Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length - Ma, Xin-Rui; Wang, Xian-Lin; Chen, Shi-Zhi (Automation in construction, 2024)
Three-dimensional concrete printing (3DCP) faces challenges in determining and ensuring adequate bond strength between reinforcement and printed concrete. Traditional methods for predicting bond performance are merely deterministic without considering potential uncertainty, which would lead to risks for structural safety. To address this issue, this paper develops a trustworthy machine learning based prediction model for bond strength in reinforced printed concrete (RPC) structures using Natural Gradient Boosting algorithm. This developed model provides both scalar bond strength predictions and corresponding standard deviations, and in the test, it achieved a 94.5% safety rate and outperformed empirical formulas and deterministic approaches. Instructive guidance can be offered for structural engineers and designers in determining reinforcement embedment lengths for 3D-printed concrete during constructions. This probabilistic prediction approach can further enhance the safety and efficiency of digitally fabricated concrete structures, potentially extending its application to other critical parameters in printed concrete. •A probabilistic machine learning (ML) based prediction model for bond strength between reinforcement and printed concrete.•Higher safety rate by proposed model in comparison with previous ones.•A trustworthy approach for optimal embedment length design in printed concrete.•Ensuring safety rather than statistical indexes is more critical for applying ML.