Developing a UAE-Based Disputes Prediction Model using Machine Learning

A Master of Science thesis in Construction Management by Ibrahim Wasef Subhi Abu Laila entitled, “Developing a UAE-Based Disputes Prediction Model using Machine Learning”, submitted in April 2022. Thesis advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Abu Laila, Ibrahim Wasef Subhi (author)
التنسيق: doctoralThesis
منشور في: 2022
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/11073/24086
الوسوم: إضافة وسم
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author Abu Laila, Ibrahim Wasef Subhi
author_facet Abu Laila, Ibrahim Wasef Subhi
author_role author
dc.contributor.none.fl_str_mv El-Sayegh, Sameh
dc.creator.none.fl_str_mv Abu Laila, Ibrahim Wasef Subhi
dc.date.none.fl_str_mv 2022-09-05T08:50:51Z
2022-09-05T08:50:51Z
2022-04
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 35.232-2022.06
http://hdl.handle.net/11073/24086
dc.language.none.fl_str_mv en_US
dc.subject.none.fl_str_mv Construction Disputes
Prediction Models
Machine Learning (ML)
Artificial Neural Networks (ANN)
Random Forests
Support Vectors Machine (SVM)
dc.title.none.fl_str_mv Developing a UAE-Based Disputes Prediction Model using Machine Learning
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 Ibrahim Wasef Subhi Abu Laila entitled, “Developing a UAE-Based Disputes Prediction Model using Machine Learning”, submitted in April 2022. Thesis advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form)
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network_acronym_str aus
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oai_identifier_str oai:repository.aus.edu:11073/24086
publishDate 2022
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spelling Developing a UAE-Based Disputes Prediction Model using Machine LearningAbu Laila, Ibrahim Wasef SubhiConstruction DisputesPrediction ModelsMachine Learning (ML)Artificial Neural Networks (ANN)Random ForestsSupport Vectors Machine (SVM)A Master of Science thesis in Construction Management by Ibrahim Wasef Subhi Abu Laila entitled, “Developing a UAE-Based Disputes Prediction Model using Machine Learning”, submitted in April 2022. Thesis advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form)Disputes are a major phenomenon in the construction industry around the world that stems from unsettled disagreements between project stakeholders. These types of disagreements can take place in any project regardless of its size and properties. The UAE is no different. To resolve these disputes, a substantial amount of money and time must be allocated which might cause the project to collapse. As a result, proactive construction management is needed to prevent this issue from arising in the first place. The aim of this research is to develop prediction models for construction disputes, thereby providing early insights to the stakeholders, and thus making precautionary measures that can prevent these disputes from taking place during the project execution. Initially, a literature review was performed to gather the input parameters needed for the prediction models. These parameters were verified by industry experts using a preliminary survey to find out the most important ones. The top three parameters were found to be the general experience and competence of the contractor, the project size, and the level of contract readiness. Moreover, another survey was administered in order to acquire actual project data that will be the input for the proposed prediction models. The sample size was 79 projects, where 67% of these projects faced disputes. These models will be able to predict dispute occurrence, the number of disputes, the impact of disputes on time and cost, the dispute resolution procedure, as well as the time and cost of the disputes resolution. Additionally, three different Machine Learning (ML) algorithms, Artificial Neural Networks (ANN), Support Vectors Machine (SVM), and Random Forests, were used to run these models and perform predictions. It was found that SVM and Random Forests provided better results in terms of accuracy in all of the seven models. Most of the models were well-performing since the testing accuracies lies within the 70-90% range, with disputes resolution duration prediction model even exceeded the 90% mark. Furthermore, as demonstrated in the case study, these models were successful in predicting the different aspects of disputes and demonstrated that they can be implemented in future projects to achieve disputes mitigation.College of EngineeringMultidisciplinary ProgramsMaster of Science in Construction Management (MSCM)El-Sayegh, Sameh2022-09-05T08:50:51Z2022-09-05T08:50:51Z2022-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2022.06http://hdl.handle.net/11073/24086en_USoai:repository.aus.edu:11073/240862025-06-26T12:20:20Z
spellingShingle Developing a UAE-Based Disputes Prediction Model using Machine Learning
Abu Laila, Ibrahim Wasef Subhi
Construction Disputes
Prediction Models
Machine Learning (ML)
Artificial Neural Networks (ANN)
Random Forests
Support Vectors Machine (SVM)
status_str publishedVersion
title Developing a UAE-Based Disputes Prediction Model using Machine Learning
title_full Developing a UAE-Based Disputes Prediction Model using Machine Learning
title_fullStr Developing a UAE-Based Disputes Prediction Model using Machine Learning
title_full_unstemmed Developing a UAE-Based Disputes Prediction Model using Machine Learning
title_short Developing a UAE-Based Disputes Prediction Model using Machine Learning
title_sort Developing a UAE-Based Disputes Prediction Model using Machine Learning
topic Construction Disputes
Prediction Models
Machine Learning (ML)
Artificial Neural Networks (ANN)
Random Forests
Support Vectors Machine (SVM)
url http://hdl.handle.net/11073/24086