Improving Project Scheduling and Material Ordering with Hyperparameter Optimization

Understanding Construction Progress and Material Appearance Using Deep Learning Models
Construction sites are complex and dynamic environments where various activities take place, making it challenging to accurately monitor progress and material appearance. To address this challenge, researchers have turned to deep learning models, such as GRU RNN-based models, which can capture patterns in space utilization, worker skills, equipment availability, and material supply delays. However, these models are only as good as the data they are trained on, and a limited dataset might not cover all possible scenarios or task patterns.
To improve the accuracy of deep learning models, it is essential to collect and use larger and more diverse datasets from various construction activities across different types of sites. This will help increase the robustness of the model, allowing it to capture a broader range of activity patterns and relationships. Moreover, integrating other relevant factors, such as worker skills, equipment availability, or material supply delays, can produce a comprehensive approach with enhanced predictive accuracy.
In summary, deep learning models have shown promising results in estimating performance and productivity in construction work. However, their effectiveness relies on the quality and diversity of the training data. To improve the accuracy of these models, it is crucial to gather and utilize larger and more diverse datasets from various construction activities across different sites. By doing so, we can create a more comprehensive approach that takes into account multiple factors influencing construction progress and material appearance.