Due to the increasing traffic volume on the European railway network, the remaining fatigue life of existing steel bridges is a major concern. Several investigations demonstrated that supplementing the assessment with monitoring data enables to achieve more reliable remaining fatigue life estimations. In this paper, an event-driven monitoring system based on a wireless sensor network that consists of two functionally different components was designed and tested. Sentinel nodes, which were mounted on the track, were used for detecting approaching trains and alerting with alarm messages the monitoring nodes. These nodes, which were mounted on the bridge, started strain sensing and data recording after receiving the alarm message and went back to a power saving mode upon completion. An embedded data processing algorithm transformed the recorded raw data into a much smaller data set representing strain cycles. A test deployment on a railway bridge demonstrated that train detection and alarming was fast and reliable. The combination of event-driven monitoring and embedded data processing allowed to extend the battery lifetime of monitoring nodes to several months.

Wireless sensor networks have been shown to be a cost-effective monitoring tool for many applications on civil structures. Strain cycle monitoring for fatigue life assessment of railway bridges, however, is still a challenge since it is data intensive and requires a reliable operation for several weeks or months. In addition, sensing with electrical resistance strain gauges is expensive in terms of energy consumption. The induced reduction of battery lifetime of sensor nodes increases the maintenance costs and reduces the competitiveness of wireless sensor networks. To overcome this drawback, a signal conditioning hardware was designed that is able to significantly reduce the energy consumption. Furthermore, the communication overhead is reduced to a sustainable level by using an embedded data processing algorithm that extracts the strain cycles from the raw data. Finally, a simple software triggering mechanism that identifies events enabled the discrimination of useful measurements from idle data, thus increasing the efficiency of data processing. The wireless monitoring system was tested on a railway bridge for two weeks. The monitoring system demonstrated a good reliability and provided high quality data.

Event-driven monitoring policies enable to significantly reduce the power consumption of wireless sensor networks by reducing the recording period to those time intervals that provide valuable data. The resulting longer operation lifetime increase discloses fields of application that require long monitoring periods. This paper presents a structural monitoring system that uses specialized sentinel nodes for detecting possibly heavy road vehicles and for alarming monitoring nodes, which are specialized on strain sensing. Heavy vehicles are identified by estimating nearly in real time height and length of vehicles of a traffic flow by processing data recorded from low-cost ultrasonic and magnetic displacement sensors. Field tests demonstrated that while height detection is very reliable, length detection is too imprecise to discriminate with high success rates between trucks and delivery vans.

<p>Cross girders of a riveted steel frame bridge were monitored with a wireless sensor network. Since strain sensing is very expensive in terms of power consumption a novel signal conditioning hardware was developed that enabled to significantly reduce the power consumption. An embedded data processing algorithm transformed the recorded raw data into a sequence of maxima and minima representing the strain cycles thus reducing data communication and enabling an additional energy saving. By using a software based event triggering algorithm the embedded data processing was performed only for data acquired during train transits. Strain cycles of more than 900 trains were recorded. The quality of the recorded data was very good and demonstrated that WSNs can be a competitive alternative to conventional tethered monitoring systems for recording operational data for fatigue assessment of steel railway bridges.</p>