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Optimization and decision-making tools are often utilized to enhance the performance of community buildings under extreme events, but this may compromise the ability of future generations to enhance performance. Hence, a sustainability-oriented approach is required to enhance the performance of community buildings under extreme events. In this context, this paper proposes an optimization and decision-making framework considering multiple performance indicators, including socioeconomic and environmental consequences as well as retrofit costs. These performance indicators are assessed by utilizing performance-based assessment methodologies in terms of sustainability dimensions. The performance indicators are then exploited as multiple performance objectives in a genetic optimization to determine the Pareto optimal solutions. Finally, the Pareto optimal solutions are utilized for decision making to extract ideal solutions for the given retrofit costs. The ideal solutions provide trade-offs between the consequences of extreme events and the retrofit costs required to reduce the consequences of extreme events. [ABSTRACT FROM AUTHOR]

• Develop an intelligent seismic risk optimization framework for building structures. • Leverage deep reinforcement learning for performance enhancements under hazard. • Compare model performance with genetic optimization, policy gradients, and deep Q-networks. • The proposed tool is efficient for complex decision-making problem of high dimension. • Illustrate the proposed methodology on building structures under seismic 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. [ABSTRACT FROM AUTHOR]

• Propose multi-objective optimization and decision-making framework. • Considers risk attitudes of decision-makers under uncertain consequences. • Assess consequences by establishing surrogate-based Gaussian process models. • Illustrates the framework on a community building portfolio. The performance of community building portfolios under extreme events is increasingly been assessed in terms of socioeconomic and environmental consequences. These multiple consequences are expensive to be evaluated, uncertain in nature, and it requires an efficient optimization and decision-making tool for performance enhancements on a community level. In this paper, an efficient multi-objective performance-based optimization and decision-making framework is proposed to assess and enhance the performance of community building portfolios under uncertain consequences. The proposed approach includes (1) performance-based black-box to evaluate the socioeconomic and environmental consequences given community building portfolios by considering all the possible hazard scenarios, (2) surrogate-based multi-objective optimization to efficiently approximate the Pareto-optimal solutions by exploiting Gaussian process models, spectral sampling, non-dominated crowding and sorting, and hyper improvements, among others, and (3) utility theory-based decision-making of a community building portfolios considering multiple performance-objectives over an entire hazard curve and under different risk attitudes. The proposed methodology is illustrated on community building portfolio under seismic hazards of varying intensities and occurrence probabilities. A performance-based black box is utilized along with a proposed optimization technique to assess community-level consequences. Finally, the utility for Pareto-optimal solutions is assessed and discussed for possible performance enhancements and decision-making. [ABSTRACT FROM AUTHOR]

It is essential to assess the performance of a community under probable hazard scenarios and to provide possible performance enhancements. This requires establishing performance indicators, an assessment method, and an optimization technique to provide mitigation alternatives. In this paper, multiple performance indicators are utilized to assess the performance of a community building portfolio including loss, downtime, and environmental impact (e.g., CO2 emissions). The performance of a community is assessed by utilizing a performance-based assessment methodology. Then, the performance indicators are utilized as performance objectives to be optimized considering non-dominated sorting and crowding distance evolutionary optimization techniques. The framework utilizes retrofit alternatives for each building in a community and provides Pareto-optimal solutions for considered performance objectives given retrofit cost. This process of performance assessment and optimization is repeated by utilizing the Monte Carlo approach to consider uncertainties. Finally, the Pareto-optimal solutions are utilized to evaluate the retrofit programs for community building portfolios in terms of considered performance indicators. [ABSTRACT FROM AUTHOR]