AI 2D Plan Analysis for Construction Cost Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for detailing and cost estimation in building construction from 2D plans are inaccurate and time-consuming, often requiring skilled quantity surveyors and resulting in significant delays and errors.
Innovation Solution
A structural building design system that uses AI to analyze and interpret complex 2D architectural plans, automatically providing detailed output information such as building takeoffs, construction estimates, and material lists through a combination of object detection, semantic segmentation, and text recognition, along with machine learning algorithms and feature vector space datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods by skilled quantity surveyors are used to analyze 2D plans, then measurement precision and reliability are improved, but productivity is significantly reduced and loss of time increases
Solution Approach 1:
The patent replaces the manual mechanical analysis process performed by quantity surveyors with an automated AI-based computer vision system. The system uses object detection, semantic segmentation, and text recognition algorithms to automatically extract building components and material quantities from 2D plans, eliminating the need for manual measurement and calculation while maintaining high accuracy through machine learning models trained on annotated plan datasets.
Solution Approach 2:
The patent introduces an AI processing system as an intermediary between the 2D plan input and the final quantity survey output. This intermediary system performs multiple processing stages including image preprocessing, object detection, semantic segmentation, text recognition, and data aggregation to bridge the gap between raw plan images and structured material quantity data, achieving both speed and accuracy.
2Reliability
If traditional manual methods are used for construction detailing and cost estimation, then reliability is improved through expert judgment, but loss of time and productivity are significantly worsened
Solution Approach 1:
The patent performs preliminary automated extraction of building components, material quantities, and specifications from 2D plans before cost estimation is required. The system pre-processes the plans to identify and quantify all building elements, creating a structured database of materials and components that can be quickly converted into cost estimates using integrated pricing databases, eliminating the need for time-consuming manual takeoffs and calculations during the estimation phase.
Solution Approach 2:
The patent creates a multi-functional AI system that performs multiple functions including object detection, semantic segmentation, text recognition, material quantification, construction detailing, and cost estimation within a single integrated platform. This universal system handles the entire workflow from plan analysis to cost estimation, replacing multiple manual processes and expert interventions with one automated system that maintains reliability through cross-validation and error checking.
3Productivity
If AI-based automated systems are used to analyze 2D plans, then productivity and speed are improved, but measurement precision and reliability may be worsened due to complexity of automated analysis
Solution Approach 1:
The patent divides the complex task of plan analysis into multiple specialized AI modules including object detection for identifying building components, semantic segmentation for precise boundary detection, text recognition for extracting specifications, and quantity calculation for material quantification. Each module focuses on a specific aspect of the analysis, improving overall accuracy through specialized processing while maintaining high productivity through parallel execution of these segmented tasks.
Solution Approach 2:
The patent implements feedback mechanisms where the AI system validates its detections against multiple criteria including geometric consistency, contextual relationships between building elements, and comparison with typical construction patterns. The system uses confidence scoring to identify uncertain detections and can request additional processing or human review for low-confidence results, ensuring high measurement precision while maintaining rapid processing speed for high-confidence detections.
Data Source
AI summary
A structural building design system for processing, interpreting and analysing holistically a multipage set of two-dimensional (2D) real-world building construction plans for a building and yielding near real time accurate material type, quantity, and specification outputs as required for construction of a compliant building from said plans, through computationally generating a mathematical feature vector space dataset, the system comprising: one or more processors configured to: receive a two-dimensional real-world architectural plan for construction of a structural building, wherein the two-dimensional real-world architectural plan includes objects comprising: architectural symbols, lines, shading, or text; perform pre-processing, of characteristics on or associated with the objects on the two dimensional real-world architectural plan on a pixel by pixel basis for measurement or adjacent multi-pixel basis for object detection, wherein the pre-processing comprises two or more of: object detection and recognition, semantic segmentation, or text recognition to identify a plurality of objects, on the two dimensional real-world architectural plan to identify the characteristic features thereof; computationally transforming at least one characteristic feature of known (learned) and unknown (unlearned) detected objects into a mathematical representation thereof to form part of a future vector space dataset; and wherein said transformations for learned and unlearned detected objects have both high level and low level classifications of identified characteristic features; performing a comparison of the future feature vector space dataset for the identified detected objects to confirm the detected objects meet a predetermined confidence threshold for the classification of each detected object; performing a correlation analysis of the characteristic features for detected objects meeting the pre-determined confidence threshold level, wherein the comparison and correlation analyses above include one or more of determining shape, position, adjacent objects and using said pre-processing and results of correlation analysis of the feature vector space dataset via the algorithms to provide output information regarding the 2D plan including producing autonomous, and highly accurate creation of at least one or more of the following outputs: near real time accurate building takeoffs; complete construction estimates; complete construction detailing; detailed bill of materials for the construction of the building; or a document summarizing differences or similarities between building plans.