A Versatile Optimization Framework For Sustainable Post-Disaster Building Reconstruction

TL;DR

This paper introduces a flexible optimization framework for sustainable post-disaster building reconstruction that balances costs and environmental impacts, aiding decision-makers in selecting optimal designs under various constraints.

Contribution

It presents a novel mixed-integer nonlinear optimization model integrating structural, safety, and regional code constraints for sustainable building design.

Findings

Identifies Pareto-optimal building designs balancing cost and embodied energy.

Demonstrates the framework's application through a case study in Nepal.

Shows how design choices are sensitive to material prices and safety regulations.

Abstract

This paper proposes an optimization framework for sustainable post-disaster building reconstruction. Based on mathematical optimization, it is intended to provide decision-makers with a versatile tool to optimize building designs and to explore the trade-off between costs and environmental impact (represented by embodied energy) of alternative building materials. The mixed-integer nonlinear optimization model includes an analytical building model that considers structural and safety constraints and incorporates regional building codes. Using multi-objective optimization concepts, Pareto-optimal designs are computed that represent the best trade-off designs from which a decision-maker can choose when they take additional criteria into consideration. As a case study, we consider the design of a multi-room one-story masonry building in Nepal. We demonstrate how the framework can be employed to answer a variety of questions, such as the optimal building design and material selection, the sensitivity of the decision to the prices, and the impact of regional safety regulation thresholds.