Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry
A Master of Science thesis in Construction Management by Sara Samir Isbaih entitled, “Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry”, submitted in June 2025. Thesis advisor is Dr. Sameh M. El-Sayegh. Soft copy is available (Thesis, Completion Certif...
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| Format: | doctoralThesis |
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2025
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| Online Access: | https://hdl.handle.net/11073/26309 |
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| _version_ | 1864513443724263424 |
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| author | Isbaih, Sara Samir |
| author_facet | Isbaih, Sara Samir |
| author_role | author |
| dc.contributor.none.fl_str_mv | El-Sayegh, Sameh |
| dc.creator.none.fl_str_mv | Isbaih, Sara Samir |
| dc.date.none.fl_str_mv | 2025-09-10T07:30:32Z 2025-09-10T07:30:32Z 2025-06 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2025.26 https://hdl.handle.net/11073/26309 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | Master of Science in Construction Management (MCM) |
| dc.subject.none.fl_str_mv | Digital Twins BIM IoT Machine Learning Construction |
| dc.title.none.fl_str_mv | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Construction Management by Sara Samir Isbaih entitled, “Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry”, submitted in June 2025. Thesis advisor is Dr. Sameh M. El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_87869d7c7b4c926d4e555117e7452544 |
| identifier_str_mv | 35.232-2025.26 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/26309 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction IndustryIsbaih, Sara SamirDigital TwinsBIMIoTMachine LearningConstructionA Master of Science thesis in Construction Management by Sara Samir Isbaih entitled, “Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry”, submitted in June 2025. Thesis advisor is Dr. Sameh M. El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).With the Industrial Revolution 4.0 era, Digital Twins are becoming important in several domains. Utilizing the recent developments in the Internet of Things, Machine Learning, and Big Data, Digital Twins are quickly growing in popularity as a tool across several industries and fields. A digital twin replicates a process, person, location, system, or equipment in the real world. The original purpose of Digital Twins was to enhance manufacturing processes through simulations that used very accurate component models, a fascinating application of this technology. Over time, with bigger and more precise building information models (BIM) and massive data produced by the Internet of Things sensors, Digital Twins are promising to solve the challenges of the construction field, implement advanced socio-technological changes, and transform the construction field digitally. However, there is a lack of applications, knowledge, and awareness of the Digital Twins concept in the AEC industry. Therefore, the main aim of this study is to determine and evaluate the importance of the key enablers for implementing digital twins in the construction sector by concentrating on understanding industry needs and assessing preparedness. The thesis studied and identified the Digital Twins enablers in the KSA and UAE construction industry. This thesis followed a systematic review to evaluate the significance level by collecting relevant data through an online survey and Subject Matter Experts (SMEs). The online survey had two scaling methods including the Likert scale and the Analytic Hierarchy Process (AHP). The online survey was conducted by 82 respondents from industry professionals and experts. The pair-wise comparison data for the AHP was used to derive relative weights and validate the consistency of judgment. The results reveal that the organizational category ranks highest among the six enabler categories in the UAE, while the technological category is top ranked in KSA. Data governance and management are the top enablers in the UAE, reflecting its focus on data-driven practices. In KSA, committed leadership and strategic vision emphasize the role of executive direction. These findings show that both countries focus on organizational strength. However, they prioritize different drivers for integrating digital twinning into the construction industry.College of EngineeringDepartment of Civil EngineeringMaster of Science in Construction Management (MSCM)El-Sayegh, Sameh2025-09-10T07:30:32Z2025-09-10T07:30:32Z2025-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2025.26https://hdl.handle.net/11073/26309en_USMaster of Science in Construction Management (MCM)oai:repository.aus.edu:11073/263092025-09-10T12:34:12Z |
| spellingShingle | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry Isbaih, Sara Samir Digital Twins BIM IoT Machine Learning Construction |
| status_str | publishedVersion |
| title | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry |
| title_full | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry |
| title_fullStr | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry |
| title_full_unstemmed | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry |
| title_short | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry |
| title_sort | Identifying and Assessing the Significance of Digital Twin Enablers in the Construction Industry |
| topic | Digital Twins BIM IoT Machine Learning Construction |
| url | https://hdl.handle.net/11073/26309 |