Building Circularity Planning Through Multi-Objective Design Scenarios
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Solution Overview
Problem
Current methodologies for optimizing building adaptations and new constructions fail to consider a comprehensive range of design strategies, are limited in optimizing multiple metrics, and take months to generate suboptimal designs, while neglecting future adaptability and recycling potential of construction materials.
Innovation Solution
A method and system for building project circularity optimization using multi-objective optimization methods, physics-based simulation tools, and decision-making tools to generate and analyze a vast array of design scenarios, considering salvage objectives and end-of-life possibilities, with algorithms for automated scenario generation and sensitivity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If comprehensive multi-objective optimization is implemented to optimize multiple metrics simultaneously, then design quality improves, but computational complexity and processing time increase
Solution Approach 1:
The optimization process is segmented into multiple phases: preliminary filtering of design scenarios, intermediate optimization of selected scenarios, and final multi-objective optimization. This segmentation allows the system to handle comprehensive optimization by breaking it into manageable stages, reducing overall computational complexity while maintaining design quality.
Solution Approach 2:
The system performs preliminary actions by pre-generating and pre-filtering design scenarios before applying multi-objective optimization. This preliminary filtering reduces the search space and computational burden of the subsequent optimization process, allowing comprehensive optimization to be performed efficiently on a reduced set of promising designs.
2Loss of time
If a limited number of pre-generated design options are optimized, then processing time is reduced, but design comprehensiveness and optimality deteriorate
Solution Approach 1:
The system dynamically adjusts the number of design scenarios to analyze based on project requirements and computational resources. Rather than using a fixed limited set, the system can generate and analyze a variable number of scenarios, balancing processing time with design comprehensiveness by adapting the scope to specific project needs.
Solution Approach 2:
The system changes parameters such as the number of design scenarios, optimization metrics, and analysis depth based on project-specific requirements. This allows the same optimization framework to efficiently handle both quick assessments with limited scenarios and comprehensive studies with extensive scenario analysis, resolving the contradiction between speed and thoroughness.
3Adaptability or versatility
If future adaptability and material recycling potential are considered in design optimization, then circularity and sustainability improve, but design complexity and analysis requirements increase
Solution Approach 1:
The system performs preliminary assessment of future adaptability and material recyclability during the initial design phase rather than as a separate later analysis. By evaluating these factors upfront alongside traditional design criteria, the system integrates circularity considerations into the core optimization process without requiring separate complex analysis frameworks.
Solution Approach 2:
The optimization system is designed to handle multiple objectives simultaneously including traditional performance metrics, future adaptability, and material recycling potential. This multi-functional optimization framework consolidates diverse evaluation criteria into a unified process, reducing overall design complexity by avoiding separate specialized analyses for each objective.
Data Source
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AI summary
Provided are methodologies to improve the recovery of building materials at end-of-life (EoL). The methodologies provide a decision support tool incorporating the main factors that impact the value of materials in buildings and employs a multi-objective optimization model to estimate optimal EoL options for materials. The impact of regional factors can be assessed. The methodologies are aimed at evaluating circular design and construction strategies. A first method includes selecting building strategies, inputting building information, generating simulations of design scenarios based on a generated building model, analyzing the design scenarios to determine at least one optimal design, by analyzing the design scenarios with multi-objective optimization methods, analyzing the salvage estimation of the design scenarios, performing an optimization to generate salvage objective tradeoff curve, analyzing each building material for EoL possibilities using the optimization, and performing a sensitivity analysis to validate the EoL possibilities and to analyze an optimization framework.