{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T06:03:31Z","timestamp":1776060211866,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation (NRF) of Korea","doi-asserted-by":"publisher","award":["2021R1I1A1A01052521"],"award-info":[{"award-number":["2021R1I1A1A01052521"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation (NRF) of Korea","doi-asserted-by":"publisher","award":["6148\/31\/2021"],"award-info":[{"award-number":["6148\/31\/2021"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Business Finland","award":["2021R1I1A1A01052521"],"award-info":[{"award-number":["2021R1I1A1A01052521"]}]},{"name":"Business Finland","award":["6148\/31\/2021"],"award-info":[{"award-number":["6148\/31\/2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted for global probabilistic assessment of damage at local and global levels, unlike traditional methods. A distribution-free machine learning model has been adopted for enhanced reliability in quantifying uncertainty and ensuring robustness in post-earthquake probabilistic assessments and early warning systems. The distribution-free machine learning model is capable of quantifying uncertainty with high accuracy as compared to previous methods such as the bootstrap method, etc. This research demonstrates the efficacy of the QD-LUBE method in complex seismic risk assessment scenarios, thereby contributing significant enhancement in building resilience and disaster management strategies. This study also validates the findings through fragility curve analysis, offering comprehensive insights into structural damage assessment and mitigation strategies.<\/jats:p>","DOI":"10.3390\/s24134218","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"4218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning"],"prefix":"10.3390","volume":"24","author":[{"given":"Khurram","family":"Shabbir","sequence":"first","affiliation":[{"name":"Department of Global Smart City, Sungkyunkwan University, Suwon 16419, Republic of Korea"},{"name":"Department of Civil Engineering, Aalto University, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7113-1337","authenticated-orcid":false,"given":"Muhammad","family":"Umair","sequence":"additional","affiliation":[{"name":"Department of Global Smart City, Sungkyunkwan University, Suwon 16419, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7737-1892","authenticated-orcid":false,"given":"Sung-Han","family":"Sim","sequence":"additional","affiliation":[{"name":"Department of Global Smart City, Sungkyunkwan University, Suwon 16419, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8986-3173","authenticated-orcid":false,"given":"Usman","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"}]},{"given":"Mohamed","family":"Noureldin","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Aalto University, 02150 Espoo, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1007\/s00477-022-02181-7","article-title":"Deep learning-based uncertainty quantification of groundwater level predictions","volume":"36","author":"Nourani","year":"2022","journal-title":"Stoch. 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