Soft Computing Based Multilevel Strategy for Bridge Integrity Monitoring

Abstract: In recent years, structural integrity monitoring has become increasingly important in structural engineering and construction management. It represents an important tool for the assessment of the dependability of existing complex structural systems as it integrates, in a unified perspective, advanced engineering analyses and experimental data processing. In the first part of this work the concepts of dependability and structural integrity are discussed and it is shown that an effective integrity assessment needs advanced computational methods. For this purpose, soft computing methods have shown to be very useful. In particular, in this work the neural networks model is chosen and successfully improved by applying the Bayesian inference at four hierarchical levels: for training, optimization of the regularization terms, data‐based model selection, and evaluation of the relative importance of different inputs. In the second part of the article, Bayesian neural networks are used to formulate a multilevel strategy for the monitoring of the integrity of long span bridges subjected to environmental actions: in a first level the occurrence of damage is detected; in a following level the specific damaged element is recognized and the intensity of damage is quantified.