Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Abstract

In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a non-contact displacement sensor is placed on a limited number of spots such as foundations of the structures, and the sensor must have a very long measurement distance (typically longer than 100 m). RTK-GNSS, therefore, has been widely used in displacement measurement on civil infrastructures. However, RTK-GNSS has a low sampling frequency of 10–20 Hz and often suffers from its low stability due to the number of satellites and the surrounding environment. The proposed method combines data from an RTK-GNSS receiver and an accelerometer to estimate the dynamic displacement of the structure with higher precision and accuracy than those of RTK-GNSS and 100 Hz sampling frequency. In the proposed method, a heuristic drift reduction method estimates displacement with better accuracy employing a low-pass-filtered acceleration measurement by an accelerometer and a displacement measurement by an RTK-GNSS receiver. Then, the displacement estimated by the heuristic drift reduction method, the velocity measured by a single GNSS receiver, and the acceleration measured by the accelerometer are combined in a two-stage Kalman filter to estimate the dynamic displacement. The effectiveness of the proposed dynamic displacement estimation method was validated through three field application tests at Yeongjong Grand Bridge in Korea, San Francisco–Oakland Bay Bridge in California, and Qingfeng Bridge in China. In the field tests, the root-mean-square error of RTK-GNSS displacement measurement reduces by 55–78 percent after applying the proposed method.

The Concept of Creating Digital Twins of Bridges Using Load Tests

“…Due to the scope, amount, and complexity of the data, this comparison is usually carried out using automatic or semi-automatic systems based on machine learning and artificial intelligence algorithms [57][58][59]. It may include damage recognition based on the computer vision [60,61], structural condition assessment by pattern recognition and detection of anomalies [62], surrogate model-based reliability analysis [63], and dynamic measurement of displacement [64]. The results and decisions derived from the analysis are visualized in the as-built BIM model.…”

Sensing Mechanism and Real-Time Bridge Displacement Monitoring for a Laboratory Truss Bridge Using Hybrid Data Fusion

“…While this approach showed promise, it required manual adjustment of the filter parameters, which limited its utility for automation. Kim et al [25] proposed a displacement estimation system based on the fusion of an accelerometer and a RTK-GNSS. However, low-frequency noise in a RTK-GNSS signal was observed, which caused a high level of estimation errors in the results, and using RTK-GNSS makes it hard to monitor the displacement of a bridge in real time.…”

Validity and Reliability of an Instrumented Treadmill with an Accelerometry System for Assessment of Spatio-Temporal Parameters and Impact Transmission

“…Accelerometers are today a proven and low-cost technology used for displacement estimation [ 32 ] in a wide range of applications, such as electrohydraulic systems [ 33 ], architecture, civil engineering [ 34 , 35 ], seismology [ 36 ], or even astronomy [ 37 ]. In all these applications, the accelerometers are rigidly attached to the element whose displacement is to be monitored, and with an analysis of the accelerometry signals, the motion and other parameters of interest can be estimated.…”