# Digital Twins' Application for Geotechnical Engineering:
## 3 Challenges in Geotechnical Digital Twin Development  
Digital twin technology, as an inevitable outcome of advanced immortalization, is emerging as a revolutionary tool for deconstructing, characterizing, and understanding the physical world. Scholars worldwide have conducted extensive research on geotechnical DT systems, including DT frameworks [18, 48, 49, 50], 3D geological modeling [51, 52, 53], BIM [54, 55, 56], simulation and geotechnical numerical analysis [57, 58], geotechnical monitoring and sensing [59, 60, 61], etc. For instance, [PERSON] et al. [48] developed a BIM-based data integration and sharing mechanism for geotechnical DT systems. Their work established an integrated geological-structural model and created a simulation analysis module, forming a preliminary geotechnical DT framework. The implementation of geotechnical digital twins relies on both commercial and open-source platforms. Industry solutions like ANSYS Twin Builder (for system simulation), Dassault 3 DEXPERIENCE (BIM integration), and Siemens Simcenter (IoT data fusion) provide robust toolchains, while emerging open frameworks like OpenTWIN offer modular architectures for customized deployments. Notably, IFC-Geo standard extensions (e.g., IFC-Soil and IFC-Tunnel) are bridging BIM gaps in geotechnical data interoperability. However, there remain significant challenges to be addressed in the practical implementation of digital twins in geotechnical engineering. Current research exhibits notable deficiencies in several critical areas, including intelligent perception of physical entities, integration of multi-source heterogeneous data, construction of multidimensional information models, integrated application of geological body models and structural body models, standardization of geotechnical digital twins, and whole-lifecycle management. These limitations are primarily manifested in the following aspects:  
1. Intelligent perception and interconnectivity of multi-source heterogeneous physical entities in geotechnical engineering remain weak:  
Currently, the sensing of various geotechnical parameters operates independently and in isolation, making comprehensive processing highly challenging due to the absence of integrated intelligent sensing devices. Geotechnical engineering involves massive multi-dimensional heterogeneous data that vary in format across multi-scale, multi-temporal, and multi-scenario dimensions, posing significant challenges for data fusion. There exists a critical lack of unified interface support and highly standardized, modularized theories/methods for comprehensive data-model integration. 2. Integrated sharing of 3D geological and structural models faces substantial obstacles and reliability problems:  
Digital twins in geotechnical engineering comprise geological and structural models, which exhibit fundamental disparities in organizational frameworks. The absence of a unified spatial data model and data integration standards results in poor compatibility and consistency. Difficulties in data integration and information sharing between 3D geological models and structural models directly compromise geotechnical engineering analysis and decision-making. There are also scalability issues when combining high-fidelity geological models with structural simulations (e.g., soil-structure interface grid complexity). Secondly, the existing constitutive models (such as the Mohr-Coulomb model and Hoek-Brown model) cannot capture the time-dependent behavior of soft soil creep. Finally, the digital twin component based on AI/ML has the characteristics of a \"black box\" and lacks interpretability in key scenarios such as landslide triggering. There is also an unresolved propagation problem of model uncertainty in DT-based predictions, such as the effect of soil heterogeneity on slope stability simulations. These will seriously affect the reliability of digital twins.