Resilient and Sustainable Buildings

A risk-informed decision-making approach is required that accounts for the positive spatio-temporal stochastic dependencies in demand and response on systems to assess their risks of failure and to identify cost-effective strategies to manage risk and enhance resilience. In this chapter, major attributes that make a community resilient are de-aggregated to the building level to facilitate development of systems that optimize building performance with respect to resilience and sustainability goals. The research hypothesis is that it is possible to develop risk-informed performance criteria for individual resilient and sustainable buildings exposed to a spectrum of natural hazards, which can be matched to community goals; that building attributes can be identified and parameterized to support this general risk-informed decision framework; and that the risk-informed decision framework supporting these performance criteria for individual buildings will enable enhanced community resilience and sustainability by targeting public and private investments to manage life-cycle costs.

This chapter discusses the development and application of a quantitative science-based decision-making framework for design of multihazard resilient and sustainable soil-foundation-structure-envelope building systems. A key obstacle to resilience-based design is the high dimensionality of building design and the wide range of potential objectives and constraints applied to it, which hinder decision making. Multi-objective optimization (MOO) is the primary tool used to overcome this obstacle in the framework. The developed framework and methodology incorporate additional building performance objectives and are based on MOO of design variables of interest. The framework consists of four main components: natural hazard characterization, modeling of fragility and damage to building components, building resilience and sustainability assessment, and MOO and decision making. MOO methods are found to be a powerful tool for solving complex engineering design problems. A copula-based joint probability model for wind and flood hazards is also presented.

The design of resilient and sustainable buildings (RSB) is a challenging task owing in part to the consideration of various economic, sustainability, resilience, and other metrics while making decisions about a system comprised of subsystems, each generally consisting of numerous components. The combination of metrics and subsystems translates into numerous feasible design alternatives from which one must be selected. This chapter aims to provide an overview of the mathematical basis for the development of the sequential decision process (SDP), its various extensions and relevant applications, and examples to illustrate its use for the design of RSB. It begins with a review of the concept of viewing design as an SDP to provide the necessary background information. Key concepts pertaining to the development of low-fidelity models to bound the precise values of decision criteria, sequencing the low-fidelity models for efficient tradespace exploration, and calculating optimal modeling policies are described.

Decision support systems (DSS) minimize human effort and evade the excessive role of precedence and intuition. This chapter presents a decision framework for resilient, sustainable buildings exposed to multiple hazards that holistically characterizes building performance, aiming to support early design. It describes a modular DSS for the early holistic design in a multihazard environment that satisfies different stakeholders' preferences. The proposed DSS aim to identify design alternatives that will perform optimally at a given site and ensure that the highest performance over the building lifetime is achieved. The overall organization of the framework and its values are described. A detailed description of each module and the accompanying methods, data, and software are provided. The chapter presents an illustrative example that applies the proposed DSS to choose design alternatives for Charleston, South Carolina. It reflects on the lessons learned from the example and provides recommendations for future implications.

The design of multihazard resilient and sustainable buildings (RSB) is necessarily complex owing to the various economic, environmental, and technical considerations that must be factored into the design decision-making process. To facilitate broad exploration while identifying sufficing design solutions within a reasonable amount of time and effort, a general mathematical framework that views design as a sequential decision process was developed and applied for RSB design. The design and construction of RSB requires a significant amount of data ranging from recovery models to cost data for alternative strategies/designs. A data-driven and convergence-training framework is needed in which probabilistic- and artificial intelligence-based approaches assess networks' performance, quantify infrastructure network interdependence, and make projections on systems' resilience to natural and human hazards. Such a training framework can help prepare a new cadre of professionals to address the transdisciplinary challenges of infrastructure and societal resilience and sustainability.