Issue 42

Correia et alii, Frattura ed Integrità Strutturale, 42 (2017) 136-146; DOI: 10.3221/IGF-ESIS.42.15

1.0E‐2

R=0.0 R=0.25 R=0.5 Mean curve ‐ Exp. Data

1.0E‐3

mean FCG curve + 2S mean FCG curve ‐ 2S Mean curve +2S: Stage B; BS7910 Mean curve: Stage B; BS7910

1.0E‐4

[mm/cycle]

1.0E‐5

1.0E‐6

100

500

1000 1500

2000

[N.mm ‐1.5 ]

Figure 7 : Statistical analysis of the FCG data for the material from the Trezói Bridge.

1.0E‐2

Luiz I Pinhão Eiffel Trezói Fão Mean curve ‐ Exp. Data

1.0E‐3

1.0E‐4

1.0E‐5 [mm/cycle]

mean FCG curve + 2S mean FCG curve ‐ 2S Mean curve +2S: Satge B; BS7910 Mean curve: Satge B; BS790

1.0E‐6

1.0E‐7

500

2000

1000

1500

100

[N.mm ‐1.5 ]

Figure 8 : Statistical analysis of the FCG data for all materials from ancient riveted bridges.

C ONCLUSIONS he statistical procedure used to analyse the experimental FCG data of the materials from the Portuguese ancient metallic bridges proved to be efficient. The design FCG curves proposed by BS7910 standard are not representative of all materials from old metallic bridges. The slopes of the design curves for the fatigue crack growth data of the materials from Eiffel, Luiz I and Fão bridges revealed to be similar. However, the slopes of the design curves for the FCG data of the materials from Pinhão and Trezói bridges exhibited a consistent behaviour and similar when compared with the slope of the design FCG curve proposed by BS 7910 standard. It should be noted that the latter materials are more recent and have mechanical properties similar to current constructional steels. Further statistical analysis of the experimental FCG data from old materials is therefore necessary to better represent their behaviour. A comparison between this statistical analysis for FCG curve using several pairs { * i C , * i m } and the probabilistic approaches for evaluating the FCG rates using local approaches should also be performed. T

A CKNOWLEDGEMENTS

T

he authors of this paper thank the SciTech-Science and Technology for Competitive and Sustainable Industries, R&D project NORTE-01-0145-FEDER-000022 co-financed by Programme Operational Regional do Norte ("NORTE2020") through Fundo Europe de Desenvolvimento Regional (FEDER) and the Portuguese Science

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