Abstract In response to the growing demands for resilient and durable reinforced concrete (RC) structures, the integration of automation in design processes has become imperative. Stainless steel reinforcement has gained increased attention in recent years due to its exceptional performance and unique properties. However, its widespread use has remained limited mainly due to insufficient design guidance and standardized rules. To address this gap, this study presents a novel automated predictive model designed to predict the ultimate flexural capacity of concrete beams reinforced with stainless steel, employing eight different machine learning (ML) algorithms. This work represents the first known attempt to automate the design of stainless steel RC beams using ML, addressing both the accuracy and efficiency challenges in structural design. For this purpose, a comprehensive literature survey is conducted based on the existing tests in the literature. A visual analysis of the collected databases was performed to examine the database and assess the key parameter correlations. The paper evaluates and compares the accuracy and efficiency of existing global design standards and recently proposed methods against the developed ML models. A sensitivity analysis was conducted to explore the influence of key input parameters on the ultimate flexural capacity. To facilitate broader accessibility, a user‐friendly graphical user interface (GUI) was developed, allowing efficient interaction with the ML model without requiring extensive technical knowledge, ensuring broader accessibility. The results underscore the potential of machine learning‐driven automation to optimize the design process and improve the reliability and efficiency of reinforced concrete design.
Rabi et al. (Tue,) studied this question.
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