{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T02:12:04Z","timestamp":1768097524807,"version":"3.49.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031202407","type":"print"},{"value":"9783031202414","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-20241-4_8","type":"book-chapter","created":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T18:03:00Z","timestamp":1668880980000},"page":"101-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards the Development of a Budget Categorisation Machine Learning Tool: A Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0789-9368","authenticated-orcid":false,"given":"Lu\u00eds","family":"Jacques de Sousa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-3792","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Po\u00e7as Martins","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-5503","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Santos Baptista","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2578-6981","authenticated-orcid":false,"given":"Lu\u00eds","family":"Sanhudo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Elhegazy, H., et al.: Artificial Intelligence for developing accurate preliminary cost estimates for composite flooring systems of multi-storey buildings. J. Asian Archite. Build. Eng. (2021). https:\/\/doi.org\/10.1080\/13467581.2020.1838288","DOI":"10.1080\/13467581.2020.1838288"},{"key":"8_CR2","doi-asserted-by":"publisher","unstructured":"Pessoa, A., Sousa, G., Maues, L.M.F., Alvarenga, F.C., Santos, D.D.: Cost forecasting of public construction projects using multilayer perceptron artificial neural networks: a case study. Ingenieria E Investigacion 41(3) (2021 Dec). Art no. e87737, https:\/\/doi.org\/10.15446\/ing.investig.v41n3.87737","DOI":"10.15446\/ing.investig.v41n3.87737"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Jafari, P., Al Hattab, M., Mohamed, E., Abourizk, S.: Automated extraction and time-cost prediction of contractual reporting requirements in construction using natural language processing and simulation. Applied Sciences (Switzerland), Article 11(13) (2021). Art no. 6188, https:\/\/doi.org\/10.3390\/app11136188","DOI":"10.3390\/app11136188"},{"issue":"2","key":"8_CR4","doi-asserted-by":"publisher","first-page":"463","DOI":"10.3390\/s21020463","volume":"21","author":"S Sharma","year":"2021","unstructured":"Sharma, S., Ahmed, S., Naseem, M., Alnumay, W.S., Singh, S., Cho, G.H.: A survey on applications of artificial intelligence for pre-parametric project cost and soil shear-strength estimation in construction and geotechnical engineering. Sensors 21(2), 463 (2021). https:\/\/doi.org\/10.3390\/s21020463","journal-title":"Sensors"},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/7952434","volume":"2018","author":"M Juszczyk","year":"2018","unstructured":"Juszczyk, M., Le\u015bniak, A., Zima, K.: ANN based approach for estimation of construction costs of sports fields. Complexity 2018, 1\u201311 (2018). https:\/\/doi.org\/10.1155\/2018\/7952434","journal-title":"Complexity"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Jeon, J.H., Xu, X., Zhang, Y.X., Yang, L., Cai, H.B.: Extraction of construction quality requirements from textual specifications via natural language processing. Transportation Research Record 2675(9), 222\u2013237 (Sep 2021). Art no. 03611981211001385, https:\/\/doi.org\/10.1177\/03611981211001385","DOI":"10.1177\/03611981211001385"},{"key":"8_CR7","unstructured":"Ul Hassan, F., Le, T., Tran, D.H.: Multi-class categorisation of design-build contract requirements using text mining and natural language processing techniques. In: 2020: American Society of Civil Engineers (ASCE), pp. 1266\u20131274. [Online]. Available: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85096775363&partnerID=40&md5=9d03020e09d8cc3942e26264a6f8dc69. [Online]. Available: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85096775363&partnerID=40&md5=9d03020e09d8cc3942e26264a6f8dc69"},{"key":"8_CR8","unstructured":"Baker, H., Smith, S., Masterton, G., Hewlett, B.: Data-led learning: using natural language processing (NLP) and machine learning to learn from construction site safety failures. In: 2020: Association of Researchers in Construction Management, pp. 356\u2013365. [Online]. Available: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85096961633&partnerID=40&md5=2521ea7c5e88117decc8f8474cef482c. [Online]. Available: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85096961633&partnerID=40&md5=2521ea7c5e88117decc8f8474cef482c"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Akanbi, T., Zhang, J.S.: Design information extraction from construction specifications to support cost estimation. Autom. Constr. 131 (Nov 2021). Art no. 103835, https:\/\/doi.org\/10.1016\/j.autcon.2021.103835","DOI":"10.1016\/j.autcon.2021.103835"},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Li, R.Y.M., Li, H.C.Y., Tang, B., Au, W.C.: Fast AI classification for analysing construction accidents claims. ICST, pp. 1\u20134 (2020). https:\/\/doi.org\/10.1145\/3407703.3407705. [Online]. Available: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85090386362&doi=10.1145%2f3407703.3407705&partnerID=40&md5=eb3d8cb4611d6b48d80a5e1d1ae5171c","DOI":"10.1145\/3407703.3407705"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1177\/1087724x17737321","volume":"23","author":"L Dimitriou","year":"2018","unstructured":"Dimitriou, L., Marinelli, M., Fragkakis, N.: Early bill-of-quantities estimation of concrete road bridges: an artificial intelligence-based application. Public Works Manag. Policy 23(2), 127\u2013149 (2018). https:\/\/doi.org\/10.1177\/1087724x17737321. Apr","journal-title":"Public Works Manag. Policy"},{"issue":"1","key":"8_CR12","doi-asserted-by":"publisher","first-page":"04020147","DOI":"10.1061\/(asce)co.1943-7862.0001953","volume":"147","author":"S Moon","year":"2021","unstructured":"Moon, S., Lee, G., Chi, S., Oh, H.: Automated construction specification review with named entity recognition using natural language processing. J. Constr. Eng. Manage. 147(1), 04020147 (2021). https:\/\/doi.org\/10.1061\/(asce)co.1943-7862.0001953","journal-title":"J. Constr. Eng. Manage."},{"issue":"4","key":"8_CR13","doi-asserted-by":"publisher","first-page":"04020020","DOI":"10.1061\/(asce)me.1943-5479.0000784","volume":"36","author":"Y Cao","year":"2020","unstructured":"Cao, Y., Ashuri, B.: Predicting the volatility of highway construction cost index using long short-term memory. J. Manage. Eng. 36(4), 04020020 (2020). https:\/\/doi.org\/10.1061\/(asce)me.1943-5479.0000784","journal-title":"J. Manage. Eng."},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"217848","DOI":"10.1109\/access.2020.3042329","volume":"8","author":"X Xue","year":"2020","unstructured":"Xue, X., Jia, Y., Tang, Y.: Expressway project cost estimation with a convolutional neural network model. IEEE Access 8, 217848\u2013217866 (2020). https:\/\/doi.org\/10.1109\/access.2020.3042329","journal-title":"IEEE Access"},{"issue":"4","key":"8_CR15","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1109\/tem.2018.2856376","volume":"66","author":"H Alaka","year":"2019","unstructured":"Alaka, H., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., Ajayi, S.: A big data analytics approach for construction firms failure prediction models. IEEE Trans. Eng. Manage. 66(4), 689\u2013698 (2019). https:\/\/doi.org\/10.1109\/tem.2018.2856376","journal-title":"IEEE Trans. Eng. Manage."},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Tajziyehchi, N., Moshirpour, M., Jergeas, G., Sadeghpour, F.: A predictive model of cost growth in construction projects using feature selection. In: 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 9\u201313 Dec. 2020, pp. 142\u2013147 (2020). https:\/\/doi.org\/10.1109\/aike48582.2020.00029","DOI":"10.1109\/aike48582.2020.00029"},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"Bloch, T., Sacks, R.: Clustering information types for semantic enrichment of building information models to support automated code compliance checking. J. Comput. Civil. Eng. Article 34(6) (2020). Art no. 04020040, https:\/\/doi.org\/10.1061\/(ASCE)CP.1943-5487.0000922","DOI":"10.1061\/(ASCE)CP.1943-5487.0000922"},{"issue":"4","key":"8_CR18","doi-asserted-by":"publisher","first-page":"04519024","DOI":"10.1061\/(asce)la.1943-4170.0000308","volume":"11","author":"Y Jallan","year":"2019","unstructured":"Jallan, Y., Brogan, E., Ashuri, B., Clevenger, C.M.: Application of natural language processing and text mining to identify patterns in construction-defect litigation cases. J. Leg. Aff. Disput. Resolut. Eng. Constr. 11(4), 04519024 (2019). https:\/\/doi.org\/10.1061\/(asce)la.1943-4170.0000308","journal-title":"J. Leg. Aff. Disput. Resolut. Eng. Constr."},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Hong, Y., Xie, H.Y., Bhumbra, G., Brilakis, I.: Comparing natural language processing methods to cluster construction schedules. J. Constr. Eng. Manage. 147(10) (Oct 2021). Art no. 04021136, https:\/\/doi.org\/10.1061\/(asce)co.1943-7862.0002165","DOI":"10.1061\/(asce)co.1943-7862.0002165"},{"key":"8_CR20","unstructured":"Suneja, N., Shah, J.P., Shah, Z.H., Holia, M.S.: A neural network approach to design reality oriented cost estimate model for infrastructure projects. Reliability: Theory and Applications Article 16, 254\u2013263 (2021). [Online]. Available: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85104675716&partnerID=40&md5=9f8374c5f47f0da720ce44c182abaa56"},{"key":"8_CR21","doi-asserted-by":"publisher","unstructured":"Moon, S., Lee, G., Chi, S.: Semantic text-pairing for relevant provision identification in construction specification reviews. Autom. Constr. Article 128 (2021). Art no. 103780, https:\/\/doi.org\/10.1016\/j.autcon.2021.103780","DOI":"10.1016\/j.autcon.2021.103780"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Gaussmann, R., Coelho, D., Fernandes, A.M.R., Crocker, P., Leithardt, V.R.Q.: Using machine learning for road maintenance cost estimates in brazil: a case study in the federal district. In: 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), 24\u201327 June 2020, pp. 1\u20137 (2020). https:\/\/doi.org\/10.23919\/cisti49556.2020.9141148","DOI":"10.23919\/cisti49556.2020.9141148"},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Juszczyk, M.: Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings\u2019 floor structural frames, p. 020014 (2018). https:\/\/doi.org\/10.1063\/1.5030318. [Online]. Available: https:\/\/app.dimensions.ai\/details\/publication\/pub.1103695750","DOI":"10.1063\/1.5030318"},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Zhang, J., et al.: A RMM based word segmentation method for chinese design specifications of building stairs. In: 14th International Conference on Computational Intelligence and Security (CIS), Hangzhou, PEOPLES R CHINA, Nov 16\u201319 2018, pp. 277\u2013280 (2018). https:\/\/doi.org\/10.1109\/cis2018.2018.00068. [Online]. Available: <Go to ISI>:\/\/WOS:000456370300060","DOI":"10.1109\/cis2018.2018.00068"},{"issue":"12","key":"8_CR25","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1139\/cjce-2017-0692","volume":"46","author":"K Cho","year":"2019","unstructured":"Cho, K., Kim, J., Kim, T.: Decision support method for estimating monetary value of post-renovation office buildings. Can. J. Civ. Eng. 46(12), 1103\u20131113 (2019). https:\/\/doi.org\/10.1139\/cjce-2017-0692. Dec","journal-title":"Can. J. Civ. Eng."},{"key":"8_CR26","doi-asserted-by":"publisher","unstructured":"Juszczyk, M., Zima, K., Lelek, W.: Forecasting of sports fields construction costs aided by ensembles of neural networks. J. Civ. Eng. Manag. Article 25(7), 715\u2013729 (2019). https:\/\/doi.org\/10.3846\/jcem.2019.10534","DOI":"10.3846\/jcem.2019.10534"},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"Ronghui, S., Liangrong, N.: An intelligent fuzzy-based hybrid metaheuristic algorithm for analysis the strength, energy and cost optimisation of building material in construction management. Engineering with Computers, Article (2021). https:\/\/doi.org\/10.1007\/s00366-021-01420-9","DOI":"10.1007\/s00366-021-01420-9"},{"key":"8_CR28","doi-asserted-by":"publisher","first-page":"409","DOI":"10.36680\/j.itcon.2021.022","volume":"26","author":"J Wang","year":"2021","unstructured":"Wang, J., Gao, X.A., Zhou, X.P., Xie, Q.S.: Multi-scale information retrieval for bim using hierarchical structure modelling and natural language processing. J. Info. Technol. Constr. 26, 409\u2013426 (2021). https:\/\/doi.org\/10.36680\/j.itcon.2021.022","journal-title":"J. Info. Technol. Constr."},{"key":"8_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105688","volume":"31","author":"HH Elmousalami","year":"2020","unstructured":"Elmousalami, H.H.: Data on field canals improvement projects for cost prediction using artificial intelligence. Data Brief 31, 105688 (2020). https:\/\/doi.org\/10.1016\/j.dib.2020.105688","journal-title":"Data Brief"},{"key":"8_CR30","unstructured":"Yaqubi, M.K., Salhotra, S.: The automated cost estimation in construction. Int. J. Innov. Technol. Explor. Eng. Article 8(7), 845\u2013849 (2019). [Online]. Available: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85067886604&partnerID=40&md5=204f50af5e0b795d941ee265528cb0c1"},{"key":"8_CR31","series-title":"Lecture Notes in Civil Engineering","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/978-3-030-51295-8_8","volume-title":"Proceedings of the 18th International Conference on Computing in Civil and Building Engineering","author":"K Jeon","year":"2021","unstructured":"Jeon, K., Lee, G., Jeong, H.D.: Classification of the Requirement Sentences of the US DOT Standard Specification Using Deep Learning Algorithms. In: Toledo Santos, E., Scheer, S. (eds.) ICCCBE 2020. LNCE, vol. 98, pp. 89\u201397. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-51295-8_8"}],"container-title":["Lecture Notes in Civil Engineering","Trends on Construction in the Digital Era"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20241-4_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T18:03:58Z","timestamp":1668881038000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20241-4_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,20]]},"ISBN":["9783031202407","9783031202414"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20241-4_8","relation":{},"ISSN":["2366-2557","2366-2565"],"issn-type":[{"value":"2366-2557","type":"print"},{"value":"2366-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,20]]},"assertion":[{"value":"20 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Trends on Construction in the Post-Digital Era","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guimar\u00e3es","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isic12022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}