Abstract
This study explores the application of recycled aggregate concrete (RAC) in prestressed segmental bridges, emphasizing its potential as a sustainable material in structural engineering. The research investigates the structural performance of RAC by analyzing 27 dry joint specimens, assessing shear strength characteristics in single-keyed and multi-keyed joint configurations. Results indicate that while RAC exhibits a reduction in shear strength compared to conventional concrete, particularly in single-keyed joints, its performance remains competitive in multi-keyed configurations under confining stress. To address the complexity of predicting RAC’s load-bearing capacity, this study introduces a novel predictive model developed through advanced machine learning techniques, including Linear Regression and Random Forest algorithms. The model effectively captures the unique mechanical properties of RAC, offering high-accuracy estimations that bridge the gap between empirical findings and practical design applications. By integrating sustainable materials with data-driven analytical methods, this research provides valuable insights into enhancing RAC’s viability for critical structural applications. The findings contribute to the broader adoption of environmentally friendly construction practices, aligning with global sustainability goals while ensuring structural reliability and resource efficiency in modern engineering.
| Original language | English |
|---|---|
| Journal | Proceedings of the Institution of Civil Engineers: Engineering Sustainability |
| DOIs | |
| Publication status | E-pub ahead of print (In Press) - 2025 |
Bibliographical note
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Keywords
- bridge compressive strength prediction
- bridges
- concrete structures
- dry joints
- durability
- machine learning
- recycled aggregate concrete
- sustainability