This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

In this research, a T-type Perfobond rib shear connector for steel concrete composite bridges is suggested, and a series of push-out tests are performed with numerous parameters, such as length and thickness of the ribs, diameter and number of the holes in the ribs, number of transversal reinforcing steel bars, and strength of concrete. Based on the test results, the effect of each parameter is analysed. Also, a shear strength evaluation formula is suggested for a 400 mm long T-type Perfobond rib shear connector. Comparison of the test results and the calculation results, using the suggested formula, validates that the suggested formula rationally evaluates the effect of considering parameters.

In Korea, there has been a wide consensus about the necessity of bridge monitoring systems. This is primarily due to a chain of accidental bridge collapses over the past decade. The first generation bridge monitoring system originated from an instrumentation system introduced upon the reconstruction of the New-Haengju Bridge after its sudden collapse in 1995. After that, on-line instrumentation systems were installed in existing cable-supported bridges including Namhae, Jindo and Dolsan Bridges. These systems can be considered the second generation bridge health monitoring systems (BHMSs). These were then followed by the development of the third generation bridge monitoring systems installed and currently in service on the Seohae and Yeongjong Bridges. This study tracks the background and advent of Korean bridge monitoring systems and their developmental history up to the present time. It also investigates current associated studies in progress and the limitations of contemporary BHMS technologies, and suggests possible remedial solutions for a follow-up bridge monitoring system.