Remaining fatigue life assessment of in-service road bridge decks based upon artificial neural networks

Fatigue Life of RC Bridge Decks Affected by Non-Uniformly Dispersed Stagnant Water

“…2019, 9, 607 5 of 17 amount of RC decks, which are currently problematic in maintenance due to short fatigue life, were designed as one-way slab supported by side girders, while their length generally depends on several conditions such as the span of adjacent girders. In a previous research study [26], 6.0 meters' length was selected as a reference to represent a large amount of bridge infra-stocks in Japan. Thus, the authors select the same target as well.…”

“…The simulation model is discretized with finite elements by using the open code program "FABriS" [26], which is specialized for the wheel-type moving load on RC bridge decks. Mesh size is chosen to be 250 × 250 mm in the x-y plane, and the number of layers in the z-direction is four as shown previously in Figure 5.…”

“…The fatigue life of the referential RC deck in dry conditions and fully submerged was investigated in previous research [17,26,31]. However, this study targets the effect of wetting locations only at the upper entire surface layer of the RC deck.…”

“…On the other hand, the impact of stagnant water is significantly reduced, when the wetting locations are near the sides of the deck, away from the wheel loading path, as the following cases: (4-9), (17)(18)(19), and (26). It is demonstrated that the negative impact of the stagnant water on the fatigue life is reduced as the wetting locations start to be farther from the central zone of the wheel-loading path, as shown in Figure 16, where X is the longitudinal distance of the central wetting location to the center of the RC deck.…”

“…For other RC decks, whose dimensions and material properties differ from the referential RC deck, the sensitivity of the proposed correlation will be checked in the near future regarding its generalization to deal with any RC deck. Then, as a next future step, the authors aim to couple the introduced predictive model in this study with our previous proposed artificial intelligence (AI) models that deal with fatigue life prediction based on site-inspected cracks [17,26,31].…”