Hybrid machine learning and physical modeling framework for climate-driven risk zonation of concrete shrinkage damage
Year: 2026
Type: article
Abstract: Concrete shrinkage under non-stationary climatic forcing poses an increasing threat to the serviceability and longevity of infrastructure in low-pressure, arid and high-altitude regions. Current models neglect multi-environment interactions and climate-driven risk evolution. This study presents a hybrid modeling and assessment framework that coupled physics-informed empirical priors with optimized machine learning to predict shrinkage evolution, quantify structural risk, and map spatiotemporal vulnerability under future climate scenarios. A curated shrinkage database was fused with high-resolution meteorological projections and downscaled via filtering and cubic interpolation. The empirical CEB-FIP 2010 shrinkage formulation and air pressure parameters were embedded into feature engineering to create temperature-humidity-pressure coupled predictors. An XGBoost (Extreme Gradient Boosting) model was optimized through systematic hyperparameter tuning and physics-guided transfer learning. The optimized coupling model attained R 2 = 0.92 to predict shrinkage evolution, and reduced long-term prediction divergence to within 15% against independent data from three-factor experiments. To translate material-level shrinkage into structural risk, multiphysics finite-element simulations of a representative reinforced-concrete pier incorporated eigenstrain shrinkage fields and reinforcement constraint to resolve strain–stress–damage progression. Four critical normalized strain thresholds were identified that demarcated initiation, stable propagation, accelerated expansion and through-crack stages. A five-tier risk zoning map across China was constructed, covering both historical data and mid-future climate scenario. Plateau and northwestern basins showed marked vulnerability. Using C60 concrete as a representative case study due to its prevalence, results showed the medium-to-high risk area increasing by 65%, with 31.1% of China's territory classified as medium–high risk by 2050.
Authors: Qiaosong Hu, Dujian Zou, Zhilin Bai, Tiejun Liu, Ao Zhou
Institution: Harbin Institute of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
OpenAlex ID: W7124296756