{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T21:42:17Z","timestamp":1780522937889,"version":"3.54.1"},"reference-count":91,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CI"],"published-print":{"date-parts":[[2024,1,9]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors\u2019 knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ci-10-2022-0278","type":"journal-article","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:14:45Z","timestamp":1674864885000},"page":"8-32","source":"Crossref","is-referenced-by-count":35,"title":["A new concept for large additive manufacturing in construction: tower crane-based 3D printing controlled by deep reinforcement learning"],"prefix":"10.1108","volume":"24","author":[{"given":"Fabio","family":"Parisi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentino","family":"Sangiorgio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Parisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agostino M.","family":"Mangini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Pia","family":"Fanti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose M.","family":"Adam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"key2024010804391656300_ref001","unstructured":"APIS (2022), \u201cAPIS\u201d, [Online], available at: www.apis-cor.com\/ (accessed 25 May 2022)."},{"key":"key2024010804391656300_ref002","first-page":"1","article-title":"ISO\/ASTM 52900: additive manufacturing\u2013general principles\u2013terminology","volume":"1","author":"ASTM International","year":"2015","journal-title":"ASTM Int"},{"key":"key2024010804391656300_ref003","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.addma.2015.05.001","article-title":"Large-scale 3D printing with a cable-suspended robot","volume":"7","year":"2015","journal-title":"Additive Manufacturing"},{"key":"key2024010804391656300_ref004","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TSMC.1983.6313077","article-title":"Neuronlike adaptive elements that can solve difficult learning control problems","volume":"SMC-13","year":"1983","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"key2024010804391656300_ref005","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1126\/science.153.3731.34","article-title":"Dynamic Programming","volume":"153","year":"1966","journal-title":"Science"},{"key":"key2024010804391656300_ref006","unstructured":"Block, I. 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