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Due to many differences in the material, geometry, and assembly method of the commercially available beam-end-connectors in steel storage pallet racks (SPR), no common numerical model has been universally accepted to accurately predict the M–θ behavior of complex semirigid connections so far. Despite the fact that the finite element method (FEM) an...
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Extended end-plate (EP) bolted connections are widely used in steel structures as moment-resisting connections. Most of these connections are semi-rigid or in other words flexible. The paper aims to study the behavior of such connections under the effect of column top-side cyclic loading using the finite element (FE) method. For semi-rigid connecti...
Citations
... The majority of ML algorithms considered in this work were employed for structural concrete: support vector machines (SVM) were studied in [35][36][37][38][39][40][41], decision trees (DT) in [40,42], random forest (RF) in [40,[42][43][44][45], and k-nearest neighbors (KNN) in [37,40,42,46]. Steel and steel-concrete composite structures have also seen limited applications of SVM [47][48][49] and DT and KNN [50]. ...
Staggered rectangular perforations (slots) are provided in the webs of cold-formed steel (CFS) beams and columns to reduce their thermal conductivity and improve the energy efficiency of CFS buildings. The perforations adversely affect the structural characteristics of the members, especially those governed by the web parameters, such as the shear strength and shear buckling. This paper presents machine learning (ML) models to predict the elastic shear buckling load and the ultimate shear strength of CFS channels with slotted webs. Support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN) regressors were trained using a large dataset of numerical results with 3512 samples. An extensive search was conducted to find optimal hyperparameters of the models that result in the best predictions and prevent overfitting. The models’ performances were evaluated employing the ten-fold cross-validation method to make more data available for training and reduce bias and variance. The SVR, DT, and RF models demonstrate good prediction accuracy, which exceeds the accuracy of the existing descriptive equations. Relative feature importance was evaluated using the permutation and SHAP methods for each model. Partial dependence of the buckling load and the shear strength from the channel features was assessed. The predictions of the developed ML models were also compared with the predictions of previously developed artificial neural networks (ANNs). The comparisons demonstrated that ANNs showed higher accuracy in predicting the elastic buckling load, whereas the SVR model provided the most accurate shear strength predictions.
Machine learning (ML) has proven to be a powerful and efficient approach for solving complex engineering problems, which have been challenging for conventional methods. This chapter presents an overview of ML, including a discussion of items that must be addressed in ML model development, brief descriptions of popular ML algorithms and available open-source libraries, and options for ML model deployment. A review of publications on ML applications in CFS, spanning from 2000 to 2022, is provided. The review demonstrates that ML methods have been applied to many problems, including property predictions for materials, members, and connections, structural analysis, and quality control. However, the number of publications on ML applications to CFS remains relatively small compared with that for other materials. Practically all publications describe exploratory studies that have not found practical use in design and manufacturing due to the challenges discussed in the chapter. Future research directions, focused primarily on addressing the challenges, are also presented.
