Evaluation method of slope instability space-time probability based on machine learning
The invention discloses a side slope instability space-time probability evaluation method based on machine learning, which has better prediction precision and generalization ability, and comprises the following steps: Step 100, based on a Bootstrap algorithm, a GRU algorithm and a Kriging algorithm,...
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| creator | HAO ZHERUI WANG WUBIN XIAO XIANPU DENG ZHIXING XIE KANG LI JIASHEN |
| description | The invention discloses a side slope instability space-time probability evaluation method based on machine learning, which has better prediction precision and generalization ability, and comprises the following steps: Step 100, based on a Bootstrap algorithm, a GRU algorithm and a Kriging algorithm, establishing a BGK side slope displacement space-time uncertainty prediction model, and outputting a side slope displacement space-time uncertainty prediction result; step 200, excavating a slope displacement space-time uncertainty prediction result based on a reliability theory, establishing a slope instability space-time probability evaluation model, and outputting a slope instability space-time probability interval; and Step 300, constructing a slope full-section displacement-instability probability binary coupling analysis index DP based on the most disadvantageous principle, and judging the overall safety of the slope according to the value of the DP.
本发明公开了一种具有较好的预测精度和泛化能力的基于机器学习的边坡失稳时空概率的评估方法，包括以下步骤：Step100 |
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本发明公开了一种具有较好的预测精度和泛化能力的基于机器学习的边坡失稳时空概率的评估方法，包括以下步骤：Step100</description><language>chi ; eng</language><subject>ALARM SYSTEMS ; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVEREDIN A SINGLE OTHER SUBCLASS ; CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; MEASURING ; MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE ; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR ; ORDER TELEGRAPHS ; PHYSICS ; SIGNALLING ; SIGNALLING OR CALLING SYSTEMS ; TARIFF METERING APPARATUS ; TESTING</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240705&DB=EPODOC&CC=CN&NR=118296369A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76419</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240705&DB=EPODOC&CC=CN&NR=118296369A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HAO ZHERUI</creatorcontrib><creatorcontrib>WANG WUBIN</creatorcontrib><creatorcontrib>XIAO XIANPU</creatorcontrib><creatorcontrib>DENG ZHIXING</creatorcontrib><creatorcontrib>XIE KANG</creatorcontrib><creatorcontrib>LI JIASHEN</creatorcontrib><title>Evaluation method of slope instability space-time probability based on machine learning</title><description>The invention discloses a side slope instability space-time probability evaluation method based on machine learning, which has better prediction precision and generalization ability, and comprises the following steps: Step 100, based on a Bootstrap algorithm, a GRU algorithm and a Kriging algorithm, establishing a BGK side slope displacement space-time uncertainty prediction model, and outputting a side slope displacement space-time uncertainty prediction result; step 200, excavating a slope displacement space-time uncertainty prediction result based on a reliability theory, establishing a slope instability space-time probability evaluation model, and outputting a slope instability space-time probability interval; and Step 300, constructing a slope full-section displacement-instability probability binary coupling analysis index DP based on the most disadvantageous principle, and judging the overall safety of the slope according to the value of the DP.
本发明公开了一种具有较好的预测精度和泛化能力的基于机器学习的边坡失稳时空概率的评估方法，包括以下步骤：Step100</description><subject>ALARM SYSTEMS</subject><subject>ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVEREDIN A SINGLE OTHER SUBCLASS</subject><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>MEASURING</subject><subject>MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE</subject><subject>MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR</subject><subject>ORDER TELEGRAPHS</subject><subject>PHYSICS</subject><subject>SIGNALLING</subject><subject>SIGNALLING OR CALLING SYSTEMS</subject><subject>TARIFF METERING APPARATUS</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZAh3LUvMKU0syczPU8hNLcnIT1HIT1MozskvSFXIzCsuSUzKzMksqVQoLkhMTtUtycxNVSgoyk-CCSclFqcCdQD1JiZnZOalKuSkJhblZeal8zCwpiXmFKfyQmluBkU31xBnD93Ugvz4VLBpeakl8c5-hoYWRpZmxmaWjsbEqAEAlQA6Xw</recordid><startdate>20240705</startdate><enddate>20240705</enddate><creator>HAO ZHERUI</creator><creator>WANG WUBIN</creator><creator>XIAO XIANPU</creator><creator>DENG ZHIXING</creator><creator>XIE KANG</creator><creator>LI JIASHEN</creator><scope>EVB</scope></search><sort><creationdate>20240705</creationdate><title>Evaluation method of slope instability space-time probability based on machine learning</title><author>HAO ZHERUI ; WANG WUBIN ; XIAO XIANPU ; DENG ZHIXING ; XIE KANG ; LI JIASHEN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118296369A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>ALARM SYSTEMS</topic><topic>ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVEREDIN A SINGLE OTHER SUBCLASS</topic><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>MEASURING</topic><topic>MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE</topic><topic>MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR</topic><topic>ORDER TELEGRAPHS</topic><topic>PHYSICS</topic><topic>SIGNALLING</topic><topic>SIGNALLING OR CALLING SYSTEMS</topic><topic>TARIFF METERING APPARATUS</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>HAO ZHERUI</creatorcontrib><creatorcontrib>WANG WUBIN</creatorcontrib><creatorcontrib>XIAO XIANPU</creatorcontrib><creatorcontrib>DENG ZHIXING</creatorcontrib><creatorcontrib>XIE KANG</creatorcontrib><creatorcontrib>LI JIASHEN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HAO ZHERUI</au><au>WANG WUBIN</au><au>XIAO XIANPU</au><au>DENG ZHIXING</au><au>XIE KANG</au><au>LI JIASHEN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Evaluation method of slope instability space-time probability based on machine learning</title><date>2024-07-05</date><risdate>2024</risdate><abstract>The invention discloses a side slope instability space-time probability evaluation method based on machine learning, which has better prediction precision and generalization ability, and comprises the following steps: Step 100, based on a Bootstrap algorithm, a GRU algorithm and a Kriging algorithm, establishing a BGK side slope displacement space-time uncertainty prediction model, and outputting a side slope displacement space-time uncertainty prediction result; step 200, excavating a slope displacement space-time uncertainty prediction result based on a reliability theory, establishing a slope instability space-time probability evaluation model, and outputting a slope instability space-time probability interval; and Step 300, constructing a slope full-section displacement-instability probability binary coupling analysis index DP based on the most disadvantageous principle, and judging the overall safety of the slope according to the value of the DP.
本发明公开了一种具有较好的预测精度和泛化能力的基于机器学习的边坡失稳时空概率的评估方法，包括以下步骤：Step100</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | ALARM SYSTEMS ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVEREDIN A SINGLE OTHER SUBCLASS CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MEASURING MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR ORDER TELEGRAPHS PHYSICS SIGNALLING SIGNALLING OR CALLING SYSTEMS TARIFF METERING APPARATUS TESTING |
| title | Evaluation method of slope instability space-time probability based on machine learning |
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