http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=Li_Ouyang%2A_2024a Li Ouyang* 2024a - Revision history 2026-05-11T07:38:19Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 http://www.colloquiam.com/wd/index.php?title=Li_Ouyang*_2024a&diff=301137&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 233893946 to Li Ouyang* 2024a 2024-06-06T13:38:27Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_233893946" class="mw-redirect" title="Draft Sanchez Pinedo 233893946">Draft Sanchez Pinedo 233893946</a> to <a href="/public/Li_Ouyang*_2024a" title="Li Ouyang* 2024a">Li Ouyang* 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 13:38, 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 http://www.colloquiam.com/wd/index.php?title=Li_Ouyang*_2024a&diff=301136&oldid=prev JSanchez at 13:38, 6 June 2024 2024-06-06T13:38:22Z <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 13:38, 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>Obtaining soil parameters through laboratory tests and solving the governing equations that describe soil settlement can be time-consuming, making immediate on-site predictions of soil settlement challenging. In-situ testing provides a more efficient approach to obtain soil parameters than laboratory tests. Data from the Piezocone penetration test (CPTu) can be used for on-the-spot interpretation of soil mechanical parameters, which can then be incorporated into the governing equations for soil settlement calculation. Physics Informed Neural Networks (PINNs) algorithm uses automatic differentiation method to directly embed partial differential equations (PDEs) into a deep learning neural network and provides solution for these PDEs in a cost-effective manner compared to traditional numerical methods. In this paper, a framework integrating data from CPTu and PINNs to predict soil settlement is proposed and evaluated through comparison with numerical simulations from Finite Element Methods (FEMs). Results show that the framework gave a reasonably good agreement with the FEMs benchmark while substantially reduced the computation time. This method allows for immediate on-site prediction of soil settlement during site investigations, thus better guiding surveying and construction activities.</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>Obtaining soil parameters through laboratory tests and solving the governing equations that describe soil settlement can be time-consuming, making immediate on-site predictions of soil settlement challenging. In-situ testing provides a more efficient approach to obtain soil parameters than laboratory tests. Data from the Piezocone penetration test (CPTu) can be used for on-the-spot interpretation of soil mechanical parameters, which can then be incorporated into the governing equations for soil settlement calculation. Physics Informed Neural Networks (PINNs) algorithm uses automatic differentiation method to directly embed partial differential equations (PDEs) into a deep learning neural network and provides solution for these PDEs in a cost-effective manner compared to traditional numerical methods. In this paper, a framework integrating data from CPTu and PINNs to predict soil settlement is proposed and evaluated through comparison with numerical simulations from Finite Element Methods (FEMs). Results show that the framework gave a reasonably good agreement with the FEMs benchmark while substantially reduced the computation time. This method allows for immediate on-site prediction of soil settlement during site investigations, thus better guiding surveying and construction activities.</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_23389394646.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:301134:newid:301136 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=Li_Ouyang*_2024a&diff=301134&oldid=prev JSanchez at 13:38, 6 June 2024 2024-06-06T13:38:20Z <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 13:38, 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;">Obtaining soil parameters through laboratory tests and solving the governing equations that describe soil settlement can be time-consuming, making immediate on-site predictions of soil settlement challenging. In-situ testing provides a more efficient approach to obtain soil parameters than laboratory tests. Data from the Piezocone penetration test (CPTu) can be used for on-the-spot interpretation of soil mechanical parameters, which can then be incorporated into the governing equations for soil settlement calculation. Physics Informed Neural Networks (PINNs) algorithm uses automatic differentiation method to directly embed partial differential equations (PDEs) into a deep learning neural network and provides solution for these PDEs in a cost-effective manner compared to traditional numerical methods. In this paper, a framework integrating data from CPTu and PINNs to predict soil settlement is proposed and evaluated through comparison with numerical simulations from Finite Element Methods (FEMs). Results show that the framework gave a reasonably good agreement with the FEMs benchmark while substantially reduced the computation time. This method allows for immediate on-site prediction of soil settlement during site investigations, thus better guiding surveying and construction activities.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:301133:newid:301134 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=Li_Ouyang*_2024a&diff=301133&oldid=prev JSanchez: Created blank page 2024-06-06T13:38:18Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez