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Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This study aims to enhance the decision-making process by identifying optimal construction techniques, material selection, and ventilation window dimensions to promote sustainable energy use in buildings. A novel framework combining Building Information Modeling (BIM), computational analysis, and Multi-Criteria Decision-Making (MCDM) approaches is applied to assess the energy use intensity (EUI), annual electric energy consumption, and lifecycle energy cost across multiple sequences for each type of construction. Computational analysis in this research is combined in two main tools. Minitab is utilized for experimental design to determine the number and configurations of sequences analyzed. The Simple Additive Weighting (SAW) method, applied as an MCDM tool, is used to assess and rank the performance of sequences based on equally weighted criteria. Subsequently, 3D models of case study buildings are developed, and energy simulations are conducted using Autodesk Revit and Autodesk Green Building Studio, respectively, as BIM tools to compare the energy performance of various design alternatives. The results revealed that 3D printing surpassed other methods, where Sequence 7 achieved approximately 10.3% higher efficiency than modular methods and 40.5% better performance than conventional methods in the evaluated criteria. The findings underscore the higher energy efficiency of 3D printing, followed by modular construction as a competitive method, while conventional methods lagged significantly.<\/jats:p>","DOI":"10.3390\/computation12120247","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T08:56:32Z","timestamp":1734512192000},"page":"247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Comparative Analysis of Energy Efficiency in Conventional, Modular, and 3D-Printing Construction Using Building Information Modeling and Multi-Criteria Decision-Making"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4131-6165","authenticated-orcid":false,"given":"Abdullah","family":"Al Masri","sequence":"first","affiliation":[{"name":"Programa de Engenharia Ambiental, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4793-0905","authenticated-orcid":false,"given":"Assed N.","family":"Haddad","sequence":"additional","affiliation":[{"name":"Programa de Engenharia Ambiental, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3407-4142","authenticated-orcid":false,"given":"Mohammad K.","family":"Najjar","sequence":"additional","affiliation":[{"name":"Programa de Engenharia Ambiental, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-901, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al Masri, A., da Costa, B.B.F., Vasco, D., Boer, D., Haddad, A.N., and Najjar, M.K. 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