Graph machine learning applications in construction engineering research for 2024 have seen advancements in various areas. One notable application is in project scheduling optimization. Graph-based models can analyze dependencies between construction tasks, allowing for more efficient scheduling and resource allocation.

Another application involves risk management, where graph algorithms help assess and mitigate potential risks by modeling relationships between different project factors. This aids in identifying critical points and improving overall project resilience.

In structural health monitoring, graph-based machine learning is utilized to analyze data from sensors placed on structures. This enables real-time detection of anomalies, helping in predictive maintenance and ensuring structural integrity.

Furthermore, graph models are applied in supply chain optimization for construction projects. They help optimize the flow of materials, reducing delays and improving overall project efficiency.

Overall, the integration of graph machine learning in construction engineering research enhances decision-making processes, efficiency, and risk management in the field.

Dear Kareem Adel ,

Graph Neural Networks (GNNs) have emerged as a promising solution for effectively handling non-Euclidean data in construction, including building information models (BIM) and scanned point clouds. However, despite their potential, there is a lack of comprehensive scholarly work providing a holistic understanding of the application of GNNs in the construction domain.

Article Graph Neural Networks for Construction Applications

Regards,

Shafagat

Graph neural networks, a form of machine learning, have proven to be particularly potent in the construction engineering sector. By representing decision-making tasks as input networks, these models bring a new level of sophistication to project planning and execution.

For a broader perspective, you might consult the review article from last year, and for a more focused view, our research has been pioneering the use of these networks in construction management.

I would like to recommend you automated technology of construction management "Building Manager" - construction modelling based on complex intellectual models (CIM) – in our case, the Dynamic Resource - Organizational and Technological Model of Construction – digital modelling of building projects which can facilitate organisational modelling and automated scheduling in project management. BIM models, as initial data, can be successfully used in complex intellectual models for automated generation of PERT diagrams and Gant charts, for automated planning of the flow and sequences of tasks in the building projects.