Details

Autor(en) / Beteiligte

• Schönfelder, Phillip
• Aziz, Angelina
• Bosché, Frédéric
• König, Markus

Titel

Enriching BIM models with fire safety equipment using keypoint-based symbol detection in escape plans

Ist Teil von

• Automation in construction, 2024-06, Vol.162, Article 105382

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In the context of fire safety inspections, Building Information Modeling (BIM) models enriched with Fire Safety Equipment (FSE) components can be used to complete compliance checks and other analyses. However, BIM models often lack the required FSE information. To address this issue, escape plans are a convenient source of data, as they show the position and type of FSE on floor plans. Therefore, this study proposes an automated method to analyze escape plans and extract FSE component information to enrich existing BIM models. The method employs the deep learning model Keypoint R-CNN for symbol detection. Symbol locations are then translated into physical positions within the BIM model. Through a real-building case study, the method demonstrates promising results. Future research may focus on improving the symbol detection performance and the registration between the BIM models and fire escape plans, as well as utilizing the extracted information for actual fire safety analyses. •Proposing a method to extract fire safety equipment locations from escape plans.•Employing Keypoint R-CNN to detect symbols and locate associated fire safety equipment.•Integrating the identified equipment to existing BIM models at its physical location.•Demonstrating the method through its application on a university building.•Suggesting the integration of escape plan analysis with on-site autonomous fire safety inspections.