FTL's Structural Damage And Repair Inferencing Tool (SDARIT) leverages FTLs 2D and 3D computer vision and machine learning pipeline. It provides an automated pier repair planning tool that accepts inputs from remote sensing systems and is capable of identification, volume approximation, location, and tabular summation of defects, all approximated from conventional construction practices. SDARIT can inventory individual structural elements and indicate certain types of damage including battle damage, explosive effects, scouring, and fatigue while requiring only raw point clouds as input, acquired in the field through either manned or unmanned surveys. This system addresses an immediate Navy need for Port Damage Repair (PDR), which commonly includes gathering of 3D point clouds. Currently, all key details of the scanned data are interpreted and entered through a process that is both manual and laborious. FTL proposes that this bottleneck can be alleviated by automatically converting structural 3D scan data into actionable Battle Damage Assessments (BDAs). SDARIT combines classically-coded algorithmic point cloud dissection and pattern manipulation with neural network part detection and damage recognition. It does not rely on libraries of pre-modelled mechanical components, but addresses the inherent variability and lack of uniformity, precision, and accuracy in port structures. SDARIT will deliver volumetric construction details on both existing and missing components, while differentiating design variability from gross defects and battle damage.
Benefit: The proposed technology, when implemented, will allow a significant enhancement in structural damage surveying; at first for piers, but eventually across a wide variety of architectural structures. In addition to providing value to the Navy by accelerating the tempo and efficiency of repairing battle theater ports of opportunity, SDARITs enabling technology has applications far beyond piers and battle damage. SDARIT technology is perfectly suited as a plugin or addon for existing Geographic Information System software applications, a $6.3 billion industry. Additionally, the basic insight for using physics-based structural pattern recognition to drive Machine Learning (ML) recognition algorithms has applications throughout structures, including commercial and residential buildings, and extends to damage preparedness associated with earthquakes and climate-related disasters. This represents a market of over $20 billion per year and rising rapidly. In applications that require rapid characterization of structural damage, FTL Labs (FTLs) Structural Damage And Repair Inferencing Tool (SDARIT) system can find a significant market. FTL believes that the proposed Navy application is the perfect launchpad for a disruptive software enterprise rivaling Autodesk ($4B) and Bentley ($1B).
Keywords: Repair Planning Tool, Point Cloud Conversion, Structural Damage And Repair Inferencing Tool (SDARIT), Machine Learning (ML), Building Information Modeling (BIM), 3D Point Cloud Data, battle damage assessment (BDA), Expeditionary Pier Repair
