{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T16:18:55Z","timestamp":1756311535373,"version":"3.40.4"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031724930"},{"type":"electronic","value":"9783031724947"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-72494-7_54","type":"book-chapter","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T18:22:29Z","timestamp":1739902949000},"page":"553-560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning for Materials\u2019 Transportation Cost Prediction in Modular Construction"],"prefix":"10.1007","author":[{"given":"Maria Jos\u00e9","family":"Pereira","sequence":"first","affiliation":[]},{"given":"Eduardo","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Maria Teresa","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Marisa","family":"Guerra Pereira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"54_CR1","unstructured":"McKinsey (2019) Modular construction: from projects to products. Accessed 10 Apr 2024. [Online]. Available: https:\/\/www.mckinsey.com\/capabilities\/operations\/our-insights\/modular-construction-from-projects-to-products"},{"key":"54_CR2","doi-asserted-by":"publisher","unstructured":"Thai HT, NgoT, Uy B (2020) A review on modular construction for high-rise buildings. Structures 28:1265\u20131290. Elsevier. https:\/\/doi.org\/10.1016\/j.istruc.2020.09.070","DOI":"10.1016\/j.istruc.2020.09.070"},{"key":"54_CR3","doi-asserted-by":"publisher","first-page":"668","DOI":"10.4028\/www.scientific.net\/amm.802.668","volume":"802","author":"KMA El-Abidi","year":"2015","unstructured":"El-Abidi KMA, Ghazali FEM (2015) Motivations and limitations of prefabricated building: an overview. Appl Mech Mater 802:668\u2013675. https:\/\/doi.org\/10.4028\/www.scientific.net\/amm.802.668","journal-title":"Appl Mech Mater"},{"issue":"1","key":"54_CR4","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1108\/CI-02-2020-0017","volume":"22","author":"C Liu","year":"2022","unstructured":"Liu C, Sepasgozar SME, Shirowzhan S, Mohammadi G (2022) Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms. Constr Innov 22(1):141\u2013159. https:\/\/doi.org\/10.1108\/CI-02-2020-0017","journal-title":"Constr Innov"},{"key":"54_CR5","unstructured":"Berdiyorova I, Akhtamova P, Ganiev IM (2021) Artificial intelligence in various industries. In: Development issues of innovative economy in the agricultural sector. International Scientific-Practical Conference, pp 186\u2013193"},{"key":"54_CR6","unstructured":"Blanco JL, Fuchs S, Matt P, Ribeirinho MJ Artificial intelligence: construction technology\u2019s next frontier. Build Econ"},{"key":"54_CR7","doi-asserted-by":"publisher","unstructured":"Ghannad P, Lee Y-C (2023) Optimizing modularization of residential housing designs for rapid postdisaster mass production of housing. J Constr Eng Manag 149(7). https:\/\/doi.org\/10.1061\/(asce)co.1943-7862.0002390.","DOI":"10.1061\/(asce)co.1943-7862.0002390"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"Park K, Ergan S, Feng C (2021) Towards intelligent agents to assist in modular construction: evaluation of datasets generated in virtual environments for AI training. In: Proceedings of the ISARC Accessed 11 Mar 2024 [Online]. Available: https:\/\/par.nsf.gov\/biblio\/10388386","DOI":"10.22260\/ISARC2021\/0046"},{"issue":"4","key":"54_CR9","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1111\/mice.12504","volume":"35","author":"W Yi","year":"2020","unstructured":"Yi W, Wang S, Zhang A (2020) Optimal transportation planning for prefabricated products in construction. Comput-Aided Civ Infrastruct Eng 35(4):342\u2013353. https:\/\/doi.org\/10.1111\/mice.12504","journal-title":"Comput-Aided Civ Infrastruct Eng"},{"key":"54_CR10","doi-asserted-by":"publisher","unstructured":"Tayefeh Hashemi S, Ebadati OM, Kaur H (2020) Cost estimation and prediction in construction projects: a systematic review on machine learning techniques. SN Appl Sci 2(10):1703. Springer Nature. https:\/\/doi.org\/10.1007\/s42452-020-03497-1","DOI":"10.1007\/s42452-020-03497-1"},{"key":"54_CR11","doi-asserted-by":"publisher","unstructured":"Shinde PP, Shah S (2018) A review of machine learning and deep learning applications. In: 2018 fourth international conference on computing communication control and automation (ICCUBEA), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICCUBEA.2018.8697857","DOI":"10.1109\/ICCUBEA.2018.8697857"},{"issue":"3","key":"54_CR12","doi-asserted-by":"publisher","first-page":"129","DOI":"10.56578\/ataiml020302","volume":"2","author":"F Farchi","year":"2023","unstructured":"Farchi F, Farchi C, Touzi B, Mabrouki C (2023) A comparative study on AI-based algorithms for cost prediction in pharmaceutical transport logistics. Acadlore Trans AI Mach Learn 2(3):129\u2013141. https:\/\/doi.org\/10.56578\/ataiml020302","journal-title":"Acadlore Trans AI Mach Learn"},{"key":"54_CR13","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/OJCS.2021.3052518","volume":"2","author":"X Zeng","year":"2021","unstructured":"Zeng X, Lin S, Liu C (2021) Multi-view deep learning framework for predicting patient expenditure in healthcare. IEEE Open J Comput Soc 2:62\u201371. https:\/\/doi.org\/10.1109\/OJCS.2021.3052518","journal-title":"IEEE Open J Comput Soc"},{"key":"54_CR14","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.jmsy.2019.12.005","volume":"54","author":"F Ning","year":"2020","unstructured":"Ning F, Shi Y, Cai M, Xu W, Zhang X (2020) Manufacturing cost estimation based on a deep-learning method. J Manuf Syst 54:186\u2013195. https:\/\/doi.org\/10.1016\/j.jmsy.2019.12.005","journal-title":"J Manuf Syst"},{"key":"54_CR15","doi-asserted-by":"publisher","unstructured":"Malistov A, Trushin A (2019) Gradient boosted trees with extrapolation. In: Proceedings\u201418th IEEE international conference on machine learning and applications ICMLA 2019 Institute of Electrical and Electronics Engineers Inc., pp 783\u2013789. https:\/\/doi.org\/10.1109\/ICMLA.2019.00138","DOI":"10.1109\/ICMLA.2019.00138"},{"key":"54_CR16","doi-asserted-by":"publisher","unstructured":"Kulkarni P, Gala I, Nargundkar A (2022) Freight cost prediction using machine learning algorithms. In: International conference on intelligent systems and applications. Lecture notes in electrical engineering 959, pp 507\u2013515. https:\/\/doi.org\/10.1007\/978-981-19-6581-4_40","DOI":"10.1007\/978-981-19-6581-4_40"}],"container-title":["Springer Proceedings in Business and Economics","Human-Centred Technology Management for a Sustainable Future"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72494-7_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T17:22:30Z","timestamp":1745515350000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72494-7_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031724930","9783031724947"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72494-7_54","relation":{},"ISSN":["2198-7246","2198-7254"],"issn-type":[{"type":"print","value":"2198-7246"},{"type":"electronic","value":"2198-7254"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"19 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IAMOT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Association for the Management of Technology Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iamot2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iamot2024.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}