Neural Computing and Applications

, Volume 31, Issue 12, pp 8205–8215 | Cite as

Research on prediction model of geotechnical parameters based on BP neural network

  • Kai CuiEmail author
  • Xiang Jing
Machine Learning - Applications & Techniques in Cyber Intelligence


With the vigorous development of the national economy, the pace and scale of urban construction have been unfolded at an unprecedented speed. A large number of construction projects have made the urban engineering geological exploration activities reach a considerable scale in depth and breadth. The survey results of these projects are very valuable information resources, which not only played an important role in urban planning and construction at that time, but also had high reuse value. Based on BP neural network theory, this paper uses engineering geological database as the research and development platform. Based on the theory of BP neural network and the engineering geological database as the research and development platform, this paper establishes the prediction of geotechnical parameters based on the analysis of the characteristics of geotechnical materials and the distribution of geotechnical sediments and geotechnical parameters. Based on the survey data and specific engineering information, the prediction model of the project was established, and the distribution of the stratum and the relevant geotechnical parameters were predicted. Based on the study of geotechnical properties and BP neural network, a new parameter prediction model is established. Taking the engineering geological database as the platform, using the programming language such as MATLAB, the preliminary research and construction of this prediction system were carried out. The results show that the generalization ability of the prediction model meets the requirements.


BP neural network Geotechnical parameters Prediction 



This work was supported by the National Natural Science Foundation of China (Grant No. 41572245) and the Fundamental Research Funds for the Central Universities (Grant No. 2682018GJPY02).

Compliance with ethical standards

Conflict of interest

There are no conflicts of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of High-speed Railway Engineering of the Ministry of EducationSouthwest Jiaotong UniversityChengduChina
  2. 2.School of Civil EngineeringSouthwest Jiaotong UniversityChengduChina

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