Flexural repair of hollow rectangular bridge columns failed due to earthquake-type loading

[[abstract]]The purpose of this research is to develop a repair technique for hollow-bridge columns that have failed, due to fracturing or buckling of longitudinal rebars, so that bridge function can be quickly restored after earthquakes. In order to validate the proposed technique, two full size and three scaled-down hollow bridge columns, which had previously failed under tests conducted in conjunction with other projects, were repaired using the proposed technique and subsequently retested. To restore the column's flexural strength, the fractured longitudinal bars were replaced with dog-bone shaped bars. In addition, a steel jacket was emplaced in the plastic hinge region in order to enhance the deformation capacity of the repaired columns. Test results showed that the damaged columns could be repaired within three days, effecting a 90% restoration of the original column's flexural strength and a comparable degree of restoration of ultimate displacement to the original columns. However, it was also found that there was only a 50% recovery of the column's original stiffness and ductility

Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current state-of-the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice