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When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.<\/jats:p>","DOI":"10.3390\/s24113347","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T11:37:13Z","timestamp":1716464233000},"page":"3347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1800-9612","authenticated-orcid":false,"given":"Misganaw","family":"Abebe","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}]},{"given":"Yonggeun","family":"Cho","sequence":"additional","affiliation":[{"name":"Korea Construction Equipment Technology Institute, 52, Saemangeumsandan 2-ro, Gunsan 54002, Republic of Korea"}]},{"given":"Seung Chul","family":"Han","sequence":"additional","affiliation":[{"name":"Korea Construction Equipment Technology Institute, 52, Saemangeumsandan 2-ro, Gunsan 54002, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7980-6140","authenticated-orcid":false,"given":"Bonyong","family":"Koo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.autcon.2019.03.025","article-title":"Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles","volume":"104","author":"Kim","year":"2019","journal-title":"Autom. 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