HHT-Based Probabilistic Model of Prestressed Bridges Inferred From Traffic Loads

Abstract

Prestressed bridges’ performance is strongly dependent on the health state of their prestressing cables, but unfortunately, these structural components are hidden and cannot be assessed through visual inspections. Moreover, conventional low-energy methods, like operational modal analysis, are inadequate due to their inability to detect the nonlinear effects of the prestressing force on the response under heavy travelling loads. In this paper, a methodology exploiting the Hilbert–Huang transform (HHT) is investigated in which the bridge’s nonlinear constitutive force–displacement relationship can be reconstructed by analysing the traffic-induced dynamic response, which has the features of a short-time nonstationary and potentially nonlinear signal. HHT, thanks to its adaptability to complex behaviours, is suitable for treating such type of signals and makes it possible to trace the response properties at each time instance, thus allowing to correlate instantaneous values of deformation with the simultaneous instantaneous (tangent) stiffness in a one-to-one relationship. Starting from a previous introductory study, and with the aim of making the proposed approach suitable for real structural health monitoring applications, a comprehensive investigation is performed considering a bridge with dynamical properties in the range of interest and realistic traffic scenarios adequately describing the time series of travelling loads and relevant internal actions. In particular, three main issues are considered: (i) development of a refined probabilistic response model (to be inferred from data collected under service loads) capable to overcome troubles induced by the nonhomogeneous distributions of data, generally consisting of frequent passages of light vehicles and rare passages of heavy vehicles; (ii) convergence analysis aimed at providing a relationship between the duration of the training period and the accuracy expected to infer the probabilistic model; and (iii) proposal and validation of a novel procedure to derive constitutive model of the bridge exploiting only deformation data recorded during vehicle passages and provide a tool for relating prestressing losses to variations in the dynamic response. The outcomes prove the potential of the proposed strategy paving the way for real-world experimental applications.