Summary When an earthquake strikes an area, unless the local infrastructure has a structural health monitoring system, it is difficult to obtain its health condition due to a lack of data. A smartphone could be competently used for many monitoring tasks to gather large amounts of data at a low cost. In this study, a smartphone is used as a sensor to obtain the interstory drift of buildings by recording a video of the ceiling with the front camera using a feature point matching algorithm. In addition, static and dynamic tests were carried out. In the static tests, the precision and error were investigated and compared with the reference sensor. In the dynamic tests, the displacement under a seismic wave was acquired by a smartphone and compared with the reference sensor, and the illumination condition was varied to test the robustness of the algorithm. The results indicated that feature point matching was suitable for calculating the displacement, and the smartphone was competent in monitoring the interstory drift during an earthquake.

Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This paper proposes a method to solve these problems by monitoring the interstory drift with a smartphone and a laser device. In this method, a laser device is installed on the ceiling while a smartphone is fixed on a steel projection plate on the floor. Compared with a reference sensor, the method designed in this study shows that a smartphone is competent in monitoring the interstory drift. This method utilizes a smartphone application (APP) named D-Viewer to implement monitoring and data storage just in one place, which is also inexpensive. The results showed that this method has an average percent error of 3.37%, with a standard deviation of 2.67%. With the popularization of the smartphone, this method is promising in acquiring large amounts of data, which will be significant for building assessment after an earthquake.