{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117568"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117568","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimal planning of maintenance activities in education buildings","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Alashari, Mishal Ahmad"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["El-Rayes, Khaled","Golparvar-Fard, Mani","El-Gohary, Nora","Attalla, Mohamed"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Optimization","Scheduling","Maintenance Planning","Maintenance Activities","Genetic Algorithm","Facility Management","Education Buildings","Roof Maintenance","Epdm Roofs","Maintenance Costs","Regression Analysis","Machine Learning","Xgboost"],"languages":["en","eng"],"rights":["Copyright 2022 Mishal Alashari"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117568","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["El-Rayes, Khaled","Golparvar-Fard, Mani","El-Gohary, Nora","Attalla, Mohamed"]},{"key":"dc:creator","label":"Author","values":["Alashari, Mishal Ahmad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-11-29"]},{"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":["Optimization","Scheduling","Maintenance Planning","Maintenance Activities","Genetic Algorithm","Facility Management","Education Buildings","Roof Maintenance","Epdm Roofs","Maintenance Costs","Regression Analysis","Machine Learning","Xgboost"]}]},{"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 2022 Mishal Alashari"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117568"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Mishal Alashari, accepted the attached license on 2022-11-22 at 19:00.","The student, Mishal Alashari, submitted this Dissertation for approval on 2022-11-22 at 19:26.","This Dissertation was approved for publication on 2022-11-29 at 11:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18623 on 2023-04-12 at 11:35:15","There are thousands of education buildings in the United States with more than 12,237 million square feet and many of these buildings are in urgent need of maintenance to ensure their operational performance because many of them were built in the 1950s and 1960s. This requires facility managers to provide accurate estimates of the annual maintenance costs of their buildings and optimize the planning of maintenance activities to develop reliable maintenance programs that addresses the needs of aging education buildings. To support facility managers in this challenging task, the research objectives of this study are to develop multivariate time series and regression models for forecasting annual maintenance costs of EPDM roofing systems, machine learning model for predicting the maintenance costs of EPDM roofing systems, and innovative optimization model for planning of maintenance activities in education buildings that minimizes the total maintenance costs. These three models were developed using novel methodologies and their performance was evaluated and refined. The main research contributions of this study include the development of novel multivariate time series and linear regression models for forecasting annual maintenance costs of EPDM roofing systems, innovative machine learning model for predicting the maintenance costs of EPDM roofs, original optimization model for the planning of maintenance activities in education buildings that minimizes the total maintenance costs, novel methodology for identifying optimal ranking of maintenance activities that have similar priority scores, innovative scheduling module for generating optimal maintenance schedule that complies with all practical time constraints on performing maintenance work such as the availability of classrooms to be maintained only during their non-operational hours, and original methodology for identifying optimal overtime use and crew size for all maintenance activities. These research contributions are expected to have significant and broad impacts on the current practices for optimizing the maintenance planning of education buildings. They have a strong potential to provide facility managers of education buildings with much-needed support to develop reliable forecasts of annual roof maintenance costs, enhance the scheduling of maintenance activities to comply with all practical constraints, and minimize the total maintenance costs of education buildings."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Optimal planning of maintenance activities in education buildings"]}]}],"canonical_facts":{"dc:contributor":["El-Rayes, Khaled","Golparvar-Fard, Mani","El-Gohary, Nora","Attalla, Mohamed"],"dc:creator":["Alashari, Mishal Ahmad"],"dc:date":["2022-12","2022-11-29"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Mishal Alashari, accepted the attached license on 2022-11-22 at 19:00.","The student, Mishal Alashari, submitted this Dissertation for approval on 2022-11-22 at 19:26.","This Dissertation was approved for publication on 2022-11-29 at 11:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18623 on 2023-04-12 at 11:35:15","There are thousands of education buildings in the United States with more than 12,237 million square feet and many of these buildings are in urgent need of maintenance to ensure their operational performance because many of them were built in the 1950s and 1960s. This requires facility managers to provide accurate estimates of the annual maintenance costs of their buildings and optimize the planning of maintenance activities to develop reliable maintenance programs that addresses the needs of aging education buildings. To support facility managers in this challenging task, the research objectives of this study are to develop multivariate time series and regression models for forecasting annual maintenance costs of EPDM roofing systems, machine learning model for predicting the maintenance costs of EPDM roofing systems, and innovative optimization model for planning of maintenance activities in education buildings that minimizes the total maintenance costs. These three models were developed using novel methodologies and their performance was evaluated and refined. The main research contributions of this study include the development of novel multivariate time series and linear regression models for forecasting annual maintenance costs of EPDM roofing systems, innovative machine learning model for predicting the maintenance costs of EPDM roofs, original optimization model for the planning of maintenance activities in education buildings that minimizes the total maintenance costs, novel methodology for identifying optimal ranking of maintenance activities that have similar priority scores, innovative scheduling module for generating optimal maintenance schedule that complies with all practical time constraints on performing maintenance work such as the availability of classrooms to be maintained only during their non-operational hours, and original methodology for identifying optimal overtime use and crew size for all maintenance activities. These research contributions are expected to have significant and broad impacts on the current practices for optimizing the maintenance planning of education buildings. They have a strong potential to provide facility managers of education buildings with much-needed support to develop reliable forecasts of annual roof maintenance costs, enhance the scheduling of maintenance activities to comply with all practical constraints, and minimize the total maintenance costs of education buildings."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117568"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Mishal Alashari"],"dc:subject":["Optimization","Scheduling","Maintenance Planning","Maintenance Activities","Genetic Algorithm","Facility Management","Education Buildings","Roof Maintenance","Epdm Roofs","Maintenance Costs","Regression Analysis","Machine Learning","Xgboost"],"dc:title":["Optimal planning of maintenance activities in education buildings"],"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":"2026-07-22T22:24:56Z"}