Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection
During the construction of the tunnel, there may be water-bearing anomalous structures such as fault fracture zone. In order to ensure the safety of the tunnel, it is necessary to carry out advanced tunnel detection. The traditional linear inversion method is highly dependent on the initial model in...
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| Veröffentlicht in: | Mathematical problems in engineering 2021, Vol.2021, p.1-7 |
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| creator | Ma, Zhao Nie, Lichao Zhou, Pengfei Deng, Zhaoyang Guo, Lei |
| description | During the construction of the tunnel, there may be water-bearing anomalous structures such as fault fracture zone. In order to ensure the safety of the tunnel, it is necessary to carry out advanced tunnel detection. The traditional linear inversion method is highly dependent on the initial model in the tunnel resistivity inversion, which makes the inversion results falling into the local optimal optimum rather than the global one. Therefore, an inversion method for tunnel resistivity advanced detection based on ant colony algorithm is proposed in this paper. In order to improve the accuracy of tunnel advanced detection of deep anomalous bodies, an ant colony optimization (ACO) inversion is used by integrating depth weighting into the inversion function. At the same time, in view of the high efficiency and low cost of one-dimension inversion and the advantages of L1 norm in boundary characterization, a one-dimensional ant colony algorithm is adopted in this paper. In order to evaluate the performance of the algorithm, two sets of numerical simulations were carried out. Finally, the application of the actual tunnel water-bearing anomalous structure was carried out in a real example to evaluate the application effect, and it was verified by excavation exposure. |
| doi_str_mv | 10.1155/2021/6649454 |
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In order to ensure the safety of the tunnel, it is necessary to carry out advanced tunnel detection. The traditional linear inversion method is highly dependent on the initial model in the tunnel resistivity inversion, which makes the inversion results falling into the local optimal optimum rather than the global one. Therefore, an inversion method for tunnel resistivity advanced detection based on ant colony algorithm is proposed in this paper. In order to improve the accuracy of tunnel advanced detection of deep anomalous bodies, an ant colony optimization (ACO) inversion is used by integrating depth weighting into the inversion function. At the same time, in view of the high efficiency and low cost of one-dimension inversion and the advantages of L1 norm in boundary characterization, a one-dimensional ant colony algorithm is adopted in this paper. In order to evaluate the performance of the algorithm, two sets of numerical simulations were carried out. Finally, the application of the actual tunnel water-bearing anomalous structure was carried out in a real example to evaluate the application effect, and it was verified by excavation exposure.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2021/6649454</identifier><language>eng</language><publisher>LONDON: Hindawi</publisher><subject>Accuracy ; Algorithms ; Ant colony optimization ; Electrical resistivity ; Electrodes ; Engineering ; Engineering, Multidisciplinary ; Food ; Foraging behavior ; Mathematical models ; Mathematics ; Mathematics, Interdisciplinary Applications ; Optimization ; Performance evaluation ; Pheromones ; Physical Sciences ; Science & Technology ; Technology ; Trails ; Tunnel construction</subject><ispartof>Mathematical problems in engineering, 2021, Vol.2021, p.1-7</ispartof><rights>Copyright © 2021 Zhao Ma et al.</rights><rights>Copyright © 2021 Zhao Ma et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>1</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000663675200012</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c376t-ae572e03e83be0cd858d1c1e6f22e9c7187a49db81e8ca2add6ddbe1f56904b3</citedby><cites>FETCH-LOGICAL-c376t-ae572e03e83be0cd858d1c1e6f22e9c7187a49db81e8ca2add6ddbe1f56904b3</cites><orcidid>0000-0003-3177-4052 ; 0000-0001-9468-2329 ; 0000-0003-0609-416X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,4025,27928,27929,27930,39263</link.rule.ids></links><search><contributor>Hanne, Thomas</contributor><contributor>Thomas Hanne</contributor><creatorcontrib>Ma, Zhao</creatorcontrib><creatorcontrib>Nie, Lichao</creatorcontrib><creatorcontrib>Zhou, Pengfei</creatorcontrib><creatorcontrib>Deng, Zhaoyang</creatorcontrib><creatorcontrib>Guo, Lei</creatorcontrib><title>Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection</title><title>Mathematical problems in engineering</title><addtitle>MATH PROBL ENG</addtitle><description>During the construction of the tunnel, there may be water-bearing anomalous structures such as fault fracture zone. In order to ensure the safety of the tunnel, it is necessary to carry out advanced tunnel detection. The traditional linear inversion method is highly dependent on the initial model in the tunnel resistivity inversion, which makes the inversion results falling into the local optimal optimum rather than the global one. Therefore, an inversion method for tunnel resistivity advanced detection based on ant colony algorithm is proposed in this paper. In order to improve the accuracy of tunnel advanced detection of deep anomalous bodies, an ant colony optimization (ACO) inversion is used by integrating depth weighting into the inversion function. At the same time, in view of the high efficiency and low cost of one-dimension inversion and the advantages of L1 norm in boundary characterization, a one-dimensional ant colony algorithm is adopted in this paper. In order to evaluate the performance of the algorithm, two sets of numerical simulations were carried out. Finally, the application of the actual tunnel water-bearing anomalous structure was carried out in a real example to evaluate the application effect, and it was verified by excavation exposure.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Ant colony optimization</subject><subject>Electrical resistivity</subject><subject>Electrodes</subject><subject>Engineering</subject><subject>Engineering, Multidisciplinary</subject><subject>Food</subject><subject>Foraging behavior</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Mathematics, Interdisciplinary Applications</subject><subject>Optimization</subject><subject>Performance evaluation</subject><subject>Pheromones</subject><subject>Physical Sciences</subject><subject>Science & Technology</subject><subject>Technology</subject><subject>Trails</subject><subject>Tunnel construction</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkEtPwzAQhCMEEuVx4wdY4ggBrxM7ybEKT6kCDkXiFjn2Boxau8QuFfx6HFrBDXHaOXyzuzNJcgT0DIDzc0YZnAuRVznPt5IRcJGlHPJiO2rK8hRY9rSb7Hn_SiPJoRwlD2MbSO1mzn6Q-0Uwc_Mpg3GW3Np37P2gHr2xzyS8IJkAuXP9nBhLxvpdWoWaTJfW4oxcYEA1GA-SnU7OPB5u5n4yvbqc1jfp5P76th5PUpUVIqQSecGQZlhmLVKlS15qUICiYwwrVUBZyLzSbQlYKsmk1kLrFqHjoqJ5m-0nx-u1i969LdGH5tUtexsvNiwGowCVYJE6XVOqd9732DWL3sxl_9EAbYbKmqGyZlNZxMs1vsLWdV4ZjBF_LJRSITJRcBYVsNqE76Zqt7QhWk_-b_2lX4zVcmX-fusLg12Lmg</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Ma, Zhao</creator><creator>Nie, Lichao</creator><creator>Zhou, Pengfei</creator><creator>Deng, Zhaoyang</creator><creator>Guo, Lei</creator><general>Hindawi</general><general>Hindawi Publishing Group</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-3177-4052</orcidid><orcidid>https://orcid.org/0000-0001-9468-2329</orcidid><orcidid>https://orcid.org/0000-0003-0609-416X</orcidid></search><sort><creationdate>2021</creationdate><title>Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection</title><author>Ma, Zhao ; 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| subjects | Accuracy Algorithms Ant colony optimization Electrical resistivity Electrodes Engineering Engineering, Multidisciplinary Food Foraging behavior Mathematical models Mathematics Mathematics, Interdisciplinary Applications Optimization Performance evaluation Pheromones Physical Sciences Science & Technology Technology Trails Tunnel construction |
| title | Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection |
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