Transforming Post-Construction Practices: The Role of AI in Maintenance, Monitoring, and Predictive Analytics for Sustainable Infrastructure

Abstract

In pursuit of the recognition of how Artificial Intelligence (AI) could enable and improve events after construction stages, this work will focus on predictive analytics, performance monitoring, and the maintenance of buildings. The quantitative approach is used by this research and it covers surveys, analysis by statistics, as well as case studies aiming to discover AI adoption rates, the issues, expected value, and impact on the performance of infrastructural elements and maintenance. The findings demonstrate the high rate of the application of AI technologies in post-construction stages which have several benefits, among which are smaller maintenance costs, fewer downtimes, and longer infrastructure life length. Regression analysis and descriptive statistics are the two types of statistical analysis that are very important for reviewing AI algorithms's effectiveness in predictive maintenance and efficiency of resources. AI’s ramifications are presented in the form of actual scenarios, observation of which gave rise to the noticeable trend of fewer unplanned shutdowns, higher efficiency, and lower operational costs. The study notes that AI is likely to lead to efficiency gains and also render novel opportunities in post-construction phases which may be taken advantage of to develop robust and sustainable management strategies for infrastructure. To fully reap the benefits of AI in transforming the functions of management, cooperation between the industries and long-term investment in the solutions powered by AI is the key.