MONITORING OF PROGRESSIVE DAMAGE IN BUILDINGS USING LASER SCAN DATA
- 1Defense University Center, Spanish Naval Academy, Plaza de España s/n, 36900, Marín, Spain
- 2Dept. of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628 CN, Delft, The Netherlands
- 3Dept. of Material, Mechanics, Management and Design, Delft University of Technology, Stevinweg 1, 2628 CN, Delft, The Netherlands
Keywords: Change, monitoring, unreinforced masonry building, LiDAR, point cloud, post-disaster
Abstract. Vulnerability of buildings to natural and man-induced hazards has become a main concern for our society. Ensuring their serviceability, safety and sustainability is of vital importance and the main reason for setting up monitoring systems to detect damages at an early stage. In this work, a method is presented for detecting changes from laser scan data, where no registration between different epochs is needed. To show the potential of the method, a case study of a laboratory test carried out at the Stevin laboratory of Delft University of Technology was selected. The case study was a quasi-static cyclic pushover test on a two-story high unreinforced masonry structure designed to simulate damage evolution caused by cyclic loading. During the various phases, we analysed the behaviour of the masonry walls by monitoring the deformation of each masonry unit. First a plane is fitted to the selected wall point cloud, consisting of one single terrestrial laser scan, using Principal Component Analysis (PCA). Second, the segmentation of individual elements is performed. Then deformations with respect to this plane model, for each epoch and specific element, are determined by computing their corresponding rotation and cloud-to-plane distances. The validation of the changes detected within this approach is done by comparison with traditional deformation analysis based on co-registered TLS point clouds between two or more epochs of building measurements. Initial results show that the sketched methodology is indeed able to detect changes at the mm level while avoiding 3D point cloud registration, which is a main issue in computer vision and remote sensing.