AI Smoke Compliance Analysis for Building Design Plans
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining whether a building design provides adequate smoke management and air quality compliance are time-consuming, labor-intensive, and prone to inconsistencies due to manual processes and varying interpretations by skilled workers.
Innovation Solution
The use of artificial intelligence (AI) and machine learning to analyze two-dimensional building plans, auto-detect, measure, and classify components, and determine compliance with smoke control codes, while also providing recommendations for improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection by skilled workers is used to determine smoke management compliance, then the analysis can be performed with existing methods, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer vision system that uses image processing algorithms to analyze building designs. The system automatically detects smoke management components, measures their dimensions, and determines code compliance without human intervention, thereby increasing productivity and reducing time loss.
Solution Approach 2:
The compliance determination system performs self-service by automatically analyzing building designs against smoke management codes. The system independently identifies components, measures dimensions, and generates compliance determinations without requiring skilled workers, enabling the system to serve itself in the compliance assessment process.
2Reliability
If manual compliance determination is performed by skilled workers, then compliance can be assessed, but inconsistencies arise due to varying interpretations
Solution Approach 1:
The patent transforms subjective compliance assessment into objective parameter-based evaluation. The system measures specific dimensional parameters (width, height, spacing) of smoke management components and compares them against coded requirements, eliminating interpretive variability and ensuring consistent, accurate compliance determinations.
Solution Approach 2:
The system replaces human judgment with automated image processing and machine learning algorithms that objectively analyze building designs. This substitution eliminates the inconsistencies caused by varying worker interpretations and provides precise, repeatable compliance assessments.
3Measurement precision
If detailed analysis of building designs is performed manually, then compliance can be determined, but the process becomes labor-intensive
Solution Approach 1:
The automated system performs self-service analysis by independently detecting, measuring, and evaluating smoke management components in building designs. The machine learning model automatically identifies relevant elements and performs precise measurements without requiring labor-intensive manual review, thereby maintaining measurement precision while significantly improving productivity.
Solution Approach 2:
The patent replaces manual measurement and analysis with automated computer vision technology. The system uses image processing algorithms to precisely detect and measure component dimensions, eliminating the need for manual measurement while maintaining or improving measurement precision and dramatically increasing analysis efficiency.
4Productivity
If automated AI analysis is implemented, then compliance determination becomes consistent and efficient, but the system complexity increases
Solution Approach 1:
The patent segments the complex compliance analysis task into distinct functional modules: image input processing, smoke management component detection, dimensional measurement, code requirement retrieval, and compliance determination. This segmentation manages system complexity by breaking down the overall system into manageable, independent components that can be developed and maintained separately.
Data Source
AI summary
Apparatus operative to efficiently manage smoke aspects of a design plan. and smoke control practices. A design plan is represented using multiple dynamic components. Each dynamic component may include a parameter changeable via the user interactive interface. The dynamic components may be arranged in a user interactive interface to form a first set of boundaries, including a respective length and area, and defining at least a portion of a first unit. AI may determine a longest path of egress and a supportable occupancy load that may be used to determine compliance with parameters of a given code. The AI may assess whether a building described in the design plans complies with a relevant code set forth by an authority having jurisdiction. Codes may include, for example, codes relating to smoke control and fire safety.