https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Sottile%2A_et_al_2024a Sottile* et al 2024a - Revision history 2026-05-11T23:08:52Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Sottile*_et_al_2024a&diff=300909&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 505238612 to Sottile* et al 2024a 2024-06-06T12:58:37Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_505238612" class="mw-redirect" title="Draft Sanchez Pinedo 505238612">Draft Sanchez Pinedo 505238612</a> to <a href="/public/Sottile*_et_al_2024a" title="Sottile* et al 2024a">Sottile* et al 2024a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 12:58, 6 June 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> JSanchez https://www.scipedia.com/wd/index.php?title=Sottile*_et_al_2024a&diff=300908&oldid=prev JSanchez at 12:58, 6 June 2024 2024-06-06T12:58:31Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:58, 6 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Building a ground model through manual processes can be time consuming, as large amounts of data need to be classified to define the extent and spatial distribution of the different soil materials. This paper delves into the application of machine learning (ML) methodologies, in conjunction with in-situ geotechnical testing data, to develop the ground model for a downstream dam founded on both weak and liquefiable soils. The dam covers a linear extent of approximately 800 m and was extensively characterized by means of in-situ tests, including 206 cone penetration tests (CPTu), 37 boreholes and 35 test pits. The performance of two unsupervised ML clustering algorithms are compared: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and an extended version with a hierarchical component (HDBSCAN). The clustering uses CPTu data, which consists of the normalized cone tip resistance (Qtn) and the normalized sleeve friction (Fr) varying with elevation. Nearby borehole logs are used to evaluate the results of both clustering methods for a single single CPTu sounding using different clustering parameters. Then, a global clustering including several CPTu soundings is done and results are compared with the ground model that was manually made using Leapfrog software. Both methods show very good performance, with HDBSCAN being better and more robust.</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Building a ground model through manual processes can be time consuming, as large amounts of data need to be classified to define the extent and spatial distribution of the different soil materials. This paper delves into the application of machine learning (ML) methodologies, in conjunction with in-situ geotechnical testing data, to develop the ground model for a downstream dam founded on both weak and liquefiable soils. The dam covers a linear extent of approximately 800 m and was extensively characterized by means of in-situ tests, including 206 cone penetration tests (CPTu), 37 boreholes and 35 test pits. The performance of two unsupervised ML clustering algorithms are compared: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and an extended version with a hierarchical component (HDBSCAN). The clustering uses CPTu data, which consists of the normalized cone tip resistance (Qtn) and the normalized sleeve friction (Fr) varying with elevation. Nearby borehole logs are used to evaluate the results of both clustering methods for a single single CPTu sounding using different clustering parameters. Then, a global clustering including several CPTu soundings is done and results are compared with the ground model that was manually made using Leapfrog software. Both methods show very good performance, with HDBSCAN being better and more robust.</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_505238612256.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:300906:newid:300908 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Sottile*_et_al_2024a&diff=300906&oldid=prev JSanchez at 12:58, 6 June 2024 2024-06-06T12:58:28Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:58, 6 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Building a ground model through manual processes can be time consuming, as large amounts of data need to be classified to define the extent and spatial distribution of the different soil materials. This paper delves into the application of machine learning (ML) methodologies, in conjunction with in-situ geotechnical testing data, to develop the ground model for a downstream dam founded on both weak and liquefiable soils. The dam covers a linear extent of approximately 800 m and was extensively characterized by means of in-situ tests, including 206 cone penetration tests (CPTu), 37 boreholes and 35 test pits. The performance of two unsupervised ML clustering algorithms are compared: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and an extended version with a hierarchical component (HDBSCAN). The clustering uses CPTu data, which consists of the normalized cone tip resistance (Qtn) and the normalized sleeve friction (Fr) varying with elevation. Nearby borehole logs are used to evaluate the results of both clustering methods for a single single CPTu sounding using different clustering parameters. Then, a global clustering including several CPTu soundings is done and results are compared with the ground model that was manually made using Leapfrog software. Both methods show very good performance, with HDBSCAN being better and more robust.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:300905:newid:300906 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Sottile*_et_al_2024a&diff=300905&oldid=prev JSanchez: Created blank page 2024-06-06T12:58:27Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez