Artificial intelligence‐based displacement capacity prediction tool for three‐dimensional printed concrete walls

Abstract Digital technology applications such as three‐dimensional printers offer novel avenues for automating the construction of concrete structures. The process of 3D printing concrete structures involves designing in an office setting and printing on‐site. This sequential approach to 3D printing makes achieving fully automated practice unfeasible. To address the goal of achieving fully automated 3D concrete printing, this study proposes an automated design tool utilizing artificial intelligence (AI) techniques to ascertain the capacities of 3D‐printed concrete walls and conduct the necessary design tasks for each wall. Moreover, this automated design tool is capable of determining the most efficient section geometry for the intended 3D‐printed wall geometry. Consequently, the AI model identifies the most cost‐effective and lightweight cross‐section topology with the desired strength without requiring trial and error, thus eliminating the need for human intervention in the design process.