Abstract

Abstract

Abstract

The formulation of a multi-hazard loss model for a given structure is not only of interest for predicting the economic impact of future damage but it can also be of importance for risk mitigation. A methodology that can assess the vulnerability of the built environment is a significant component of a loss model. The multi-risk vulnerability of heritage buildings necessitates greater knowledge about the history of their construction, including aspects relating to preserving them as assets. Timber as an organic material is more susceptible to decay and the structural assessment of timber buildings is essential for their preservation. A preliminary survey as a basis for multi-risk vulnerability assessment of such buildings is essential. In this step of the process, the history of the building is investigated, as well as any intervention to it during its lifetime. A damage inspection of structural elements conducted by experts is an essential part of the second step. After this, the configuration of the building, including height, plan view and connection details should be documented. After the preliminary survey of the building, detailed methods are employed to gather further information about the structure’s behaviour under different risk scenarios. In this paper, heritage timber buildings in Tønsberg, Norway have been selected as case studies for multi-risk vulnerability assessments. The preliminary survey has been conducted by a team of experts and useful data have been recorded and explained. 3D laser scanners have been used in the survey in place of traditional 2D methods to obtain a more detailed and accurate 3D representation of the buildings.

Abstract

Abstract

The preforming consists of processing the reinforcement to obtain a geometry of the dry fiber, close to that of the final product before being impregnated by the resin during the injection process. During the process, the fibers are manipulated to reach the desired shape are subjected to tensile, shear and torsion stresees that can cause changes in the orientation of the fibers, misalignments,or other defects such as wrinkles or fiber breakage that can affect the performance of the preform . In this work a solution for the automation of the quality control of the preforming process by artificial vision is investigated.. Two approaches are proposed, 3D vision for the geometric and superficial inspection of the fibers and machine learning aproach based on Deep Learning (DL) to detect irregularities in the orientation. The solution based on 3D vision automatically detects defects based on the comparison of a theoretical 3D model and a 3D reconstruction of the real part. A structured light system is used to generate dense and precise point clouds. Once aligned, the dissimilarities between both surfaces are analyzed. The solution is complemented by a 2D vision system based on DL that classifies the irregularities in the orientations of the fabric. The model is trained with images of classified fiber orientations to automatically identify areas of fiber whose orientation does not correspond to the expected one. The combination of both technologies allows to give a complete solution to the automated quality inspection of preforms for manufacturing or defects.