data samples were collected from literature and utilized for developing the ML models. The results reveal that the ANN and KNNs models outperformed other ML models in predicting the shear strength of rectangular RC columns with the value larger than 0.99. Additionally, the KNNs model achieved the highest accuracy, mostly 100%, for identifying the failure modes of rectangular RC columns. Based on the superior performance of the ANN and KNNs models, a graphical user interface was also developed to rapidly predict the shear strength and failure modes of rectangular RC columns.

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“…For example, machine and deep learning-based technological tools were used in [20] for providing a robust, fast, accurate, and flexible forecasting framework for the prediction influence of concrete mix properties on the shear strength of slender structured concrete beams without stirrups. In addition, in [21], machine learning algorithms were used in order to predict the shear strength accurately, which enable the automation to be applied in steel buildings.…”

Section: Introductionmentioning

confidence: 99%

Post-Buckling Behaviour of Steel Structures with Different Types of Imperfections

Rzeszut

2022

Applied Sciences

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In this paper, the stability of steel members with a complex initial geometrical imperfection pattern are analysed. This issue is extremely important in the case of slender structures, characterised by multiple close critical loads and modal interactions, which can lead to unstable post-critical paths and imperfection sensitivity. Despite the fact that the loss of stability, as a result of complex geometrical imperfections, is a very common mechanism for the destruction of slender steel structures, there is still no unambiguous and adequate research in the literature and in scientific research taking into account multimodal buckling. Therefore, in this study, special attention was focused on the analysis of the equilibrium path of the structure in the pre- and post-buckling range. This was studied by introducing a model of a structure composed of four rigid bars connected by elastic nodes. For this model, as well as for the structure with and without initial geometrical imperfections, a set of nonlinear algebraic equations of equilibrium was developed. A complex pattern of imperfections was taken into account using a linear superposition of buckling modes obtained from a linear eigenvalue problem. In order to investigate the nature of bifurcation points, the concept of minimum of potential energy was adopted. By means of numerous examples, the influence of imperfections on the structural behaviour was discussed. It was found that, for special imperfection patterns, an increase in the amplitude of initial geometrical imperfection can result in an increase in the value of the critical load defining the bifurcation point. In these cases, initial geometrical imperfections can play a positive role, resulting in stable post-buckling behaviour. This phenomenon corresponds with the so-called “modal nudging” which aims to improve the buckling response of slender elastic structures by introducing a small disturbance in the primary geometry of the structure, which results in equilibrium paths of greater load-carrying capacity. Among other observations, a snap-through phenomenon caused by transition from the local to the global minimum of potential energy was also noted. The observed snap-through was caused by the specific configuration of initial geometrical imperfections, which in this case played quite a dangerous role. It should be emphasised that the proposed model structure allows for a full description of the post-critical behaviour and a trace of the influence of complex imperfection configurations in a simple and clear manner.

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Machine Learning Algorithm for Shear Strength Prediction of Short Links for Steel Buildings

Cited by 35 publications

References 47 publications

Mechanical Properties Study of Miniature Steel Specimens Based on the Small Punch Test and Simulation Methods

Mechanical Properties Study of Miniature Steel Specimens Based on the Small Punch Test and Simulation Methods

Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

Post-Buckling Behaviour of Steel Structures with Different Types of Imperfections

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