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Infrastructure systems in the United States are aging and considerable investment is needed to renew and replace a significant proportion of the existing systems. Piping systems, which are used in many infrastructure systems such as the distribution networks for utilities – water, sewage, gas, oil, etc., are very important in this regard. Real time scheduling is an important and necessary task in the planning and execution of construction projects. This is of particular importance in the installation of pipe systems, for which it is time consuming to plan and coordinate between team members the detailed requirements and information for the generation of practical installation schedules. During the installation stage, there can be delays or interference that could lead to the failure of the initial schedule plan. Current approaches are time-consuming, not automated and do not provide real-time schedules. Thus, the process is still fragmented and essentially manual, with inefficient information flow. To effectively improve the installation schedule, current knowledge of the installation site situation is important, with this knowledge being used to generate realistic schedules. Artificial intelligence (AI) maximizes the value of data by learning from previous cases and facilitates decision-making by making the process smarter and automatic. This paper proposes a new AI framework with machine learning (ML) and heuristic optimization techniques for automating practical pipe system installation schedule generation and optimization. A BIM model is used as reference to provide pipe system component information. A hybrid knowledge-based system is developed to integrate data-driven knowledge base and site-driven knowledge base on pipe system installation. K-Nearest Neighbor (KNN) and Graph Neural Network (GNN) ML techniques are adapted to map extracted components with the installation activities and their requirements for installation based on knowledge obtained from industry experts and piping codes. In addition, a heuristic algorithm is adopted to optimize the installation schedule. Finally, an optimal installation schedule that minimizes overlapping activities, time and cost is suggested.

Real-time pipe system installation schedule generation and optimization using artificial intelligence and heuristic techniques