3D printed concrete faces challenges such as material proportioning, rheological properties, and difficulty in coordinating printing parameters.
Traditional trial and error designs are unable to cope with complex nonlinear relationships, resulting in unstable quality, high costs, and carbon emissions amplification.
The combination of machine learning and multi-objective optimization provides a new approach to solving this problem.
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