AI-augmented project controls: Enhancing predictability in complex tunnel construction environments
Carlson Construction Group, Canada.
Review Article
World Journal of Advanced Research and Reviews, 2022, 13(02), 649-668
Publication history:
Received on 23 December 2021; revised on 10 February 2022; accepted on 12 February 2022
Abstract:
Construction of tunnels is one of the most complicated and risky tasks of civil engineering, and it is not predictable due to uncertainties of geological conditions and logistical difficulties. It analyzes the need for more predictive and controllable tunnel projects using artificial intelligence (AI) in project management. The conventional venture control techniques were found lacking, as they are receptive and for the most part, utilize obsolete or siloed information. In any case, AI-controlled, extended control is based on aggressive decisions using real-time information, machine learning and careful analysis. Analyzing tunnel excavators (TBMS) and location sensors to analyze the sum of AI's vast data, designs can be observed and estimated before potential future problems become devastating. Teams can resolve issues before they stop and become expensive. Success Stories for Using AI Predict TBM Performance and Optimize Maintenance and Communication Between All Stakeholders Presented in the Form of Case Studies. In addition, real-time monitoring systems are used with artificial intelligence, which is equipped to analyze data obtained by sensors, monitor the operation, and continuously analyze sensor data to improve security by recognizing inconsistencies that an administrator might otherwise miss. In addition to simplifying activities, this method promotes data -based culture, allowing project managers to manage their resources more effectively and, to a certain extent, to predict risks. Ultimately, AI does not replace human operators; it transforms conventional project management into a more dynamic and responsive.
Keywords:
Tunnel Construction; Artificial Intelligence; Machine Learning; Predictive Maintenance; Project Controls; Real-Time Monitoring
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0