Check patentability & draft patents in minutes with Patsnap Eureka AI!

Method for selecting seismic oscillation based on principal component analysis and multi-objective genetic algorithm

A multi-objective genetic and principal component analysis technology, applied in the field of ground motion selection based on principal component analysis and multi-objective genetic algorithm, can solve problems such as ground motion uncertainty, and achieve the effect of good inclusiveness and good engineering application prospects.

Active Publication Date: 2021-04-16
HARBIN INST OF TECH
View PDF4 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method for selecting ground motions based on principal component analysis and multi-objective genetic algorithm in order to solve the problem of uncertainty of ground motions obtained based on amplitude modulation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for selecting seismic oscillation based on principal component analysis and multi-objective genetic algorithm
  • Method for selecting seismic oscillation based on principal component analysis and multi-objective genetic algorithm
  • Method for selecting seismic oscillation based on principal component analysis and multi-objective genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0046]DETAILED DESCRIPTION OF THE INVENTION The present embodiment is implemented in accordance with the following steps based on the main component analysis and multi-target genetic algorithm.

[0047]First, determine seismic information;

[0048]Second, the primary component algorithm (PCA) in the application machine learning extracts the data in the database, extracts the orthogonal group of segings;

[0049]Third, the seismic information determined in step one is input to the geophysical prediction model to obtain a tabular spectrum of the target site;

[0050]4. When the seismic information determined in step one is input to the prediction model of the seismic movement, the seismic manner of the target site is obtained;

[0051]V. Taste spectrum spectrum to three steps is divided into three sections, ie (1) specific structural autogency cycle (T*); (2) 0.2t of condition average spectrum*To 2T*In the range; (3) other cycles of the conditional aperation spectrum;

[0052]δ1= S*(T*) -S (t*)

[0053]δ2...

specific Embodiment approach 2

[0065]DETAILED DESCRIPTION OF THE INVENTION 2: This embodiment differs from the specific embodiments that seismic information described in step one includes a source, path, and field effect information.

specific Embodiment approach 3

[0066]BEST MODE FOR CARRYING OUT THE INVENTION The third embodiment is different from the specific embodiment two, and the sources of sources include the magnitude, tomographic position, and type.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for selecting seismic oscillation based on principal component analysis and a multi-objective genetic algorithm, and relates to a method for selecting seismic oscillation by applying machine learning. The method for selecting the seismic oscillation comprises the following steps: 1, determining seismic information; 2, extracting seismic oscillation data in the database by applying a principal component algorithm in machine learning; 3, inputting the seismic information into a seismic oscillation prediction model to obtain a conditional mean value spectrum of the target site; 4, inputting the seismic information into a prediction model of seismic duration; 5, dividing the conditional mean value spectrum into three sections according to the natural vibration period of the structure; 6, determining a seismic oscillation duration error; and 7, determining a group of combination coefficients through the multi-objective genetic algorithm by taking the step 5 and the step 6 as constraint conditions to minimize the error, namely, the selected seismic oscillation. According to the method, principal components and a multi-target genetic algorithm in machine learning are introduced into seismic oscillation selection, and the uncertainty of seismic oscillation obtained based on amplitude modulation is solved.

Description

Technical field[0001]The present invention relates to a method of applying a machine learning to select a selection.Background technique[0002]Providing reasonable ground dynamic input for building structures in the field of seismic engineering is always a widespread concern. With the development of urbanization, the city is getting bigger and bigger, and gradually expanding to earthquakes in the world, huge and complex urban infrastructure adds potential seismic risks. Therefore, the probability seismic risk analysis (PSHA) and evaluation of the building is an increasingly important issue and a key issue in seismic engineering research. Provide reasonable, accurate earthquake protection input for disaster probability, and accurate seismic input is premise, however, many factors can affect ground movement, such as uncertainty of sources (fault location, type, size and activity) , Path (slip rate, stress reduction direction) and site effects (amplified, filtering, and nonlinear soil r...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/28G01V1/30
Inventor 籍多发翟长海张辉温卫平
Owner HARBIN INST OF TECH
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com