AI-Driven Innovations in Tunnel Construction and Transport: Enhancing Efficiency with Advanced Machine Learning and Robotics

Tunnel construction is a high-risk, complex task requiring precision, safety, and efficiency. With growing infrastructure demands, this study proposes a hybrid framework integrating Building Information Modeling (BIM), machine learning models such as Artificial Neural Network (ANN), K-Nearest neighbors (KNN), Support Vector Machines (SVM), and advanced optimization techniques to improve decision-making, predict geological challenges, and automate key operations in large-diameter tunnel projects, enhancing overall project performance and risk management.

Various methods are employed in the study, including BIM, machine learning, and robust optimization, which can be perceived as enhancing tunnel construction. Prediction using AI-based algorithms, namely ANN, KNN, and SVM, was made possible with real-time sensor data on geological issues. FANUC ROBOGUIDE software was also used to simulate the actions of robots, ensuring that material handling was performed with precision. Among these three, the optimal performance of SVM outshines ANN and KNN.

The results have shown that BIM integrated with machine learning and optimization significantly increased tunnel construction performance. In predicting critical operational parameters, AI-based models, especially SVM, were found to provide an accuracy of 98.56%, outperforming KNN and ANN. Hence, this kind of predictability may allow for real-time modifications in the Tunnel Boring Machines (TBM) settings, thereby decreasing the risks associated with geological uncertainties. Additionally, the FANUC ROBOGUIDE software will ensure more precise and collision-free material handling, further enhancing safety and efficiency in tunnel construction projects.

The study demonstrates that integrating BIM with machine learning and robotic simulation significantly enhances tunnel construction efficiency and safety. Among the models evaluated, SVM achieved the highest accuracy (98.56%) in predicting geological challenges. Real-time data processing enabled timely adjustments to TBM operations, while FANUC ROBOGUIDE ensured precise material handling, reducing risks and delays in complex construction environments.

The research currently underway has established the efficacy of integrating BIM, machine learning, and optimization in improving tunnel construction. The applications of AI models, such as SVM, KNN, and ANN, have improved targeted operational parameters and reduced geological risks, with SVM yielding the highest accuracy at 98.56%. Efficiency and safety were further enhanced by real-time data-driven decisions and robotic simulations. The developed framework offers a practical solution for enhancing decision-making and operational efficiency in complex engineering projects.