This is the second in a series of blogs about Multivista’s industry-leading Automated Firestopping Assessment.
Firestopping Status Quo
Construction quality issues can be severe, numbering in the thousands. For example, a 500-unit residential project could have 20 quality issues logged per unit. This equates to 10,000 punch list issues that must be identified, tracked, and rectified.
Photo courtesy of Plan Radar
These issues are typically divided into two categories: those discovered prior to handover, and those uncovered afterwards.
The first category incurs rework costs and project delays, while the second harms a contractor’s reputation, as well as ongoing client relationships. Both represent missed opportunities for a construction company.
Furthermore, poor-quality work can have far-reaching consequences, particularly when it comes to firestopping as these issues can be tedious, hazardous, and even life-threatening.
According to a National Fire Protection Association (NFPA) report, structure fires accounted for 37% of all fires in the United States in 2019, resulting in 2,980 deaths and $12.3 billion in property damage. In the United Kingdom, the most common cause of death for fire-related fatalities in 2020 (where cause of death is known) was due to victims being ‘overcome by gas or smoke.’ This was reported in 30% (73) of fire-related fatalities.
Photo courtesy of Casalinova Investigations
The second category is that many fire-related deaths are attributed each year to inadequate flame and smoke containment, which can occur due to poorly sealed joints and wall or ceiling penetrations for plumbing, HVAC, electrical, communications, and other building trades.
Unsealed Penetrations. Photo courtesy of Firestop Contractors International Association
The future of Firestopping
Best practices utilizing Artificial Intelligence (AI) to reinvent construction quality control have progressed far beyond a futuristic concept and are now a jobsite reality.
Using a suite of established detection technologies including drones, laser-scanning, and photo documentation, construction professionals can create a vast image database that acts as a baseline for AI engines to perform exterior and interior assessments that offer unprecedented insight into the construction process.
In recent months, AI has made significant contributions to firestopping assessments and assisted in resolving many firestopping issues. In this section, we will highlight some of these issues and demonstrate how artificial intelligence is assisting contractors in controlling the quality control of the installed firestopping measures.
Firestopping subcontractors, for example, may begin work five to six months before the project’s completion date. As we all know, many changes can occur in the final months of any job, including last-minute installation and design modifications, which can affect firestopping measures.
Furthermore, when an inspector comes to review the firestopping subcontractor’s work, their report may identify issues that cause the owner to delay payment and disrupt the project schedule until the deficiencies are fixed. Watch this video to learn how to avoid construction delays caused by firestopping.
In addition, the time and labor it takes to manually discover, assess, and resolve firestopping measures and issues can easily carry over and delay the work of the next subcontractor due on site, causing a domino effect of costly delays.
Manual firestopping assessment. Photo courtesy of International Firestop Council
Moreover, if one of the measures is overlooked and a serious fire ever results, the firestopping subcontractor could be subject to substantial legal claims, which would harm their reputation and bottom line.
As a result, AI-automated detection can help prevent delays caused by firestopping issues while preserving existing workflows, reducing rework, increasing margins, increasing productivity, and providing greater control over the quality management process.
Firestop Software. Photo courtesy of Hilti
AI Technologies for Firestopping
Superior Computer Vision
AI-powered technologies can detect even the smallest unprotected penetrations that humans may not notice. The AI can also classify and precisely locate multiple defect types in a single image, saving construction specialists time and effort.
Machine Learning Algorithm detects unsealed penetrations
Construction productivity is an important metric on any jobsite as labor costs account for 30% to 50% of the total cost of a construction project.
According to industry analysts at McKinsey & Company, incorporating AI technologies such as automation can help construction companies increase productivity by 30–40% and deliver a 15–25% reduction in costs.
With Firestopping Assessments, automating the creation of tasks for each unsealed penetration discovered by computer vision would empower specialists to correct them as soon as possible, saving time and money.