Abstract
Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localised and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to estimate the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.
Highlights
The use of cellular beams in the construction sector has significantly increased over the past decade on account of the distinct and discreet advantages they offer
This paper proposes an artificial neural network (ANN)-based formula to estimate the critical elastic buckling load of supported cellular beams under uniformly distributed vertical loads, as function of eight independent geometrical parameters
‘reliable’, since results used for comparison are based on target and output datasets as used in ANN training and yielded by the designed network, respectively
Summary
The use of cellular beams (i.e., perforated beams with circular web openings) in the construction sector has significantly increased over the past decade on account of the distinct and discreet advantages they offer. The perforations lead to complex structural behaviours, attributed to the distribution of forces and stresses in the vicinity of the openings. This results in rather complicated and conservative design procedures (Morkhade and Gupta 2015, Akrami and Erfani 2016). Concerning functional approximation, ANN-based solutions are frequently more accurate than those provided by traditional approaches, such as multi-variate nonlinear regression, besides not requiring a good knowledge of the function shape being modelled (Flood 2008)
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