# Digital Twins' Application for Geotechnical Engineering:
## 4 Future Research Perspectives for Geotechnical Digital Twins  
Advancing Intelligent Perception and Interconnectivity Technologies in Geotechnical Engineering  
Continuous advancement in sensing and data acquisition technologies remains the cornerstone for driving the evolution of digital twins. Future development trajectories should prioritize sensor miniaturization to enable embedded deployment in intelligent systems, thereby achieving deeper data acquisition capabilities. Concurrently, advancing multi-sensor fusion technologies to integrate diverse sensing functionalities into unified modules will enhance the timeliness and accuracy of geotechnical decision-making. Adopting reverse digital thread technologies, such as Administration Shell (AAS) frameworks, can facilitate standardized semantic recognition, definition, and validation of existing data/models by leveraging multi-domain engineering integration standards. The development of unified interfaces for data-information interoperability will promote cross-domain compatibility and seamless interaction among multi-source heterogeneous models, enabling holistic information exchange across systems. Developing Integrated Geotechnical Digital Twin Technologies for 3D Geological-Structural Systems  
Given the inherent disparities in modeling methodologies between 3D geological bodies and engineered structural systems, the precise construction of geotechnical digital twins necessitates advanced \"modular assembly\" techniques. The application of cross-disciplinary, cross-domain, and cross-scale model convergence technologies is imperative. While significant progress has been made in meso-scale and macro-mechanistic modeling for geotechnical systems, effective integration of multi-scale models remains elusive. Breakthroughs in macro/meso cross-scale model fusion would substantially elevate digital twins' capabilities, advancing fundamental geotechnical mechanism studies. Furthermore, such integration enables collaborative simulation frameworks for design-construction synergy, supporting dynamic model reconfiguration, process emulation, and predictive evaluation. These capabilities allow for the anticipatory analysis of feedback mechanisms and evolutionary trends between geological and structural systems, thereby guiding construction practices and achieving integrated lifecycle management spanning the planning, design, construction, and operation phases. Advancing Geotechnical Knowledge Graphs and Model-Based Systems Engineering (MBSE) Technologies  
Leveraging the distinctive characteristics of geotechnical engineering, Model-Driven Systems Engineering (MDSE) should be adopted as a paradigm. At the initial stages of user requirement analysis, the Unified Modeling Language (UML) should be systematically employed to precisely define data specifications, model standards, and topological relationships among model objects. This approach facilitates semantic data representation and architectural framework development for geotechnical digital twins, laying a robust foundation for comprehensive data-model integration throughout the lifecycle. Accelerating integration with industrial Internet platforms is crucial to establish a \"Geotechnical Internet Platform + MBSE\" technical ecosystem. Migrating MBSE tools to unified platforms serves dual purposes: (1) standardizing syntax/semantics across heterogeneous models through MBSE tools, and (2) enabling seamless integration with IoT data collected by platforms. This synergy maximizes the application value of data-model integration, empowering geotechnical digital twins with advanced capabilities, including data management, model representation, simulation, scenario projection, intelligent prediction, and autonomous decision-making. Enhancing the Precision, Temporal Application, and Spatial Expansion of Geotechnical Digital Twins  
Digital twins' precision can be categorized into four hierarchical levels: basic descriptive, general diagnostic, intelligent decision-making, and autonomous control.