viAct’s Scenario-Based AI Effectively Identifies Cracks in High Rise Buildings

Updated: Nov 8, 2021


viAct’s Scenario-Based AI Effectively Identifies Cracks in High Rise Buildings
viAct’s Scenario-Based AI Effectively Identifies Cracks in High Rise Buildings


Construction of high-rise properties is a tough job; the post construction maintenance is even a bigger challenge. With a disproportionate growth in global population with respect to global residential land, the high-rise buildings and skyscraper culture have popularized worldwide. However, maintenance of these building is a tedious and error prone task.


What is the most common maintenance issue associated with high rise buildings?

Disaster Doc reports 8 high rise buildings collapse disasters occurring every year worldwide, resulting in 343 deaths/year and killing on an average 38 persons in each event. The major cause of these collapse disasters has been improper or neglected maintenance. The major issue arises with the detection of external cracks in high rise buildings. External cracks in building hamper the structural health of concrete structures and if they continue to proliferate, the effective load bearing capability gets reduced. This leads to failure of the structure in long run. However, manual process of crack detection is next to impossible due to the dimensional complexity of the buildings and also is prone to subjective judgments of inspectors. This has paved the way for Artificial Intelligence of Things (AIoT) with PropTech being the talk of the town in the real estate ecosystem.


What is the implication of AIoT in PropTech?

AIoT is the amalgamation of artificial intelligence (AI) and Internet of Things (IoT) which has been utilized for achieving enhanced IoT operations, enhancing human-machine interactions for an effective data management and analytics. PropTech has a major application of AIoT in terms of maintenance of properties larger enough to be monitored by human inspection. The technology is based on equipping IP cameras with a standard computer vision, later the cameras are customized in accordance with specific use cases by training it with deep learning algorithms. Thus, AIoT is an appropriate way to personalize the IoT devices utility for purpose of defect detection in critical facility maintenance tasks like external crack detection of high-rise buildings.



How viAct’s Scenario based AI is effective in crack detection in high rise buildings?

“Our mission is to ensure jobsite safety covering complete life cycle of premises from first brick to last breath”, says Gary Ng, CEO, viAct.
“Our mission is to ensure jobsite safety covering full life cycle of premises from first brick to last breath." - Gary Ng

In this connection, number of modules has been crafted for the real estate monitoring purposes. viAct’s Wall Crack Detection Module is one amongst them. It is a tailor-made solution that is well-trained to detect minutest of crack(s) occurring even in the exterior of the high-rise buildings using any drone camera. This Wall Crack Detection Module is a scenario based AIoT solution that aids error free and real time post-construction maintenance with easy plug & play facility that allows even a non-technical person to make use of this module.


The drone camera when powered by viAct’s AI processor becomes smart enough to detect crack(s) occurring even in the exterior of a building. Further, connecting to viAct’s smart AI cloud the captured insights of the crack(s), is immediately notified to facility management office as well as to the property owner for urgent action. viAct’s integrated dashboard helps to maintain a record of every minutest crack detected for better planning of post-construction maintenance.


viAct’s scenario-based AI is capable to work effectively in:


· Difficult viewing angles

· Poor light situations

· Broad spectrum of weather conditions

· Open-air

· Long-distance conditions


Thus, viAct’s wall crack detection module is a best fit for post-construction maintenance of any high rise building due to its ability to detect 10x more defects than normal human inspection.
 

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