Loading…

Unified optimization-based analysis of GPR hyperbolic fitting models

•Introduced a novel index to evaluate GPR's hyperbola fitting models quantitatively.•Explored how object radius, depth, antenna separation, and permittivity affect fitting models.•Employed a hybrid optimization approach across five hyperbola fitting models.•Based on accuracy, complexity, and pr...

Full description

Saved in:
Bibliographic Details
Published in:Tunnelling and underground space technology 2024-04, Vol.146, p.105633, Article 105633
Main Authors: He, Wenchao, Wai-Lok Lai, Wallace
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c300t-b7aa05cd4877ad70ba9d6fa0f19902ea9cdd03deffcbba9fc81649d58a2247e93
cites cdi_FETCH-LOGICAL-c300t-b7aa05cd4877ad70ba9d6fa0f19902ea9cdd03deffcbba9fc81649d58a2247e93
container_end_page
container_issue
container_start_page 105633
container_title Tunnelling and underground space technology
container_volume 146
creator He, Wenchao
Wai-Lok Lai, Wallace
description •Introduced a novel index to evaluate GPR's hyperbola fitting models quantitatively.•Explored how object radius, depth, antenna separation, and permittivity affect fitting models.•Employed a hybrid optimization approach across five hyperbola fitting models.•Based on accuracy, complexity, and prior information, fitting models are recommended and validated. Ground Penetrating Radar (GPR) is a valuable tool for exploring underground spaces, particularly for detecting cylindrical objects such as pipelines and rebar, where data often forms hyperbolic pattern. The technique of hyperbola fitting is a commonly used approach for extracting information from these data. However, the existing literature has not thoroughly examined how different parameters - such as antenna separation, target radius, burial depth, and the relative permittivity of the host media - influence the performance of various hyperbola fitting, or strictly speaking, non-hyperbolic ray-path models. This study presents an extensive comparative analysis of 2 hyperbolic and 3 non-hyperbolic fitting models by formulating them as a common optimization problem mathematically. A novel cost function (C-value) is introduced to quantitatively evaluate the five models. The results demonstrate that various parameters have distinct influences on the performance of the models to the reality. Recommendations for model selection are provided, taking into account the availability of prior information and efficacy in matching the models. The findings and recommendations offer practical insights that are poised to improve the precision and reliability of hyperbola and non-hyperbolic fitting in various GPR studies.
doi_str_mv 10.1016/j.tust.2024.105633
format article
fullrecord <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_tust_2024_105633</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0886779824000518</els_id><sourcerecordid>S0886779824000518</sourcerecordid><originalsourceid>FETCH-LOGICAL-c300t-b7aa05cd4877ad70ba9d6fa0f19902ea9cdd03deffcbba9fc81649d58a2247e93</originalsourceid><addsrcrecordid>eNp9kM1KAzEUhYMoWKsv4GpeYOpN5icJuJGqVSgoYtchkx-9ZTopSRTq0ztlXLu6cC7f4fARck1hQYG2N9tF_kp5wYDVY9C0VXVCZlRwUdZVW5-SGQjRlpxLcU4uUtoCQMOYnJH7zYAenS3CPuMOf3TGMJSdTmOkB90fEqYi-GL1-lZ8HvYudqFHU3jMGYePYhes69MlOfO6T-7q787J5vHhfflUrl9Wz8u7dWkqgFx2XGtojK0F59py6LS0rdfgqZTAnJbGWqis8950488bQdta2kZoxmruZDUnbOo1MaQUnVf7iDsdD4qCOnpQW3X0oI4e1ORhhG4naBzqvtFFlQy6wTiL0ZmsbMD_8F-8zGi_</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Unified optimization-based analysis of GPR hyperbolic fitting models</title><source>Elsevier</source><creator>He, Wenchao ; Wai-Lok Lai, Wallace</creator><creatorcontrib>He, Wenchao ; Wai-Lok Lai, Wallace</creatorcontrib><description>•Introduced a novel index to evaluate GPR's hyperbola fitting models quantitatively.•Explored how object radius, depth, antenna separation, and permittivity affect fitting models.•Employed a hybrid optimization approach across five hyperbola fitting models.•Based on accuracy, complexity, and prior information, fitting models are recommended and validated. Ground Penetrating Radar (GPR) is a valuable tool for exploring underground spaces, particularly for detecting cylindrical objects such as pipelines and rebar, where data often forms hyperbolic pattern. The technique of hyperbola fitting is a commonly used approach for extracting information from these data. However, the existing literature has not thoroughly examined how different parameters - such as antenna separation, target radius, burial depth, and the relative permittivity of the host media - influence the performance of various hyperbola fitting, or strictly speaking, non-hyperbolic ray-path models. This study presents an extensive comparative analysis of 2 hyperbolic and 3 non-hyperbolic fitting models by formulating them as a common optimization problem mathematically. A novel cost function (C-value) is introduced to quantitatively evaluate the five models. The results demonstrate that various parameters have distinct influences on the performance of the models to the reality. Recommendations for model selection are provided, taking into account the availability of prior information and efficacy in matching the models. The findings and recommendations offer practical insights that are poised to improve the precision and reliability of hyperbola and non-hyperbolic fitting in various GPR studies.</description><identifier>ISSN: 0886-7798</identifier><identifier>EISSN: 1878-4364</identifier><identifier>DOI: 10.1016/j.tust.2024.105633</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>C-value ; GPR ; Hyperbola fitting ; PSO</subject><ispartof>Tunnelling and underground space technology, 2024-04, Vol.146, p.105633, Article 105633</ispartof><rights>2024 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c300t-b7aa05cd4877ad70ba9d6fa0f19902ea9cdd03deffcbba9fc81649d58a2247e93</citedby><cites>FETCH-LOGICAL-c300t-b7aa05cd4877ad70ba9d6fa0f19902ea9cdd03deffcbba9fc81649d58a2247e93</cites><orcidid>0000-0002-2096-0776</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>He, Wenchao</creatorcontrib><creatorcontrib>Wai-Lok Lai, Wallace</creatorcontrib><title>Unified optimization-based analysis of GPR hyperbolic fitting models</title><title>Tunnelling and underground space technology</title><description>•Introduced a novel index to evaluate GPR's hyperbola fitting models quantitatively.•Explored how object radius, depth, antenna separation, and permittivity affect fitting models.•Employed a hybrid optimization approach across five hyperbola fitting models.•Based on accuracy, complexity, and prior information, fitting models are recommended and validated. Ground Penetrating Radar (GPR) is a valuable tool for exploring underground spaces, particularly for detecting cylindrical objects such as pipelines and rebar, where data often forms hyperbolic pattern. The technique of hyperbola fitting is a commonly used approach for extracting information from these data. However, the existing literature has not thoroughly examined how different parameters - such as antenna separation, target radius, burial depth, and the relative permittivity of the host media - influence the performance of various hyperbola fitting, or strictly speaking, non-hyperbolic ray-path models. This study presents an extensive comparative analysis of 2 hyperbolic and 3 non-hyperbolic fitting models by formulating them as a common optimization problem mathematically. A novel cost function (C-value) is introduced to quantitatively evaluate the five models. The results demonstrate that various parameters have distinct influences on the performance of the models to the reality. Recommendations for model selection are provided, taking into account the availability of prior information and efficacy in matching the models. The findings and recommendations offer practical insights that are poised to improve the precision and reliability of hyperbola and non-hyperbolic fitting in various GPR studies.</description><subject>C-value</subject><subject>GPR</subject><subject>Hyperbola fitting</subject><subject>PSO</subject><issn>0886-7798</issn><issn>1878-4364</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kM1KAzEUhYMoWKsv4GpeYOpN5icJuJGqVSgoYtchkx-9ZTopSRTq0ztlXLu6cC7f4fARck1hQYG2N9tF_kp5wYDVY9C0VXVCZlRwUdZVW5-SGQjRlpxLcU4uUtoCQMOYnJH7zYAenS3CPuMOf3TGMJSdTmOkB90fEqYi-GL1-lZ8HvYudqFHU3jMGYePYhes69MlOfO6T-7q787J5vHhfflUrl9Wz8u7dWkqgFx2XGtojK0F59py6LS0rdfgqZTAnJbGWqis8950488bQdta2kZoxmruZDUnbOo1MaQUnVf7iDsdD4qCOnpQW3X0oI4e1ORhhG4naBzqvtFFlQy6wTiL0ZmsbMD_8F-8zGi_</recordid><startdate>202404</startdate><enddate>202404</enddate><creator>He, Wenchao</creator><creator>Wai-Lok Lai, Wallace</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-2096-0776</orcidid></search><sort><creationdate>202404</creationdate><title>Unified optimization-based analysis of GPR hyperbolic fitting models</title><author>He, Wenchao ; Wai-Lok Lai, Wallace</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c300t-b7aa05cd4877ad70ba9d6fa0f19902ea9cdd03deffcbba9fc81649d58a2247e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>C-value</topic><topic>GPR</topic><topic>Hyperbola fitting</topic><topic>PSO</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Wenchao</creatorcontrib><creatorcontrib>Wai-Lok Lai, Wallace</creatorcontrib><collection>CrossRef</collection><jtitle>Tunnelling and underground space technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>He, Wenchao</au><au>Wai-Lok Lai, Wallace</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unified optimization-based analysis of GPR hyperbolic fitting models</atitle><jtitle>Tunnelling and underground space technology</jtitle><date>2024-04</date><risdate>2024</risdate><volume>146</volume><spage>105633</spage><pages>105633-</pages><artnum>105633</artnum><issn>0886-7798</issn><eissn>1878-4364</eissn><abstract>•Introduced a novel index to evaluate GPR's hyperbola fitting models quantitatively.•Explored how object radius, depth, antenna separation, and permittivity affect fitting models.•Employed a hybrid optimization approach across five hyperbola fitting models.•Based on accuracy, complexity, and prior information, fitting models are recommended and validated. Ground Penetrating Radar (GPR) is a valuable tool for exploring underground spaces, particularly for detecting cylindrical objects such as pipelines and rebar, where data often forms hyperbolic pattern. The technique of hyperbola fitting is a commonly used approach for extracting information from these data. However, the existing literature has not thoroughly examined how different parameters - such as antenna separation, target radius, burial depth, and the relative permittivity of the host media - influence the performance of various hyperbola fitting, or strictly speaking, non-hyperbolic ray-path models. This study presents an extensive comparative analysis of 2 hyperbolic and 3 non-hyperbolic fitting models by formulating them as a common optimization problem mathematically. A novel cost function (C-value) is introduced to quantitatively evaluate the five models. The results demonstrate that various parameters have distinct influences on the performance of the models to the reality. Recommendations for model selection are provided, taking into account the availability of prior information and efficacy in matching the models. The findings and recommendations offer practical insights that are poised to improve the precision and reliability of hyperbola and non-hyperbolic fitting in various GPR studies.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.tust.2024.105633</doi><orcidid>https://orcid.org/0000-0002-2096-0776</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0886-7798
ispartof Tunnelling and underground space technology, 2024-04, Vol.146, p.105633, Article 105633
issn 0886-7798
1878-4364
language eng
recordid cdi_crossref_primary_10_1016_j_tust_2024_105633
source Elsevier
subjects C-value
GPR
Hyperbola fitting
PSO
title Unified optimization-based analysis of GPR hyperbolic fitting models
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T07%3A49%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Unified%20optimization-based%20analysis%20of%20GPR%20hyperbolic%20fitting%20models&rft.jtitle=Tunnelling%20and%20underground%20space%20technology&rft.au=He,%20Wenchao&rft.date=2024-04&rft.volume=146&rft.spage=105633&rft.pages=105633-&rft.artnum=105633&rft.issn=0886-7798&rft.eissn=1878-4364&rft_id=info:doi/10.1016/j.tust.2024.105633&rft_dat=%3Celsevier_cross%3ES0886779824000518%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c300t-b7aa05cd4877ad70ba9d6fa0f19902ea9cdd03deffcbba9fc81649d58a2247e93%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true