Application of Machine Learning for Scaffolding Risk Assessment by Navi-Bais Algorithms

Abstract

Development in Artificial intelligence is providing an unprecedented opportunity to enhance highly advanced computational methods in various fields. The application and prediction require a good availability of qualitative and quantitative data. The construction industry is associated with occupational risks and hazards. The occupational physical hazards involved in scaffolding works are falls and slips, hitting of falling object etc. The existing paper provides and analyze the software of machine learning algorithms in risk evaluation of scaffoldings about workers in construction industry. The consequences associated   with   such incidents are near miss, major and minor injuries, and fatalities also.  scaffolding is a platform where the worker performs different activities like brickwork, painting, plumbing etc. In present studies and examined 150 construction workers to predict the risk assessment in scaffolding based on quantifying the occupational consequences with respect to age, experience and demographic factors, risks associated with the worker involved. Different types of scaffolding works involved in the study are wooden (Bamboo) – single and double scaffolding, steel, suspended (Jula) and trestle scaffolding. The methodology follows starting from collection of data with on-site survey, workers involved in the different construction activities. The risk analysis and assessment were done with application of machine learning using naive bayes algorithms. Based on the experience of a worker the study predicts, the high number of slips and falls is involved with wooden type of scaffolding.