To extend the model to predict the thermal conductivity

The gap

Evidence profile

Sourced from the future-work section and stated research gap of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Mô hình cơ - nhiệt dự báo sự thay đổi hệ số truyền nhiệt của bê tông dưới tác dụng của nhiệt độ cao (2026) · Journal of Construction · doi

  To extend the model to predict the thermal conductivity of concrete under other conditions, such as moisture content and chemical degradation. To validate the model against experimental results for a wider range of temperatures and mechanical stresses. To apply the model to other types of concrete and cement-based materials.

  generalfuture-work sectionevidence 5/5
  Keywords: extend model predict thermal conductivity concrete other conditions
• Prediction of Thermal Degradation in Concrete Structural Elements Using Optimized Artificial Neural Networks and Metaheuristic Algorithms (2026) · Buildings · doi

  Previous studies have generally focused on conventional machine learning applications or limited optimization strategies. There is a lack of integrated frameworks combining systematic input screening, robust validation, large-scale metaheuristic optimization, and interpretable analysis. The need for a comprehensive predictive framework for thermal degradation in concrete remains unaddressed.

  generalstated research gapevidence 5/5
  Keywords: previous studies have generally focused conventional machine learning
• Predicting the strength of waste aggregate concrete blocks using novel hybrid machine learning models and graphical user interface deployment (2026) · Scientific Reports · doi

  The need for more accurate and robust machine learning models for predicting compressive strength of waste aggregate concrete blocks. The lack of novel hybrid machine learning models that can provide excellent predictive accuracy. The need for a user-friendly interface to make predictive models accessible for practical engineering applications.

  generalstated research gapevidence 5/5
  Keywords: need accurate robust machine learning models predicting compressive

Questions about this gap