{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113975"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113975","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enabling technologies for pervasive sensing and deep learning in geotechnical engineering","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","abstract_has_math":false,"creators":["Baltaji, Omar"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Hashash, Youssef","Singer, Andrew","Olson, Scott","Ghaboussi, Jamshid"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:45:56Z","date_published":"2022-04-29T21:45:56Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Environmental engineering"],"languages":["en","eng"],"rights":["Copyright 2021 Omar Baltaji"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113975","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hashash, Youssef","Singer, Andrew","Olson, Scott","Ghaboussi, Jamshid"]},{"key":"dc:creator","label":"Author","values":["Baltaji, Omar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:45:56Z","2024-04-29T21:47:53Z","2021-12","2021-12-01"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Environmental engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Omar Baltaji"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113975"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","The student, Omar Baltaji, accepted the attached license on 2021-11-24 at 07:25.","The student, Omar Baltaji, submitted this Dissertation for approval on 2021-11-24 at 07:48.","This Dissertation was approved for publication on 2021-12-01 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17261 on 2022-04-06 at 17:16:57","Made available in DSpace on 2022-04-29T21:45:56Z (GMT). No. of bitstreams: 2 BALTAJI-DISSERTATION-2021.pdf: 20347842 bytes, checksum: 799c452b2739c8e0997819792703708a (MD5) LICENSE.txt: 4209 bytes, checksum: 5c4b373dd96f5d21a9c53cb491fe65e5 (MD5) Previous issue date: 2021-12-01","Embargo set by: Seth Robbins for item 123339 Lift date: 2024-04-29T21:46:25Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 123339 Lift date: 2024-04-29T21:47:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","The observational method has been historically employed to adapt to the numerous uncertainties encountered during geotechnical construction. It is based on measuring geotechnical systems performance, updating estimates of their future performance, and implementing changes to construction activities and design if needed. Underground sensing systems embedded in soil and rock formations and inverse analysis frameworks are powerful tools that allow engineers to better realize the potential of the observational method in the age of pervasive sensing and big data. Nevertheless, these tools suffer from important limitations: below ground sensing systems often rely on wired communication that is costly, vulnerable to damage, and of limited versatility; and inverse analyses are typically performed manually by ad hoc methods or computationally by sophisticated and time-consuming methods. In this study, advances are made in wireless through-soil communication and algorithmic development of the deep-learning-based inverse analysis simulation framework, SelfSim. A communication system, termed SoilComm, was developed, enabling communicating sensor data wirelessly through soil, and hence the deployment of underground sensors that are more economical, versatile, and robust than wired sensors. This system was tested in laboratory and field environments, and its performance was demonstrated by transmission of piezometer measurements and digital images. The latest prototype achieved a 10-m communication range with a power efficiency estimated to enable its batteries to last for around 3 years of operation. The deep-learning-based inverse analysis simulation framework, SelfSim (Self Learning Simulation), is re-implemented in a modern computational framework and is highly optimized for speed and computational efficiency, enabling much faster learning of geotechnical systems behavior. The performance of the new SelfSim framework was demonstrated through inverse analyses of simulated and real triaxial laboratory tests. SelfSim run times dropped significantly, completing some analyses in less than 5 minutes, compared to hours or days using prior implementations. In addition to their employment in geotechnical applications, both tools can potentially be employed in many other fields including agricultural, mining, petroleum, material, and bio engineering."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enabling technologies for pervasive sensing and deep learning in geotechnical engineering"]}]}],"canonical_facts":{"dc:contributor":["Hashash, Youssef","Singer, Andrew","Olson, Scott","Ghaboussi, Jamshid"],"dc:creator":["Baltaji, Omar"],"dc:date":["2022-04-29T21:45:56Z","2024-04-29T21:47:53Z","2021-12","2021-12-01"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","The student, Omar Baltaji, accepted the attached license on 2021-11-24 at 07:25.","The student, Omar Baltaji, submitted this Dissertation for approval on 2021-11-24 at 07:48.","This Dissertation was approved for publication on 2021-12-01 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17261 on 2022-04-06 at 17:16:57","Made available in DSpace on 2022-04-29T21:45:56Z (GMT). No. of bitstreams: 2 BALTAJI-DISSERTATION-2021.pdf: 20347842 bytes, checksum: 799c452b2739c8e0997819792703708a (MD5) LICENSE.txt: 4209 bytes, checksum: 5c4b373dd96f5d21a9c53cb491fe65e5 (MD5) Previous issue date: 2021-12-01","Embargo set by: Seth Robbins for item 123339 Lift date: 2024-04-29T21:46:25Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 123339 Lift date: 2024-04-29T21:47:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","The observational method has been historically employed to adapt to the numerous uncertainties encountered during geotechnical construction. It is based on measuring geotechnical systems performance, updating estimates of their future performance, and implementing changes to construction activities and design if needed. Underground sensing systems embedded in soil and rock formations and inverse analysis frameworks are powerful tools that allow engineers to better realize the potential of the observational method in the age of pervasive sensing and big data. Nevertheless, these tools suffer from important limitations: below ground sensing systems often rely on wired communication that is costly, vulnerable to damage, and of limited versatility; and inverse analyses are typically performed manually by ad hoc methods or computationally by sophisticated and time-consuming methods. In this study, advances are made in wireless through-soil communication and algorithmic development of the deep-learning-based inverse analysis simulation framework, SelfSim. A communication system, termed SoilComm, was developed, enabling communicating sensor data wirelessly through soil, and hence the deployment of underground sensors that are more economical, versatile, and robust than wired sensors. This system was tested in laboratory and field environments, and its performance was demonstrated by transmission of piezometer measurements and digital images. The latest prototype achieved a 10-m communication range with a power efficiency estimated to enable its batteries to last for around 3 years of operation. The deep-learning-based inverse analysis simulation framework, SelfSim (Self Learning Simulation), is re-implemented in a modern computational framework and is highly optimized for speed and computational efficiency, enabling much faster learning of geotechnical systems behavior. The performance of the new SelfSim framework was demonstrated through inverse analyses of simulated and real triaxial laboratory tests. SelfSim run times dropped significantly, completing some analyses in less than 5 minutes, compared to hours or days using prior implementations. In addition to their employment in geotechnical applications, both tools can potentially be employed in many other fields including agricultural, mining, petroleum, material, and bio engineering."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113975"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Omar Baltaji"],"dc:subject":["Environmental engineering"],"dc:title":["Enabling technologies for pervasive sensing and deep learning in geotechnical engineering"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}