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Welded hollow spherical joints (WHSJs) are commonly used in reticulated shell structures. Corrosion on the surface of WHSJs can remarkably reduce their compression capacity. Pitting corrosion is a typical corrosion type on steel structures. Artificial neural network (ANN) is utilized to predict the compression capacity of WHSJs with random corrosion. Corrosion occurring at different positions can affect the compression capacity in different degrees. The spherical body of WHSJs is divided into several parts, and the mass loss ratio  χ  is utilized as the representation of corrosion severity. The influences of the number of divided corrosion locations and the probabilistic distribution of  Tc / T  on prediction accuracy is investigated in this study. The applicability of trained ANN for WHSJs with different geometric sizes is also validated. Results indicated that ANNs can be utilized for predicting compression capacity with high accuracy, and the mass loss ratio can be used as the input variable.

Predictions of compression capacity of randomly corroded WHSJs based on artificial neural network