Measurement of reinforcement corrosion in concrete by integrated simulations, ultrasonic tests, and artificial neural networks
Year: 2025
Type: article
Abstract: Current methods for measuring reinforcement corrosion in concrete have limitations, including low efficiency, poor accuracy, and ambiguous interpretation. High-accuracy nondestructive measurement of reinforcement corrosion was realized in this study through the integration of three technical approaches: (1) Ultrasonic wave propagation in reinforced concrete (RC) with different rebar diameters was simulated using COMSOL to establish the relationship between rebar corrosion (reflected by diameter reduction) and ultrasonic characteristics; (2) Accelerated corrosion experiments were performed on RC specimens using a 5 % sodium chloride solution, monitoring corrosion evolution over 40 h. Three distinct corrosion stages were identified: slow development, rapid expansion, and stable deterioration. Additionally, ultrasonic propagation time was found to delay and amplitude increase with intensifying corrosion, while concrete compressive strength decreased rapidly after 8 h of corrosion; (3) A back propagation-artificial neural network (BP-ANN) optimized by the AdaBoost algorithm was developed, with ultrasonic amplitude, crack width, and compressive strength as input parameters. To enhance model training stability and avoid overfitting, 10-fold cross-validation was adopted for data partitioning, and the Gray Wolf Optimization (GWO) algorithm was integrated to optimize the weights and thresholds of the BP-ANN. The AdaBoost-BP model achieved high prediction accuracy for rebar corrosion indicators: 93.75 % for mass loss rate and 97.67 % for section loss rate. The integrated method, combining coda wave interferometry (CWI), ultrasonic testing, and ANN, aims to overcome the limitations of existing techniques. A reliable framework for nondestructive evaluation of reinforcement corrosion was provided, enabling precise monitoring of RC structure durability. This foundation can be extended to complex real-world structures (e.g., bridges, coastal buildings) and promote the development of intelligent corrosion detection systems. • Integrate COMSOL simulations, ultrasonic tests and AdaBoost-BP-ANN to realize early detection of rebar corrosion via Coda Wave Interferometry. • Enables early corrosion detection (80–200 μm depth) via coda wave interferometry, capturing subtle rebar diameter reductions before visible cracks. • The optimized model achieves 93.75 % and 97.67 % accuracy for rebar mass loss and section loss prediction, outperforming traditional methods.
Authors: Zhezhe Hou, Yanliang Du, Hongbin Xu
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,660
OpenAlex ID: W4416417661