Abstract

This research paper seeks to observe the manner in which construction quantitative research can be enhanced by the use of artificial intelligence as well as robotics in an endeavor to reduce detrimental or monotonous tasks traditionally executed by manpower. It examines papers and cases on AI and robot technologies designed for application in the construction domain. It assesses statistics from industry reports and government agencies to measure prevailing labor issues on safety, workforce mode, and restrictions on productivity that prevail in the industry. The data from live pilot projects and recorded case studies incorporating artificial intelligence and robot-supported construction projects are also reviewed. Construction is earlier accomplished with human muscles to undertake strenuous tasks at construction worksites. This focus has brought out a number of problems of safety, budget, and output, all of which are deeply rooted in labor issues. The physically demanding and risky nature of the jobs has, however, made it an even bigger challenge for the management to attract and retain a competent workforce. There is a skills shortage, which has caused early project delays and cost hikes. But relying strictly on manual tasks also limits how big and how quickly construction can happen. The studies on 3D printed construction parts such as walls and floors suggest that additive construction may help to speed up assembly by automating fabrication-intensive tasks. Contracting industries have also undergone the procurement of collaborative robots commonly known as ‘cobots,’ which are designed to work with humans to gradually automate repetitive work such as movement of goods by heavy lifting robots and lift trucks, as well as applying exoskeletons to lift loads between floors, as some research has shown. The research further notes that, in their physical form, artificial intelligence and robotics have developed some measure of accomplishment in replacing very rigorous, risky construction tasks. It is computer vision and machine learning approaches to design systems for constant safety inspections and detection of mistakes, progress monitoring, and determination of when equipment might require servicing. Exoskeleton robots also enhance human capacities by reducing physical burdens, as other research on such systems also indicates.