Deep Learning Algorithms for the Monitoring and Control of Drilling and Grouting Works-PPT Presentation

PPT Presentation

Pdf Summary

The presentation describes a data-driven system for monitoring and improving drilling and grouting operations. Developed using data from the Mosul Dam project in Iraq and the Rogun Dam project in Tajikistan, the system collects and stores operational data in real time. A scalable NoSQL database and data-processing tools prepare the records for machine-learning analysis.<br /><br />The researchers explored neural-network methods, focusing on Long Short-Term Memory (LSTM) networks because grouting is a sequential process: effective pressure and flow rate at any point can depend on earlier conditions. LSTMs use memory mechanisms to capture these time-based relationships and predict future values, potentially supporting more precise control of each grouting stage. Models were evaluated using mean squared error (MSE).<br /><br />The LSTM model achieved an MSE of 0.00183 for effective-pressure prediction and performed better than the linear regression and ARIMA models considered. Additional dataset examples reported MSE values of 0.0324 and 0.0526. The results suggest that predicting pressure and flow rate could help reduce operational errors and improve safety and efficiency in critical infrastructure work.<br /><br />Future development could test whether the approach generalizes to other construction activities, such as foundation and compensation grouting. The researchers also propose including drilling data and integrating measurements from settlement monitoring and other geotechnical instruments. Combining these inputs with real-time predictions could enable dynamic adjustments during construction.

Keywords

drilling operations

grouting

Mosul Dam

Rogun Dam

real-time monitoring

NoSQL database

machine learning

LSTM

pressure prediction

flow rate prediction