Awareness and adoption readiness of machine learning technology in the construction industry of a developing country: a case of nigeria

Abstract

This study investigated the awareness of the Nigerian construction organisations on some identified ML application areas, and the readiness of the organisations to adopt ML learning in the identified application areas. A comprehensive Literature review was undertaken to identify the application areas of ML, then, a well-structured questionnaire was developed and used to gather relevant data from construction professionals using the snowball sampling method via electronic means. 143 valid responses were obtained, and the gathered data were analysed using arrays of descriptive and inferential analytical tools. The study revealed that the critical applications areas of ML with higher awareness level and adoption readiness in Nigeria are (1) Health and Safety prediction and management, (2) Waste management, (3) Prediction of and management of construction costs, (4) Risk Management, (5) Structural Health Monitoring and Prediction, and (6) Building Life-Cycle assessment and management. Further, a significant statistical difference was observed between the opinions of the participants regarding the awareness and adoption readiness of the various ML application areas.  This study identified critical application areas of ML where the awareness and adoption readiness are very high, thus, signalling the preparedness of the Nigerian construction industry (NCI) to embrace ML to drive sustainable construction.