Multi-objective design framework under uncertainties for strengthening tubular truss by partially filling with grout

Abstract

Due to the growing needs of strengthening steel tubular truss, a new method for enhancing tubular joints by partially filling the chord with grout is proposed. However, the strengthening design of a whole truss is a challenging task, mainly because of multiple design objectives and various fabrication uncertainties. Current practice based on empirical or simple rule-based strategies is not able to handle the task. To address this challenge, a design framework for tubular truss strengthening is developed. The proposed framework can reduce the maximum deflection, improve the load capacities of the truss, and minimize the usage of grout. Furthermore, it considers geometric and modeling uncertainties through Monte Carlo simulation and predict intervals, thereby preventing over-idealization during practical optimization. To demonstrate the proposed design framework, a comparative structural analysis was conducted on a typical Warren truss between pre- and post- optimal strengthen. The results show that, by building upon the Machine Learning models, the proposed framework can produce an effective strengthening scheme. After considering uncertainties in optimization, some idealized samples are filtered out, resulting in a more practical strengthening scheme. The proposed framework is versatile and can be applied to other similar optimal strengthening designs with minimal additional effort.

Graphical abstract

Keywords

tubular truss strengthening / design framework / machine learning model / fabrication uncertainties / structural analysis

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