ABSTRACT
While the construction of high-rise buildings has become popular in big cities, an average of over 15,000 structure fires in those buildings are being reported in the United States. Especially because the fire in a building can result in a failure or even the collapse of the structure, assessing its integrity during and after the fire is of importance. Thus, in this paper, a framework with temperature sensors using wireless communication technology has been proposed. Associated hardware and software are carefully chosen and developed to provide an easy and effective solution for measuring fire load on large-scale structures during a fire. With an autonomous measurement system enabled, the key functions of the framework have been validated in a fire testing laboratory, using a real-scale steel column subject to standard fire. Unlike existing solutions of wireless temperature networks, the proposed solution can provide the user definable sampling frequencies based on the surface temperature and the means to assess the load redistribution of the structure due to fire loading in real-time. The results of the study show the great potential of using the developed framework for monitoring fire in a structure, allowing more accurate estimations of fire load in the design criteria, and advancing fire safety engineering.
ABSTRACT
Railway bridges are exposed to repeated train loads, which may cause fatigue failure. As critical links in a transportation network, railway bridges are expected to survive for a target period of time, but sometimes they fail earlier than expected. To guarantee the target bridge life, bridge maintenance activities such as local inspection and repair should be undertaken properly. However, this is a challenging task because there are various sources of uncertainty associated with aging bridges, train loads, environmental conditions, and maintenance work. Therefore, to perform optimal risk-based maintenance of railway bridges, it is essential to estimate the probabilistic fatigue life of a railway bridge and update the life information based on the results of local inspections and repair. Recently, a system reliability approach was proposed to evaluate the fatigue failure risk of structural systems and update the prior risk information in various inspection scenarios. However, this approach can handle only a constant-amplitude load and has limitations in considering a cyclic load with varying amplitude levels, which is the major loading pattern generated by train traffic. In addition, it is not feasible to update the prior risk information after bridges are repaired. In this research, the system reliability approach is further developed so that it can handle a varying-amplitude load and update the system-level risk of fatigue failure for railway bridges after inspection and repair. The proposed method is applied to a numerical example of an in-service railway bridge, and the effects of inspection and repair on the probabilistic fatigue life are discussed.
ABSTRACT
Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved.
