An integrated ANN-GA for reliability based inspection of concrete

In most concrete bridge decks subject to deicing slats or constructed in chloride-ladenenvironments, corrosion has caused serviceability damage in the form of severe cracking and/or spallingof the concrete cover. in this paper, whilst an analytical model is used for the simulation of corrosioninduced crack width, random fields are utilized accounting for the spatial variability of the concretematerial and environmental factors. then, using the monte carlo simulation method, the extent of thedamage is simulated as a dependent random variable during the service life of the bridge deck. finally, forfinding optimum reliability-based inspection plans, with minimum life cycle costs, an integrated artificialneural network-genetic algorithm (ann-ga) approach is proposed. the applicability of this method isinvestigated on a hypothetical bridge deck. it is concluded that the employed approach can well handlethe high computational complexity of the problem in finding optimum inspection plans.