Narrow Results

Simultaneous identification of vehicle load and structural damage is an issue of practical significance in structural health monitoring and maintenance of in-service concrete bridges. However, most methods proposed in history to deal with this engineering issue are solely based on data from a single source of structural vibration response measurement, which are subjected to problems such as incomplete understanding of the structural health, the computational inefficiency and the easy failure of the monitoring system. To this end, this paper proposes a new method of synthesizing data generated from two different types of sensors (the strain gauges and the accelerometers) to simultaneously identify the vehicle load and the structural damage which is established on the theories of weigh-in-motion method based on the strain influence line. The new method, which is supposed to be able to well solve the problems with the traditional approaches disseminated to the profession, is formulated in a flowchart for use, and applied to the actual Renyihe Bridge (a concrete rigid-frame continuous highway bridge with spans of 80 m + 4 × 145 m + 80 m) to validate its effectiveness. The results suggest that the new method is of high accuracy in use in low vehicle speed scenarios and superior to the traditional simultaneous identification approach based on unitary structural acceleration sensing.

The vehicle overload phenomenon in expressway has become increasingly serious in China. Research on the extreme value of the vehicle load with its development trend is an urgent problem in the toll-by-weight mode. On the basis of statistical analysis of WIM data in Beijing-Zhuhai Expressway, by the extreme value theory and POT theory, a POT model was developed. Vehicle weight distribution in the tail of the distribution function was obtained, which can predict the extreme value of vehicle load in any return period. Results demonstrated that more heavy vehicles will appear in the future. It provided a useful reference for effective forecast of overweight vehicles.