{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:41:44Z","timestamp":1783402904819,"version":"3.54.6"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia","award":["KFU260416"],"award-info":[{"award-number":["KFU260416"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) fa\u00e7ade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of fa\u00e7ade mechanical performance and lifecycle indicators (CO2 emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material\u2013building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient fa\u00e7ade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for fa\u00e7ade design decisions in smart built environments.<\/jats:p>","DOI":"10.3390\/info17020126","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T10:08:57Z","timestamp":1769681337000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI-Enabled System-of-Systems Decision Support: BIM-Integrated AI-LCA for Resilient and Sustainable Fiber-Reinforced Fa\u00e7ade Design"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1121-2523","authenticated-orcid":false,"given":"Mohammad Q.","family":"Al-Jamal","sequence":"first","affiliation":[{"name":"Department of Renewable Energy, Technical Faculty, Jadara University, P.O. Box 733, Irbid 21110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9075-2828","authenticated-orcid":false,"given":"Ayoub","family":"Alsarhan","sequence":"additional","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19111, Jordan"},{"name":"Department of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, The Hashemite University, Zarqa 13133, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3801-797X","authenticated-orcid":false,"given":"Qasim","family":"Aljamal","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Architecture and Civil Engineering, Technical University Dortmund, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5389-6778","authenticated-orcid":false,"given":"Mahmoud","family":"AlJamal","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity, Science and Information Technology, Irbid National University, Irbid 21110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4647-0460","authenticated-orcid":false,"given":"Bashar S.","family":"Khassawneh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Systems, College of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9111-8040","authenticated-orcid":false,"given":"Ahmed","family":"Al Nuaim","sequence":"additional","affiliation":[{"name":"Department of Management Information System, School of Buisiness, King Fisal University, Al Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7647-3060","authenticated-orcid":false,"given":"Abdullah","family":"Al Nuaim","sequence":"additional","affiliation":[{"name":"Department of Management Information System, School of Buisiness, King Fisal University, Al Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Przybek, A. 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