AI-powered inspections of facades in reinforced concrete buildings

Abstract

Worldwide there are plenty of aged Reinforced Concrete (RC) buildings in need of thorough inspections. Cracks, delamination, stains, leakages, debonding and moisture ingressions are common defects found in RC structures. Such problems are typically diagnosed through qualitative assessment of visual and thermal photographs (data) by certified inspectors. However, qualitative inspections are very tedious, time-consuming and costly. This paper presents an alternative novel approach to drastically increase efficiency by decreasing the data collection and analysis time. Data collection for the inspection of facades is undertaken with Unmanned Aerial Vehicles (UAVs) either through an autonomous pre-programmed flight or through a human-piloted flight. Data analysis is performed by implementing up-to-date AI-powered algorithms to automatically detect defects on visual and thermal photographs. All the recognised defects and thermal anomalies are labelled on the building facade for comprehensive evaluation of the asset. This paper reports that the implementation of AIpowered inspections can save up to 67% of the time spent and 52% of the cost in comparison to the most commonly adopted practice in the industry with an average accuracy of 90.5% and 82% for detection of visual defects and thermal anomalies, respectively.