Deep reinforcement learning for intelligent risk optimization of buildings under hazard

Risk management often involves retrofit optimization to enhance the performance of buildings against extreme events but may result in huge upfront mitigation costs. Existing stochastic optimization frameworks could be computationally expensive, may require explicit programming, and are often not intelligent. Hence, an intelligent risk optimization framework is proposed herein for building structures by developing a deep reinforcement learning-enabled actor-critic neural network model. The proposed framework is divided into two parts including (1) a performance-based environment to assess mitigation costs and uncertain future consequences under hazards and (2) a deep reinforcement learning-enabled risk optimization model for performance enhancement. The performance-based environment takes mitigation alternatives as input and provides consequences and retrofit costs as output by utilizing several steps, including hazard assessment, damage assessment, and consequence assessment. The risk optimization is performed by integrating performance-based environment with actor-critic deep neural networks to simultaneously reduce retrofit costs and uncertain future consequences given seismic hazards. For illustration, the proposed framework is implemented on a portfolio with numerous building structures to demonstrate the new paradigm for intelligent risk optimization. Also, the performance of the proposed method is compared with genetic optimization, deep Q-networks, and proximal policy optimization.