Abstract
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.
Highlights
Most of the building stock is privately owned and the decision-making related to the choices including the type of construction, upgradation, among others is partially decentralized
This paper is organized into six sections: (1) Section 1 outlines the introduction of the paper, (2) Section 2 proposed a bi-objective optimization framework for community building portfolios, (3) Section 3 presents the performance-based assessment method to assess performance objectives, (4) Section 4 highlights the bi-objective evolutionary optimization method, (5) Section 5 presents an illustrative example, and (6) the final section presents the conclusions of the paper
To measure the performance of a community building portfolio under an earthquake hazard, three performance indicators are selected: (1) total repair cost incurred on a community because of an earthquake referred as risk performance indicator, (2) total downtime of a community as a measure of how long it will take to recover from a hazard, referred as downtime performance indicator, and (3) total equivalent carbon emissions emitted as a result of damage to the community and recovery efforts, referred as sustainability performance indicator
Summary
Most of the building stock is privately owned and the decision-making related to the choices including the type of construction, upgradation, among others is partially decentralized (i.e., buildings construction and upgradation related activities are not entirely centrally planned by a government body or community stakeholders). The risk performance indicator is related to the immediate impact of extreme events and has been extensively utilized to assess the performance of community building portfolios under hazard scenarios [28,29,30] These immediate impacts may include total number of casualties, total waste generated, total repair costs on a community level, among others. The release of potential global warming gases during the repair activities from a hazard event would impact the environment negatively and may impact future generations These performance indicators can cover a wide range of consequences, provide more meaningful and intuitive information, and can be utilized to make decisions and enhance performance. According to the best of the authors’ knowledge, there have been no studies providing a performancebased bi-objective optimization framework for community building portfolios considering multiple performance indicators including sustainability and under uncertainties. This paper is organized into six sections: (1) Section 1 outlines the introduction of the paper, (2) Section 2 proposed a bi-objective optimization framework for community building portfolios, (3) Section 3 presents the performance-based assessment method to assess performance objectives, (4) Section 4 highlights the bi-objective evolutionary optimization method, (5) Section 5 presents an illustrative example, and (6) the final section presents the conclusions of the paper
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