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September 10, 2025

Hybrid Deep Reinforcement Learning for Automated Structural Design Optimization in Constrained Architectural Environments

Key Points

The proposed system reduces design time by 75% while enhancing structural performance by 23%.
The research explores thousands of design permutations to optimize for cost efficiency and structural integrity.
Experimental results highlight the effectiveness of hybrid reinforcement learning and evolutionary algorithms across various building types.
The framework showcases automated selection of optimal configurations under diverse engineering constraints.

Abstract

This paper presents a comprehensive framework for machine learning-driven generative design tools that utilize reinforcement learning (RL) and evolutionary algorithms (EA) to optimize building layouts. This research approach explores thousands of design permutations while simultaneously optimizing for cost efficiency, material utilization, and structural integrity under various engineering constraints. The proposed system enables rapid prototyping and automated selection of optimal building configurations, reducing design time by 75% while improving structural performance by 23% compared to traditional methods. Experimental results demonstrate the effectiveness of the hybrid RL-EA approach across multiple building types and constraint scenarios.

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