Strain monitoring and depth estimation for multiple surface cracks in steel structures using smart CFRP embedded with distributed fiber sensors

Abstract

The in-service strain monitoring and damage assessment of CFRP-reinforced steel structures exhibiting closely spaced cracks present a major challenge due to the large-range detection requirements and complex crack interactions. To address these challenges and broaden the practical application of smart CFRP systems—which integrate advanced CFRP materials with Distributed Optical Fiber Sensing (DOFS) technologies—this study proposes a two-phase framework for strain monitoring and crack depth estimation in steel structures with multiple surface cracks. A theoretical model incorporating Crack Opening Displacement (COD) and crack spacing was developed to quantify strain fields induced by multiple parallel cracks, including their interaction coefficients. Elasto-plastic adhesive behaviour was considered in deriving inverse explicit formulations to determine COD from distributed strain measurements. A COD-based inverse model was subsequently established to track crack depth evolution. Pre-cracked steel frame specimens bonded with smart CFRP (equipped with high-resolution PPP-BOTDA sensors) were subjected to experimental and numerical analyses. The results demonstrate that, multiple closely spaced cracks increase CFRP stress by up to 30 % compared to single-crack predictions. Crack interaction effects become non-negligible when spacing distances are within 5 times the crack depth. The proposed multi-crack model was experimentally validated for strain monitor and crack depth estimation under small-scale yielding conditions, with a discrepancy between theoretical predictions and experimental measurements of less than 8 %, demonstrating good agreement. The proposed smart CFRP embedded with distributed optical fibers will be well-suited for long-term strain monitoring and quantitative crack estimation in strengthened structures with multiple surface cracks.