Deep learning approach to estimate the optimal number of piles and beams from architectural floorplans

Siddhaarth Ragavan Anbuchezhian, Glenn Hawe, J. Liu

Research output: Contribution to conferencePaperpeer-review

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Abstract

The traditional process of estimating piles and beams from architectural floor plans is a manual, time-consuming task that is prone to human error. Accurate estimation is vital for effective planning and execution of construction projects. It helps in scheduling, resource allocation, and logistics, ensuring that the construction process is smooth and efficient. The need for an automated process to enhance efficiency and accuracy is a primary problem being addressed. This paper presents an innovative framework for automating the estimation of the optimal number of piles and beams from architectural floor plans using advanced image processing and structural analysis techniques. Leveraging a deep segmentation network with a context-aware classifier, we enhance the accuracy of identifying key structural elements in floor plans. Our method involves a three-stage process: extracting structural elements, vectorizing floor plans, and identifying load-bearing walls. We employ a thinning algorithm and contour reduction techniques for precise vectorization, and our approach in determining room spans assists in accurately locating load-bearing walls. This methodology not only streamlines the estimation process of foundational
elements but also introduces a novel way of integrating deep learning with architectural engineering, setting a new standard in construction planning. Preliminary results demonstrate a significant reduction in time and error margins compared to traditional methods, showcasing the potential of our framework
to revolutionize construction planning and execution.
Original languageEnglish
DOIs
Publication statusPublished online - 5 Jun 2024
EventThe 25th IEEE International Conference on Industrial Technology - Bristol, United Kingdom
Duration: 25 Mar 202427 Mar 2024
https://icit2024.ieee-ies.org/

Conference

ConferenceThe 25th IEEE International Conference on Industrial Technology
Abbreviated titleICIT 2024
Country/TerritoryUnited Kingdom
CityBristol
Period25/03/2427/03/24
Internet address

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • architectural floor plans
  • context-aware classifier
  • piles and beams estimation
  • segmentation network
  • structural analysis
  • vectorization

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