公开(公告)号：US20240044740A1

公开(公告)日：2024-02-08

申请号：US18547754

申请日：2022-11-25

Applicant: Northeastern University

Inventor： Benguo HE ,  Xiating FENG ,  Jie WANG ,  Shichen QIU ,  Xiangrui MENG ,  Lei WANG

IPC: G01M7/08 ,  G01N3/313 ,  G01N33/24

CPC classification number: G01M7/08 ,  G01N3/313 ,  G01N33/24

Abstract: The invention provides a method and system for a blast-induced vibration monitoring of a tunnel in high asymmetric in-situ stresses. According to the method, triaxial vibration sensors are respectively fixed in areas having different radial depths inside surrounding rocks of a stress concentration area behind a tunnel face of the tunnel in high asymmetric in-situ stresses, and each triaxial vibration sensor monitors blast vibration velocity and acceleration at a position thereof. The system comprises a plurality of triaxial vibration sensors which are fixed in areas having different radial depths inside surrounding rocks of a stress concentration area behind a tunnel face of the tunnel in high asymmetric in-situ stresses, and each triaxial vibration sensor is used for monitoring blast vibration velocity and acceleration at a position thereof. The method and system can improve the safety and the efficiency of tunnel excavation construction.

公开(公告)号：US20230341354A1

公开(公告)日：2023-10-26

申请号：US18028010

申请日：2020-11-06

Applicant: Northeastern University

Inventor： Huaguang ZHANG ,  Jinhai LIU ,  Lei WANG ,  Jiayue SUN ,  Jian FENG ,  Gang WANG ,  Dazhong MA ,  Senxiang LU

IPC: G01N27/83 ,  G01B7/02

CPC classification number: G01N27/83 ,  G01B7/02

Abstract: Provided is an intelligent inversion method for pipeline defects based on heterogeneous field signals. The method includes the following steps: firstly, acquiring heterogeneous field signals, performing an abnormality judgement, then correcting base values of the heterogeneous field signals, and performing denoising treatment; padding the denoised heterogeneous field signals corresponding to the pipeline defects, unifying the heterogeneous field signals of different sizes into the heterogeneous field signals of same sizes, and performing a nonlinear transformation on signal amplitudes; designing a sparse autoencoder with an axisymmetric structure, and obtaining primary characteristics of the heterogeneous field signals; classifying the pipeline defects according to lengths, widths and depths to obtain category labels of the pipeline defects; designing a multi-classification neural network to classify the heterogeneous field signals, and extracting deep characteristics containing defect size information; and constructing a random forest regression model to realize intelligent inversion for sizes of the pipeline defects.