# Digital Twins' Application for Geotechnical Engineering:
## 2 The Current Situation of Digital Twins in Geotechnical Engineering
\begin{table}
\begin{tabular}{c c c c} \hline
**Domain** & **Key Example** & **DT Benefits** & **Remaining Gap** \\ \hline Urban Construction & \begin{tabular}{c} The four-dimensional \\ geological model of Jinan’s \\ Spring Area. \\ The construction of a \\ digital twin model in \\ Xiong’an New Area. \\ \end{tabular} & \begin{tabular}{c} Optimize planning \\ and design. \\ Visualization of \\ construction process. \\ Real-time monitoring of \\ operation and \\ maintenance stage. \\ \end{tabular} & Difficult to obtain urban \\ Transportation & \begin{tabular}{c} Digital twin system \\ architecture of \\ Ehan Expressway, \\ Urumqi Airport \\ \end{tabular} & \begin{tabular}{c} Accurate decision-making \\ of operations and \\ maintenance. \\ Construction dynamic \\ optimization. \\ \end{tabular} & \begin{tabular}{c} High cost of \\ data acquisition. \\ Dynamic load \\ simulation limitations. \\ \end{tabular} \\ \hline Hydraulic Engineering & \begin{tabular}{c} Digital twin of Gezhouba \\ Hydropower Station \\ and Baihetan \\ Hydropower Station \\ \end{tabular} & \begin{tabular}{c} Seepage-stress \\ coupling analysis. \\ Predictive \\ maintenance decisions. \\ \end{tabular} & \begin{tabular}{c} The computational \\ complexity of \\ hydraulic-rock coupling \\ is high. \\ \end{tabular} \\ \hline Mining Industry & \begin{tabular}{c} Intelligent mine operating \\ system of Wangialing coal \\ mine and Sanshandao \\ gold mine. \\ \end{tabular} & \begin{tabular}{c} Slope slip warning \\ (multi-parameter fusion). \\ Real-time optimization of \\ mining scheme. \\ \end{tabular} & \begin{tabular}{c} Difficult to realize \\ three-dimensional \\ geological–tectonic \\ integration and real-time \\ data assimilation. \\ \end{tabular} \\ \hline Petroleum Engineering & \begin{tabular}{c} Digital twin system of \\ China petroleum oil and \\ gas exploration platform. \\ \end{tabular} & \
\begin{tabular}{c} Real-time correction of \\ drilling trajectory. \\ Wellbore stability \\ prediction. \\ \end{tabular} &
\begin{tabular}{c} The acquisition cost of \\ deep-sea geological data is \\ high, and the update \\ is delayed. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: The characteristics of the applications of digital twins in geotechnical engineering across various fields. engineers to conduct predictive modeling of potential geotechnical complications during the preliminary design phases. This capability drives preemptive design optimization, effectively eliminating latent safety risks that might otherwise manifest during subsequent construction or operational stages. Design Optimization and Decision Support  
During the design phase of geotechnical engineering, digital twin technology integrates field investigation data and laboratory test results with machine learning algorithms to conduct comparative multi-scenario analyses. This approach optimizes critical design parameters (e.g., support treatment schemes, material selection) through data-driven decision-making, enabling scientifically rigorous and precision-engineered solutions. Additionally, it leverages virtual reality environments to enable safe and efficient structural exploration, rapid design iteration, and construction process simulation. The technology significantly reduces design errors while concurrently enhancing construction feasibility and operational safety--particularly in high-risk geological conditions, where traditional design methods show limitations. Real-Time Feedback and Process Adjustment  
Through real-time synchronization of digital twin models with on-site construction data streams during geotechnical operations, this system implements continuous tracking of critical construction metrics, including progress rates, quality compliance indices, and deformation thresholds. The cyber-physical integration enables instantaneous anomaly detection through predictive analytics, allowing for the dynamic adjustment of construction methodologies to mitigate risks associated with non-conforming quality benchmarks or schedule deviations.