By Luke Carothers
When water leaks, it has the potential to wreak thousands, if not millions, of dollars in damages to buildings, inventories, and job sites. In the fight to monitor and detect leaks from occurring, innovations in the fields of artificial intelligence and machine learning enable facility and project managers to access more knowledge than ever before–allowing for real time decision making that reduces risk. WINT is at the forefront of this movement; it is a leading water conservation and leak detection tool. WINT seeks to solve water problems in buildings and facilities, moving from the construction phase into the actual occupation of the building. This means that when construction is completed, WINT continues working and transitions to the operational phase.
Yaron Dycian, WINT’s Chief Product and Strategy Officer, believes that WINT is in a perfect position to reduce risk in an industry that is just now turning its focus to water leak prevention and mitigation. Since its inception, WINT has detected over 425,000 leaks collectively for their clients all over the world; of these events, over 5,000 could have resulted in significant damages to the buildings. WINT estimates this has resulted in between $100-$200 million in damages prevented.
WINT saves for its clients by utilizing a core technology that tracks variations in water use, operating under the assumption that there are a number of different kinds of water use–sinks, toilets, washing machines, irrigation, etc. WINT is then able to use artificial intelligence to record patterns of water usage to indicate leaks and/or other forms of waste. WINT’s utilization of artificial intelligence has significant ramifications for sustainability. In a time when water conservation is a more and more pressing issue, WINT’s system has saved nearly 1 billion liters, or 250 million gallons, of water so far.
WINT can also learn through multiple functions, using pre-established databases to create flexible action policies, which are useful in projects that have varying usage patterns. Furthermore, when WINT detects a leak or waste, it applies the same artificial intelligence to offer solutions and take action. A problem such as a broken water pipe in the wall that happens during off hours can be switched off by the technology, saving money and time for the crew,
WINT was founded in 2012 and was initially focused on residential homes. Since then, however, WINT has expanded their client base to include large general contractors and other larger groups wanting to prevent water loss and detect links. One such client is Tidhar Construction, who employed WINT’s system on multiple residential and commercial projects. Tidhar, a $1 billion general contractor, faced several water leak issues before employing WINT. Tidhar has deployed WINT at more than 40 sites over the last 3 years.
Out of the 40 sites where Tidhar deployed WINT, there were 7 major leaks and 18 minor leaks were detected. WINT has performed so well that it is now a mandatory requirement for all Tidhar sites and projects. The resounding success of WINT at these sites has resulted in not only a complete stop to water damage, but also a reduction in wasted water spending. As a result, the firm received favorable insurance rates because of the prevention of water damage.
According to Dycian, the biggest cause for the rise in premium rates amongst construction projects is the potential for water damage. Thus, it is not unreasonable to assume that more projects will turn to artificial intelligence and machine-learning to solve the problems of unused water and water damage.
Luke Carothers is the Editor for Civil + Structural Engineer Media. If you want us to cover your project or want to feature your own article, he can be reached at firstname.lastname@example.org.