https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Stuyts%2A_2024a Stuyts* 2024a - Revision history 2026-04-25T05:20:56Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Stuyts*_2024a&diff=302590&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 526194034 to Stuyts* 2024a 2024-06-10T10:03:53Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_526194034" class="mw-redirect" title="Draft Sanchez Pinedo 526194034">Draft Sanchez Pinedo 526194034</a> to <a href="/public/Stuyts*_2024a" title="Stuyts* 2024a">Stuyts* 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 10:03, 10 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=Stuyts*_2024a&diff=302589&oldid=prev JSanchez at 10:03, 10 June 2024 2024-06-10T10:03:49Z <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 10:03, 10 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>During geotechnical and geophysical site characterisation for large infrastructure projects, significant data volumes are being collected which need to be processed and interpreted. Due to the limited budgets available for site characterisation and the various sources of uncertainty, the interpretation relies on a combination of data from various sources (e.g. in-situ and laboratory tests), the use of parameter correlations from the literature and expert judgement. In recent years, modern data science techniques have become increasingly accessible to practicing engineers and researchers and they offer the possibility to improve several aspects of the site characterisation and parameter selection process. Machine learning models can be trained on high-quality datasets and expert judgement can also be internalised in the model formulations. In this contribution, the role of data science and machine learning for geotechnical site characterisation is discussed based on several example applications using datasets from offshore wind farm projects. The role of data coverage and data quality is discussed as well as the role of geophysical data for interpolating geotechnical point measurements in a quantitative way. Supervised and unsupervised machine learning techniques are explained and illustrated on the provided datasets. Finally, a perspective is given on the role of the emerging Large Language Models (LLM) for geotechnical site characterisation applications.</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>During geotechnical and geophysical site characterisation for large infrastructure projects, significant data volumes are being collected which need to be processed and interpreted. Due to the limited budgets available for site characterisation and the various sources of uncertainty, the interpretation relies on a combination of data from various sources (e.g. in-situ and laboratory tests), the use of parameter correlations from the literature and expert judgement. In recent years, modern data science techniques have become increasingly accessible to practicing engineers and researchers and they offer the possibility to improve several aspects of the site characterisation and parameter selection process. Machine learning models can be trained on high-quality datasets and expert judgement can also be internalised in the model formulations. In this contribution, the role of data science and machine learning for geotechnical site characterisation is discussed based on several example applications using datasets from offshore wind farm projects. The role of data coverage and data quality is discussed as well as the role of geophysical data for interpolating geotechnical point measurements in a quantitative way. Supervised and unsupervised machine learning techniques are explained and illustrated on the provided datasets. Finally, a perspective is given on the role of the emerging Large Language Models (LLM) for geotechnical site characterisation applications.</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_526194034308.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:302587:newid:302589 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Stuyts*_2024a&diff=302587&oldid=prev JSanchez at 10:03, 10 June 2024 2024-06-10T10:03:48Z <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 10:03, 10 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;">During geotechnical and geophysical site characterisation for large infrastructure projects, significant data volumes are being collected which need to be processed and interpreted. Due to the limited budgets available for site characterisation and the various sources of uncertainty, the interpretation relies on a combination of data from various sources (e.g. in-situ and laboratory tests), the use of parameter correlations from the literature and expert judgement. In recent years, modern data science techniques have become increasingly accessible to practicing engineers and researchers and they offer the possibility to improve several aspects of the site characterisation and parameter selection process. Machine learning models can be trained on high-quality datasets and expert judgement can also be internalised in the model formulations. In this contribution, the role of data science and machine learning for geotechnical site characterisation is discussed based on several example applications using datasets from offshore wind farm projects. The role of data coverage and data quality is discussed as well as the role of geophysical data for interpolating geotechnical point measurements in a quantitative way. Supervised and unsupervised machine learning techniques are explained and illustrated on the provided datasets. Finally, a perspective is given on the role of the emerging Large Language Models (LLM) for geotechnical site characterisation applications.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:302586:newid:302587 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Stuyts*_2024a&diff=302586&oldid=prev JSanchez: Created blank page 2024-06-10T10:03:46Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez