Numerous processing and manufacturing mills, workshops, warehouses, bridges and other industrial buildings belong to industrial heritage. Their origin dates back to the 19th and 20th century when cast iron became a widely used construction material. It has been recognised that existing structures including cast-iron structures do not fulfil requirements of present codes of practice. A key step of reliability assessment is modelling of resistance of load-bearing members made of cast iron. The present paper investigates several empirical or physical models for resistance of historic cast-iron columns. Outcomes of the models are critically compared with experimental results obtained for solid and hollow cylindrical, and square columns from English grey cast iron. Imprecision of the models is expressed by means of model uncertainty for which appropriate probabilistic models are proposed. As tensile strength of cast iron is considerably lower than compressive strength, it dominates resistances of columns centrically loaded in compression with slenderness ratio over 60. In such cases model uncertainty can be described by a two-parameter lognormal distribution with the mean of 1.25 and coefficient of variation of 0.15. For columns with lower slenderness ratios compressive strength is decisive and the mean of model uncertainty decreases to 1.2.
The paper focuses on the assessment of cast-iron columns in industrial heritage structures. For cast-iron structures it is difficult to verify metallurgical composition and processing technology, which directly affect the geometry of cross-sections. The crucial issue of reliability of cast-iron structures is their brittle fracture in tension at higher slenderness ratios. The load carrying capacity of columns is affected by their stability and cast-iron strength in compression and tension. Outcomes of two recently proposed analytical models are compared with extensive experimental data and model uncertainty is quantified. It appears that the model is in a good agreement with experimental data and provides reasonably conservative estimates of load carrying capacity. Outcomes of the study may provide basis for foreseen improvements in calculating buckling coefficient according to ČSN 730038:2014.
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