With the improvement of multisource information sensing and data acquisition capabilities inside tunnels,the availability of multimodal data in tunnel engineering has significantly increased.However,due to structural ...With the improvement of multisource information sensing and data acquisition capabilities inside tunnels,the availability of multimodal data in tunnel engineering has significantly increased.However,due to structural differences in multimodal data,traditional intelligent advanced geological prediction models have limited capacity for data fusion.Furthermore,the lack of pre-trained models makes it difficult for neural networks trained from scratch to deeply explore the features of multimodal data.To address these challenges,we utilize the fusion capability of knowledge graph for multimodal data and the pre-trained knowledge of large language models(LLMs)to establish an intelligent advanced geological prediction model(GeoPredict-LLM).First,we develop an advanced geological prediction ontology model,forming a knowledge graph database.Using knowledge graph embeddings,multisource and multimodal data are transformed into low-dimensional vectors with a unified structure.Secondly,pre-trained LLMs,through reprogramming,reconstruct these low-dimensional vectors,imparting linguistic characteristics to the data.This transformation effectively reframes the complex task of advanced geological prediction as a"language-based"problem,enabling the model to approach the task from a linguistic perspective.Moreover,we propose the prompt-as-prefix method,which enables output generation,while freezing the core of the LLM,thereby significantly reduces the number of training parameters.Finally,evaluations show that compared to neural network models without pre-trained models,GeoPredict-LLM significantly improves prediction accuracy.It is worth noting that as long as a knowledge graph database can be established,GeoPredict-LLM can be adapted to multimodal data mining tasks with minimal modifications.展开更多
Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detectio...Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detection accuracy.Using multiple methods to carry out a comprehensive evaluation can eff ectively improve the accuracy of advanced geological prediction results.In this study,geological information is combined with the detection results of geophysical methods,including transient electromagnetic,induced polarization,and tunnel seismic prediction,to establish a comprehensive analysis method of adverse geology.First,the possible main adverse geological problems are determined according to the geological information.Subsequently,various physical parameters of the rock mass in front of the tunnel face can then be derived on the basis of multisource geophysical data.Finally,based on the analysis results of geological information,the multisource data fusion algorithm is used to determine the type,location,and scale of adverse geology.The advanced geological prediction results that can provide eff ective guidance for tunnel construction can then be obtained.展开更多
Tunnel engineering often has complex terrain conditions, and its construction effect directly affects the construction quality and safety of railway, highway and other traffic engineering. In the construction, the sta...Tunnel engineering often has complex terrain conditions, and its construction effect directly affects the construction quality and safety of railway, highway and other traffic engineering. In the construction, the staff should fully consider the impact of the natural environment, take effective measures to protect and avoid adverse risks. In the tunnel construction, the staff should improve the accuracy of geological prediction, reasonably control the risk factors of the project, ensure the smooth operation, strictly control the quality of the project, and improve the reliability of the project construction.展开更多
基金the National Natural Science Foundation of China(Grant Nos.52279103 and 52379103)。
文摘With the improvement of multisource information sensing and data acquisition capabilities inside tunnels,the availability of multimodal data in tunnel engineering has significantly increased.However,due to structural differences in multimodal data,traditional intelligent advanced geological prediction models have limited capacity for data fusion.Furthermore,the lack of pre-trained models makes it difficult for neural networks trained from scratch to deeply explore the features of multimodal data.To address these challenges,we utilize the fusion capability of knowledge graph for multimodal data and the pre-trained knowledge of large language models(LLMs)to establish an intelligent advanced geological prediction model(GeoPredict-LLM).First,we develop an advanced geological prediction ontology model,forming a knowledge graph database.Using knowledge graph embeddings,multisource and multimodal data are transformed into low-dimensional vectors with a unified structure.Secondly,pre-trained LLMs,through reprogramming,reconstruct these low-dimensional vectors,imparting linguistic characteristics to the data.This transformation effectively reframes the complex task of advanced geological prediction as a"language-based"problem,enabling the model to approach the task from a linguistic perspective.Moreover,we propose the prompt-as-prefix method,which enables output generation,while freezing the core of the LLM,thereby significantly reduces the number of training parameters.Finally,evaluations show that compared to neural network models without pre-trained models,GeoPredict-LLM significantly improves prediction accuracy.It is worth noting that as long as a knowledge graph database can be established,GeoPredict-LLM can be adapted to multimodal data mining tasks with minimal modifications.
基金National Natural Science Foundation of China(grant numbers 42293351,41877239,51422904 and 51379112).
文摘Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detection accuracy.Using multiple methods to carry out a comprehensive evaluation can eff ectively improve the accuracy of advanced geological prediction results.In this study,geological information is combined with the detection results of geophysical methods,including transient electromagnetic,induced polarization,and tunnel seismic prediction,to establish a comprehensive analysis method of adverse geology.First,the possible main adverse geological problems are determined according to the geological information.Subsequently,various physical parameters of the rock mass in front of the tunnel face can then be derived on the basis of multisource geophysical data.Finally,based on the analysis results of geological information,the multisource data fusion algorithm is used to determine the type,location,and scale of adverse geology.The advanced geological prediction results that can provide eff ective guidance for tunnel construction can then be obtained.
文摘Tunnel engineering often has complex terrain conditions, and its construction effect directly affects the construction quality and safety of railway, highway and other traffic engineering. In the construction, the staff should fully consider the impact of the natural environment, take effective measures to protect and avoid adverse risks. In the tunnel construction, the staff should improve the accuracy of geological prediction, reasonably control the risk factors of the project, ensure the smooth operation, strictly control the quality of the project, and improve the reliability of the project construction.
