The modern construction industry widely uses reinforced concrete structures, where high-strength prestressing strands are used. Key parameters determining strength and relaxation resistance are a steel microstructure and internal stresses. The aim of the work was a computer research of a stage-by-stage formation of internal stresses during production of prestressing strands of structure 1х7(1+6), 12.5 mm diameter, 1770 MPa strength grade, made of pearlitic steel, as well as study of various modes of mechanical and thermal treatment (MTT) influence on their distribution. To study the effect of every strand manufacturing operation on internal stresses of its wires, the authors developed three models: stranding and reducing a 7-wire strand; straightening of a laid strand, stranding and MTT of a 7-wire strand. It was shown that absolute values of residual stresses and their distribution in a wire used for strands of a specified structure significantly influence performance properties of strands. The use of MTT makes it possible to control in a wide range a redistribution of residual stresses in steel resulting from drawing and strand laying processes. It was established that during drawing of up to 80% degree, compressive stresses of 1100-1200 MPa degree are generated in the central layers of wire. The residual stresses on the wire surface accounted for 450-500 MPa and were tension in nature. The tension within a range of 70 kN to 82 kN combined with a temperature range of 360-380°С contributes to a two-fold decrease in residual stresses both in the central and surface layers of wire. When increasing temperature up to 400°С and maintaining the tension, it is possible to achieve maximum balance of residual stresses. Stranding stresses, whose high values entail failure of lay length and geometry of the studied strand may be fully eliminated only at tension of 82 kN and temperature of 400°С. Otherwise, stranding stresses result in opening of strands.
Fracture acoustic emission signals identification of stay cables in bridge engineering application using deep transfer learning and wavelet analysis
