Abstract
The behavior of beam-to-column connections significantly influences the stability, strength, and stiffness of steel structures. This is particularly important in extreme non-elastic responses, i.e., earthquakes, and sudden column removal, as the fluctuation in strength and stiffness affects both supply and demand. Accordingly, it is essential to accurately estimate the strength and stiffness of connections in the analysis of and design procedures for steel structures. Beginning with the state-of-the-art, the capacity of three available component-based mechanical models to estimate the complex mechanical properties of top- and seat-angle connections with double-web angles (TSACWs), with variable parameters, were investigated. Subsequently, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental dataset. Using several statistical metrics, including the corresponding coefficient of variation (CoV), correlation coefficient (R), and the correlation coefficient provided by the Taylor diagram, this study revealed that the krill herd-ANN model achieved the most reliable predictive accuracy for the strength and stiffness of top- and seat-angle connections with double web angles.
Highlights
Steel beam-to-column connections are a fundamental component of steel structures, and their performance affects the overall structural behavior
The results show that the ratio of the test’s coefficient of variation to the theoretical values was estimated to be 0.97 and 0.98 for Sj,ini and Mn parameters, respectively, by the krill herd algorithmartificial neural network (KHA-artificial neural network (ANN)) model, indicating its superior accuracy to the other models considered in this study
In the second phase of research, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental test dataset
Summary
Steel beam-to-column connections are a fundamental component of steel structures, and their performance affects the overall structural behavior. Bolted top- and seat-angle connections without (TSACs) or with web angles (TSACWs) have been extensively used in steel and composite structures because of their relatively high moment capacity and easy construction These types of connections are mainly designed to resist gravity loads of determinate steel beams. Examples of simplified methods are analytical and empirical models where predictions will be made by determining key parameters (e.g., moment capacity, initial stiffness, etc.) and fitting a skeleton curve over these particular points [23] In such an approach, the fundamental parameters can be extracted from experimental test data and represented with simple expressions, including polynomials, power functions, or a combination of these two expressions. Multiple linear regression and a genetic algorithm combined with an ANN model, were developed to evaluate the accuracy of the proposed KHA-ANN model
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