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

Abstract

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.

Graphical abstract

Keywords

precast deck joint / flexural strength / machine learning / model averaging / Monte Carlo method / parameter tuning

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Competing interests

The authors declare that they have no competing interests.

Appendix A: Graphical user interface application

To enhance the user-friendly application of the proposed MCMA model for predicting the precast deck joints’ flexural strength, a dedicated standalone program was developed using Tkinter in this study. Figure A1 presents the application of a graphical user interface. From the figure, by providing specific input variables, users can effortlessly determine the flexural strength of precast decks with a simple click on the predict button. It’s essential to highlight that the validity of the application program is confined to the variable ranges outlined in Tab.1, as the model was trained exclusively within these specified ranges.

Fig.A1 GUI for MCMA model.

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