I am a researcher and engineer focused on the intersection of artificial intelligence, machine learning, geotechnical engineering, and tunnel boring machine (TBM) technology. My work centers on developing data-driven solutions that improve tunneling performance, operational efficiency, predictive maintenance, and underground construction decision-making.

With a strong background in TBM mechanics, rock cutting processes, geomechanics, and data analytics, I investigate how advanced machine learning techniques can transform traditional tunneling practices into intelligent, autonomous, and adaptive systems.

My primary research areas include:

• Artificial Intelligence for Tunnel Boring Machines (TBMs)
• Machine Learning in Geotechnical and Underground Engineering
• Cutter Wear Prediction and Predictive Maintenance
• Lithology and Ground Condition Identification
• Autonomous TBM Operation and Navigation
• Digital Tunneling and Smart Construction Technologies
• Data-Driven Rock Mechanics and Geomechanics
• Predictive Analytics for Tunnel Performance Optimization
• Explainable AI for Engineering Applications
• Deep Learning for Underground Infrastructure Projects

I am actively developing and evaluating advanced Physics-Informed Neural Networks (PINNs) and AI-powered tunnel support recommendation systems to enhance tunnelling operations, geotechnical engineering analysis, and underground construction decision-making. My research focuses on integrating engineering principles, field data, and machine learning to create intelligent, explainable, and reliable solutions for complex subsurface environments.

Current research and development projects include:

• Physics-Informed Neural Networks for geotechnical and tunnelling applications
• Automated tunnel support system recommendation using machine learning and engineering knowledge
• AI-based cutter wear prediction and maintenance optimization
• Automated lithology and ground condition classification using TBM operational data
• Feature engineering and predictive analytics for tunnelling performance datasets
• Anomaly detection and operational risk assessment in TBM excavation processes
• Machine learning frameworks for geotechnical decision support and digital tunnelling
• Intelligent excavation systems and autonomous TBM advancement technologies
• Data-driven geomechanics and rock mass characterization
• Explainable AI applications for underground construction and infrastructure projects

• Python
• Data Analysis and Statistical Modeling
• Scientific Computing
• Large Language Models
• Vision Language Models

• Scikit-learn
• TensorFlow
• PyTorch
• Supervised Learning
• Unsupervised Learning
• Deep Learning
• Predictive Modeling

• Pandas
• NumPy
• Matplotlib
• Seaborn

• Tunnel Boring Machines (TBMs)
• Geotechnical Engineering
• Rock Mechanics
• Rock Cutting Processes
• Underground Construction
• Tunneling Engineering
• Geomechanics
• Ground Characterization

I welcome collaboration opportunities with researchers, universities, engineering consultants, contractors, equipment manufacturers, and technology companies working in:

• Artificial Intelligence for Civil Engineering
• Tunnel Boring Machine Research and Development
• Smart Tunneling and Digital Construction
• Geotechnical Data Analytics
• Machine Learning Applications in Infrastructure
• Autonomous Construction Systems
• Underground Space Engineering

Email: kilic_kursat@hotmail.com

GitHub: https://github.com/kilickursat

Advancing the future of underground construction by integrating artificial intelligence, machine learning, and geotechnical engineering to create safer, smarter, and more efficient tunneling systems.