Algorithmic Construction Proposal Optimization
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Solution Overview
Problem
Traditional non-computational assisted architecture methods for construction proposal design in dense cities are inefficient and lack optimization for environmental and economic factors, relying on trial and error and professional experience, which can result in suboptimal designs for construction sites.
Innovation Solution
A computer-assisted method using a Property Optimization Algorithm (POA) that determines relevant site regulations and parameters, creates a logical subdivision of the construction site, populates it with random genomes, evaluates geometrical outputs, and iteratively optimizes solutions to maximize floor space and integrate environmental, architectural, and commercial factors, utilizing software like Rhinoceros 3D and Grasshopper for algorithmic modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trial and error approach is used for design, then architect can rely on professional experience and creative ideas, but the design process becomes costly and time-consuming with suboptimal results
Solution Approach 1:
The patent replaces the mechanical trial-and-error design process with a computer-based optimization algorithm that automatically evaluates multiple design configurations. The system uses computational methods to assess geometric outputs against regulatory constraints and optimize building parameters, substituting manual iterative design with automated algorithmic optimization.
Solution Approach 2:
The optimization algorithm autonomously evaluates design proposals against predefined regulatory parameters and automatically adjusts building configurations to maximize floor space. The system serves itself by independently assessing compliance, generating optimized designs, and iterating through solutions without requiring continuous manual intervention from architects.
2Area of stationary object
If maximum building width is used to maximize floor space, then floor space increases, but the design may not satisfy regulatory distance spacing requirements
Solution Approach 1:
The patent performs preliminary assessment of regulatory constraints before finalizing building design. The optimization algorithm pre-evaluates distance spacing requirements, sunlight exposure constraints, and other regulatory parameters during the design generation phase, ensuring compliance is built into the design from the outset rather than requiring post-design adjustments.
Solution Approach 2:
The system dynamically adjusts building parameters such as width, height, orientation, and setback distances to optimize floor space while maintaining regulatory compliance. The optimization algorithm varies these parameters iteratively, finding the optimal configuration that maximizes area while satisfying all distance spacing and sunlight exposure requirements.
3Productivity
If computer-based optimization algorithms are used, then design optimization and data-driven decisions improve, but substantive manual input and preliminary construction proposals are still required
Solution Approach 1:
The patent creates a unified system that performs multiple functions: regulatory constraint validation, geometric optimization, sunlight exposure analysis, and design generation. The optimization algorithm serves as a multi-functional tool that handles various aspects of design optimization simultaneously, reducing the need for separate manual analysis processes.
Solution Approach 2:
The system acts as an intermediary between regulatory requirements and design outcomes. The optimization algorithm translates complex regulatory constraints into actionable design parameters, mediating between legal requirements and architectural creativity to produce compliant optimized designs without requiring extensive manual interpretation.
Data Source
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AI summary
The method for computer-assisted design of a construction proposal for a building comprises determining regulations and parameters relevant to the construction site on which one or more buildings are to be put up, creating a logical architectural subdivision of the construction site to obtain a model space, populating the model-space with a random collection of individual genomes, obtaining and evaluating geometrical output for each genome, and optimizing in an iterative process the best solutions within the model-space.