A novel deep learning‐based technique for efficient characterization of engineered cementitious composites cracks for durability assessment

Abstract Engineered Cementitious Composites also known as Strain‐hardening cementitious composites (SHCCs) has unique cracking patterns like cracks that have tiny widths and showcase high density. All of this makes it difficult and laborious to compute crack parameters from crack patterns. Unfortunately, this is an essential part of assessing durability and micromechanical modeling. SHSnet is developed to perform end‐to‐end semantic segmentation of SHCC cracks. SHSnet is efficient, attention based deep encoder‐decoder network with large receptive field. Loss function based on Tversky function were used for training the model. SHSnet with loss function shows promising result with mPrecision, mF1Score and mIoU of 0.87, 0.84 and 0.83 respectively for complex SHCC cracks while requiring at least an order of fewer computational parameters than those in the literature. An image processing unit is then used to estimate the width, number, and length of the cracks from the segmentation mask. Test results show that the computed crack parameters with SHSnet are exactly the same as that computed with an optical microscope but require ~100× less time. Results demonstrate that SHSnet works equally well in SHCCs with different surface textures, crack density, and widths; the final result was far superior to a conventional technique. This technique also shows promising results in an automatic evaluation of crack parameters relevant to durability and visualizing crack patterns even in the presence of artifacts during progressive testing. The results also demonstrate the necessity to accurately and densely calculate crack length and maximum crack width; else the durability results are expected to be significantly more conservative than the actual value.