Analysis of Thin Carbon Reinforced Concrete Structures through Microtomography and Machine Learning

Abstract

This study focuses on the development of novel evaluation methods for the analysis of thin carbon reinforced concrete (CRC) structures. CRC allows for the exploration of slender components and innovative construction techniques due to its high tensile strength. In this contribution, the authors have extended the analysis of CRC shells from existing research. The internal structure of CRC specimens was explored using microtomography. The rovings within the samples were segmented from the three-dimensional tomographic reconstructions using a 3D convolutional neural network with enhanced 3D data augmentation strategies and further analyzed using image-based techniques. The main contribution is the evaluation of the manufacturing precision and the simulation of the structural behavior by measuring the carbon grid positions inside the concrete. From the segmentations, surface point clouds were generated and then integrated into a multiscale framework using a parameterized representative volume element that captures the characteristic properties of the textile reinforcement. The procedure is presented using an example covering all necessary design steps from computed tomography to multiscale analysis. The framework is able to effectively evaluate novel construction methods and analyze the linear-elastic behavior of CRC shells.