Estimating flexural strength of precast deck joints using Monte Carlo Model Averaging of non-fine-tuned machine learning models

The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them. However, due to the complexity and diversity of this system, precise predictive models are currently unavailable. This study introduces an effective and precise methodology for assessing flexural strength using Monte Carlo Model Averaging (MCMA), a statistical technique that combines the strengths of model averaging (MA) and Monte Carlo simulation. To construct the MCMA model, input variables were derived by analyzing the experimental results, and a database of 433 bending test specimens was compiled. The MCMA model incorporated four different machine learning models, namely decision tree (DT), linear regression (LR), adaptive boosting (AdaBoost), and multilayer perceptron (MLP). Comparative analyses revealed that the MCMA model outperformed baseline models (DT, AdaBoost, LR, and MLP) across all employed metrics. The impact of three different categories on flexural capacity was explored through boxplot analysis. Furthermore, a comparison between the MCMA model and the strut and tie model highlighted the superior performance of the MCMA model. The impact of input variables on the flexural strength prediction was further examined through Shapley Additive exPlanations based feature importance and global interpretation, as well as parametric study.