Bridge pier scour level quantification based on output-only Kalman filtering

Abstract

Soil scour near a bridge pier foundation is one of the leading causes of bridge failures. Traditional vibration-based scour monitoring methods are nearly incapable of quantifying scour levels using a single acceleration response without knowledge of excitation information. In this paper, a new output-only scour level prediction method is introduced via the integration of an unscented Kalman filter (UKF), random decrement (RD), and newly derived continuous Euler–Bernoulli beam addressing river water, traffic loads, and the linear and nonlinear behavior of sediments around the pier as external effects. We conducted extensive simulation studies and applied the method to an existing medium-span bridge with a steel girder and concrete deck in service in the province of Manitoba, Canada. These studies show that our proposed method can accurately estimate scour levels using only one accelerometer, which was validated with an independent bathymetric survey of the soil level at the pier foundation. Furthermore, three different linear and nonlinear soil profiles representing the soil behavior around the pier were also investigated as case studies in the scour level estimation process. The results confirm that a cubic function exhibits the best performance in quantifying the scour level around bridge piers.

Time‐varying reliability analysis based on hybrid Kalman filtering and probability density evolution

“…Several factors contribute to this overestimation. In this study, one primary factor is attributed to the energy associated with stiffness values, as opposed to damping values as well as the time integration approach (Euler scheme) chosen in the prediction step of the UKF 26,46,47 …”

“…In this study, one primary factor is attributed to the energy associated with stiffness values, as opposed to damping values as well as the time integration approach (Euler scheme) chosen in the prediction step of the UKF. 26,46,47 The estimated displacement and velocity are shown in Figure 5, and Figure 6 shows the estimated stiffness and damping of the 3-DoF system with a blue dashed color over time along with the different damage scenarios from Table 1.…”

“…Structural health monitoring (SHM) methods are defined as a process to identify damage or abnormal symptoms in a structure and predicting the predict of the remaining safe life span of the structure 1,2 . Damage identification is a wide‐ranging term that includes detection, localization, and quantification of the level of damage in structures 3 .…”

“…In the last decade, different Kalman filtering methods have received considerable attention for their potential usefulness in damage identification in the field of SHM 2,19 . Wu 20 demonstrated the capability of the UKF for the system identification of linear and nonlinear structures.…”

Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures

“…Shrinkage is the change in volume observed in concrete associated with water losses and capillary forces [ 1 , 2 , 3 ]. Accurate prediction of these rheological effects is important for distinguishing expected structural behavior from long-term damage processes, such as corrosion [ 4 ], differential settlement [ 5 , 6 ], or scouring [ 7 ], the latter a leading cause of collapse of structures in North America [ 8 ], as well as for improved assessment of prestress losses and long-term performance of civil structures [ 9 , 10 ].…”