{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:51:10Z","timestamp":1776415870694,"version":"3.51.2"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research (Bundesministerium f\u00fcr Bildung und Forschung) BMBF","award":["02P17D249"],"award-info":[{"award-number":["02P17D249"]}]},{"name":"German Federal Ministry of Education and Research (Bundesministerium f\u00fcr Bildung und Forschung) BMBF","award":["DIK0415\/08"],"award-info":[{"award-number":["DIK0415\/08"]}]},{"name":"Bavarian Collaborative Research Program of the Bavarian State Government","award":["02P17D249"],"award-info":[{"award-number":["02P17D249"]}]},{"name":"Bavarian Collaborative Research Program of the Bavarian State Government","award":["DIK0415\/08"],"award-info":[{"award-number":["DIK0415\/08"]}]},{"name":"Project Production Systems Laboratory (P2SL, p2sl.berkeley.edu) at UC Berkeley","award":["02P17D249"],"award-info":[{"award-number":["02P17D249"]}]},{"name":"Project Production Systems Laboratory (P2SL, p2sl.berkeley.edu) at UC Berkeley","award":["DIK0415\/08"],"award-info":[{"award-number":["DIK0415\/08"]}]},{"name":"TUM Publishing Fund","award":["02P17D249"],"award-info":[{"award-number":["02P17D249"]}]},{"name":"TUM Publishing Fund","award":["DIK0415\/08"],"award-info":[{"award-number":["DIK0415\/08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>As in manufacturing with its Industry 4.0 transformation, the enormous potential of artificial intelligence (AI) is also being recognized in the construction industry. Specifically, the equipment-intensive construction industry can benefit from using AI. AI applications can leverage the data recorded by the numerous sensors on machines and mirror them in a digital twin. Analyzing the digital twin can help optimize processes on the construction site and increase productivity. We present a case from special foundation engineering: the machine production of bored piles. We introduce a hierarchical classification for activity recognition and apply a hybrid deep learning model based on convolutional and recurrent neural networks. Then, based on the results from the activity detection, we use discrete-event simulation to predict construction progress. We highlight the difficulty of defining the appropriate modeling granularity. While activity detection requires equipment movement, simulation requires knowledge of the production flow. Therefore, we present a flow-based production model that can be captured in a modularized process catalog. Overall, this paper aims to illustrate modeling using digital-twin technologies to increase construction process improvement in practice.<\/jats:p>","DOI":"10.3390\/a16040212","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T05:18:13Z","timestamp":1681795093000},"page":"212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["From Activity Recognition to Simulation: The Impact of Granularity on Production Models in Heavy Civil Engineering"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2106-3735","authenticated-orcid":false,"given":"Anne","family":"Fischer","sequence":"first","affiliation":[{"name":"Chair of Materials Handling Material Flow Logistics, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7934-8410","authenticated-orcid":false,"given":"Alexandre","family":"Beiderwellen Bedrikow","sequence":"additional","affiliation":[{"name":"Chair of Materials Handling Material Flow Logistics, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany"}]},{"given":"Iris D.","family":"Tommelein","sequence":"additional","affiliation":[{"name":"Civil and Environment Department, Project Production Systems Laboratory (P2SL), University of California, Berkeley, CA 94720-1712, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2863-1360","authenticated-orcid":false,"given":"Konrad","family":"N\u00fcbel","sequence":"additional","affiliation":[{"name":"Chair of Construction Process Management, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6392-0371","authenticated-orcid":false,"given":"Johannes","family":"Fottner","sequence":"additional","affiliation":[{"name":"Chair of Materials Handling Material Flow Logistics, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12599-014-0334-4","article-title":"Industry 4.0","volume":"6","author":"Lasi","year":"2014","journal-title":"Bus. 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