This paper proposes a methodology to automatically extract components of an oil storage tank from terrestrial laser scanning (TLS) point clouds, and subsequently to create a three-dimensional (3D) solid model of the tank for numerical simulation. The proposed method is integrated into a smart analysis layer of a digital twin platform consisting of three main layers: (1) smart analysis, (2) data storage, and (3) visualisation and user interaction. In this proposed method, primary components of the tank were automatically extracted in a consecutive order from a shell wall to roof and floor. Voxel-based RANSAC is employed to extract voxels containing point clouds of the shell wall, while a valley-peak-valley pattern based on kernel density estimation is implemented to remove outlier points within voxels representing to the shell wall and re-extract data points within voxels adjoined to the shell wall. Moreover, octree-based region growing is employed to extract a roof and floor from remaining point clouds. An experimental showed that the proposed framework successfully extracted all primary components of the tank and created a 3D solid model of the tank automatically. Resulting point clouds of the shell wall were directly used for estimating deformation and a 3D solid model was imported into finite element analysis (FEA) software to assess the tank in terms of stress-strain. The demonstration shows that TLS point clouds can play an important role in developing the digital twin of the oil storage tank.

Development of laser scanning has offered great opportunity to capture three-dimensional (3D) topographic information of objects’ surfaces in a highly accurately and efficiently. Particularly, a terrestrial laser scanner is able to acquire millions of points within a second at millimetre accuracy. This technology has been widely used in many civil engineering applications, including surveying, construction management, and infrastructure inspection. Traditionally, tank inspection was carried out on-site by physical inspectors with suitable measurement equipment (e.g. tapes, staffs and a total station). This approach, albeit the most common one, has many downsides: subjective results, slow and expensive procedure, requirement of experienced and trained inspectors and close service of the tank. Additionally, all results are stored as hard copies, which lead to difficulties in tracking damage development and management. To mitigate these disadvantages, this paper proposes a method for (i) automatically extracting a point cloud of a tank wall from a massive data points, and (ii) evaluating the tank wall through its deformation.