{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T21:51:09Z","timestamp":1782251469862,"version":"3.54.5"},"reference-count":124,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Hertfordshire Doctoral Scholarship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an essential element within the paradigm of digital transformation, having been widely adopted across different industries. Also, AI is anticipated to open a slew of new possibilities for how construction projects are designed and built. To obtain a better knowledge of the trend and trajectory of research concerning AI technology application in the construction industry, this research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review\u2019s findings show foremostly that AI technologies are mostly used in facility management, creating a huge opportunity for the industry to profit by allowing facility managers to take proactive action. Secondly, it shows the potential for design expansion as a key benefit according to most of the selected literature. Finally, it found data augmentation as one of the quickest prospects for technical improvement. This knowledge will assist construction companies across the world in recognizing the efficiency and productivity advantages that AI technologies can provide while helping them make smarter technology investment decisions.<\/jats:p>","DOI":"10.3390\/en17010182","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T09:35:21Z","timestamp":1703756121000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2965-8749","authenticated-orcid":false,"given":"Christian Nnaemeka","family":"Egwim","sequence":"first","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafiz","family":"Alaka","sequence":"additional","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eren","family":"Demir","sequence":"additional","affiliation":[{"name":"Decision Sciences Business Analysis and Statistics Group, Hertfordshire Business School, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5681-0585","authenticated-orcid":false,"given":"Habeeb","family":"Balogun","sequence":"additional","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0326-1719","authenticated-orcid":false,"given":"Razak","family":"Olu-Ajayi","sequence":"additional","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ismail","family":"Sulaimon","sequence":"additional","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Godoyon","family":"Wusu","sequence":"additional","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wasiu","family":"Yusuf","sequence":"additional","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adegoke A.","family":"Muideen","sequence":"additional","affiliation":[{"name":"Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"ref_1","first-page":"100166","article-title":"Applied artificial intelligence for predicting construction projects delay","volume":"6","author":"Egwim","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_2","first-page":"70","article-title":"Artificial Neural Networks for Construction Management: A Review","volume":"1","author":"Kulkarni","year":"2017","journal-title":"J. Soft Comput. Civ. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1007\/978-3-030-93733-1_41","article-title":"A Comparative Study on Machine Learning Algorithms for Assessing Energy Efficiency of Buildings","volume":"Volume 1525","author":"Egwim","year":"2021","journal-title":"Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021"},{"key":"ref_4","unstructured":"Zhang, R., and Li, D. (2011, January 8\u201310). Development of risk assessment model in construction project using fuzzy expert system. Proceedings of the 2nd IEEE International Conference on Emergency Management and Management Sciences, Beijing, China."},{"key":"ref_5","first-page":"1323","article-title":"Extraction of underlying factors causing construction projects delay in Nigeria","volume":"21","author":"Egwim","year":"2021","journal-title":"J. Eng. Des. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bajpai, A., and Misra, S.C. (2020, January 3\u20134). Identifying Critical Risk Factors for Use of Digitalization in Construction Industry: A Case Study. Proceedings of the 2020 IEEE India Council International Subsections Conference (INDISCON), Visakhapatnam, India.","DOI":"10.1109\/INDISCON50162.2020.00036"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012022","DOI":"10.1088\/1742-6596\/2040\/1\/012022","article-title":"Machine learning with health care: A perspective","volume":"2040","author":"Malik","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.7150\/ijbs.58855","article-title":"Artificial intelligence in the diagnosis of COVID-19: Challenges and perspectives","volume":"17","author":"Huang","year":"2021","journal-title":"Int. J. Biol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, J., Lim, C.P., Tan, K.H., Govindan, K., and Kumar, A. (2021). Artificial intelligence-based human-centric decision support framework: An application to predictive maintenance in asset management under pandemic environments. Ann. Oper. Res., 1\u201324.","DOI":"10.1007\/s10479-021-04373-w"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/j.procs.2018.05.095","article-title":"Smart Education with artificial intelligence based determination of learning styles","volume":"132","author":"Bajaj","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104700","DOI":"10.1016\/j.nedt.2020.104700","article-title":"Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review","volume":"97","author":"Harmon","year":"2020","journal-title":"Nurse Educ. Today"},{"key":"ref_12","unstructured":"Bao, Y., Hilary, G., and Ke, B. (2022). Innovative Technology at the Interface of Finance and Operations, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101","DOI":"10.36548\/jaicn.2021.2.003","article-title":"Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert","volume":"3","author":"Chen","year":"2021","journal-title":"J. Artif. Intell. Capsul. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"95","DOI":"10.36548\/jaicn.2019.2.005","article-title":"An Improved Safety Algorithm for Artificial Intelligence Enabled Processors in Self Driving Cars","volume":"1","author":"Manoharan","year":"2019","journal-title":"J. Artif. Intell. Capsul. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1109\/JAS.2020.1003021","article-title":"Artificial intelligence applications in the development of autonomous vehicles: A survey","volume":"7","author":"Ma","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_16","first-page":"2019","article-title":"On Defining Artificial Intelligence","volume":"10","author":"Wang","year":"2019","journal-title":"J. Artif. Gen. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104059","DOI":"10.1016\/j.autcon.2021.104059","article-title":"Natural language processing for smart construction: Current status and future directions","volume":"134","author":"Wu","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yaseen, Z.M., Ali, Z.H., Salih, S.Q., and Al-Ansari, N. (2020). Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability, 12.","DOI":"10.3390\/su12041514"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"04019085","DOI":"10.1061\/(ASCE)CO.1943-7862.0001736","article-title":"Machine Learning Algorithms for Construction Projects Delay Risk Prediction","volume":"146","author":"Gondia","year":"2020","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103127","DOI":"10.1016\/j.autcon.2020.103127","article-title":"Evidence-driven sound detection for prenotification and identification of construction safety hazards and accidents","volume":"113","author":"Lee","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/978-3-030-80312-4_50","article-title":"Severity Prediction of Construction Site Accidents Using Simple and Ensemble Decision Trees","volume":"Volume 171","author":"George","year":"2021","journal-title":"Proceedings of SECON\u201921"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xie, Y., Lee, Y.-C., Shariatfar, M., Zhang, Z.D., Rashidi, A., and Lee, H.W. (2019, January 17\u201319). Historical Accident and Injury Database-Driven Audio-Based Autonomous Construction Safety Surveillance. Proceedings of the ASCE International Conference on Computing in Civil Engineering 2019, Atlanta, GA, USA.","DOI":"10.1061\/9780784482438.014"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.wasman.2019.04.036","article-title":"A robust classification algorithm for separation of construction waste using NIR hyperspectral system","volume":"90","author":"Xiao","year":"2019","journal-title":"Waste Manag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cha, G.-W., Moon, H.J., Kim, Y.-M., Hong, W.-H., Hwang, J.-H., Park, W.-J., and Kim, Y.-C. (2020). Development of a prediction model for demolition waste generation using a random forest algorithm based on small datasets. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17196997"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cha, G.-W., Moon, H.-J., and Kim, Y.-C. (2021). Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18168530"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/S0926-5805(00)00059-5","article-title":"Robot assembly system for computer-integrated construction","volume":"9","author":"Gambao","year":"2000","journal-title":"Autom. Constr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/100.993155","article-title":"FutureHome: An integrated construction automation approach","volume":"9","author":"Balaguer","year":"2002","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.autcon.2012.12.016","article-title":"Robot-based construction automation: An application to steel beam assembly (Part I)","volume":"32","author":"Chu","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1007\/978-3-319-61431-1_31","article-title":"Concept Studies of Automated Construction Using Cable-Driven Parallel Robots","volume":"Volume 53","author":"Gosselin","year":"2018","journal-title":"Cable-Driven Parallel Robots"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, Y., Cheng, H.H., Fingrut, A., Crolla, K., Yam, Y., and Lau, D. (2018, January 16\u201319). CU-brick cable-driven robot for automated construction of complex brick structures: From simulation to hardware realisation. Proceedings of the 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), Brisbane, QLD, Australia.","DOI":"10.1109\/SIMPAR.2018.8376287"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/978-3-319-68646-2_3","article-title":"Process Analysis of Cable-Driven Parallel Robots for Automated Construction","volume":"Volume 92","author":"Ottaviano","year":"2018","journal-title":"Mechatronics for Cultural Heritage and Civil Engineering"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1080\/10803548.2015.1123516","article-title":"An expert system for the quantification of fault rates in construction fall accidents","volume":"22","author":"Birgonul","year":"2016","journal-title":"Int. J. Occup. Saf. Ergon."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1061\/(ASCE)0887-3828(1992)6:4(246)","article-title":"Expert System for Construction Safety. I: Fault-Tree Models","volume":"6","author":"Hadipriono","year":"1992","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.1016\/j.eswa.2008.02.029","article-title":"An expert system for strategic control of accidents and insurers\u2019 risks in building construction projects","volume":"36","author":"Imriyas","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"04519024","DOI":"10.1061\/(ASCE)LA.1943-4170.0000308","article-title":"Application of Natural Language Processing and Text Mining to Identify Patterns in Construction-Defect Litigation Cases","volume":"11","author":"Jallan","year":"2019","journal-title":"J. Leg. Aff. Disput. Resolut. Eng. Constr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"04021025","DOI":"10.1061\/(ASCE)AE.1943-5568.0000489","article-title":"Text Mining Risk Assessment\u2013Based Model to Conduct Uncertainty Analysis of the General Conditions of Contract in Housing Construction Projects: Case Study of the NSW GC21","volume":"27","author":"Faraji","year":"2021","journal-title":"J. Arch. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1007\/s11831-020-09504-3","article-title":"Computer Vision Techniques in Construction: A Critical Review","volume":"28","author":"Xu","year":"2020","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100868","DOI":"10.1016\/j.jobe.2019.100868","article-title":"Robotics and automated systems in construction: Understanding industry-specific challenges for adoption","volume":"26","author":"Delgado","year":"2019","journal-title":"J. Build. Eng."},{"key":"ref_39","first-page":"957","article-title":"Legal Issues and Regulatory Challenges","volume":"9","author":"Parveen","year":"2018","journal-title":"Int. J. Civ. Eng. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Schia, M.H., Trolls\u00e5s, B.C., Fyhn, H., and L\u00e6dre, O. (2020, January 6\u201312). The Introduction of AI in the Construction Industry and Its Impact on Human Behavior. Proceedings of the 27th Annual Conference of the International Group for Lean Construction (IGLC), Dublin, Ireland.","DOI":"10.24928\/2019\/0191"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103299","DOI":"10.1016\/j.jobe.2021.103299","article-title":"Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges","volume":"44","author":"Abioye","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rezaei, S. (2019). Quantitative Tourism Research in Asia: Perspectives on Asian Tourism, Springer.","DOI":"10.1007\/978-981-13-2463-5"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jhtm.2019.04.001","article-title":"A systematic review of systematic reviews in tourism","volume":"39","author":"Mura","year":"2019","journal-title":"J. Hosp. Tour. Manag."},{"key":"ref_44","unstructured":"Saunders, M.A., Lewis, P., and Thornhill, A. (2019). Research Methods for Business Students, Pearson Education. [6th ed.]. Available online: https:\/\/www.pearsoned.co.uk."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cant\u00fa-Ortiz, F.J., and Fangmeyer, J. (2017). Research Analytics: Boosting University Productivity and Competitiveness through Scientometrics, CRC Press.","DOI":"10.1201\/9781315155890"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1096\/fj.07-9492LSF","article-title":"Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses","volume":"22","author":"Falagas","year":"2008","journal-title":"FASEB J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"101584","DOI":"10.1016\/j.jobe.2020.101584","article-title":"Robotic technologies for on-site building construction: A systematic review","volume":"32","author":"Gharbia","year":"2020","journal-title":"J. Build. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"14965","DOI":"10.1007\/s00500-020-04848-1","article-title":"Hybrid machine learning for predicting strength of sustainable concrete","volume":"24","author":"Pham","year":"2020","journal-title":"Soft Comput."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, D., and Li, J. (2021, January 8\u201310). Artificial Intelligence Aided Prediction of Building Structure Anti-seismic. Proceedings of the 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC51019.2021.9418404"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"129665","DOI":"10.1016\/j.jclepro.2021.129665","article-title":"Prediction and multi-objective optimization of mechanical, economical, and environmental properties for strain-hardening cementitious composites (SHCC) based on automated machine learning and metaheuristic algorithms","volume":"329","author":"Mahjoubi","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/S0926-5805(01)00071-1","article-title":"Hybrid intelligence utilization for construction site layout","volume":"11","author":"Zhang","year":"2002","journal-title":"Autom. Constr."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, J. (2021, January 18\u201320). Potential energy saving estimation for retrofit building with ASHRAE-Great Energy Predictor III using machine learning. Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics, Guangzhou, China.","DOI":"10.1145\/3473714.3473788"},{"key":"ref_53","first-page":"303","article-title":"Machine learning-based framework for construction delay mitigation","volume":"26","author":"Zin","year":"2021","journal-title":"J. Inf. Technol. Constr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1007\/s13369-014-1032-8","article-title":"A New Hybrid Model Based on Least Squares Support Vector Machine for Project Selection Problem in Construction Industry","volume":"39","author":"Vahdani","year":"2014","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_55","first-page":"763","article-title":"Using Artificial Intelligence and computation Enhanced apply in neural network","volume":"24","author":"Varouqa","year":"2021","journal-title":"J. Appl. Sci. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.autcon.2018.12.016","article-title":"Construction site accident analysis using text mining and natural language processing techniques","volume":"99","author":"Zhang","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ssci.2019.05.027","article-title":"Safety assessment in megaprojects using artificial intelligence","volume":"118","author":"Ayhan","year":"2019","journal-title":"Saf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"103625","DOI":"10.1016\/j.cities.2022.103625","article-title":"Adopting a random forest approach to model household residential relocation behavior","volume":"125","author":"Xue","year":"2022","journal-title":"Cities"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"108656","DOI":"10.1016\/j.buildenv.2021.108656","article-title":"A novel LCSA-Machine learning based optimization model for sustainable building design\u2014A case study of energy storage systems","volume":"209","author":"Toosi","year":"2022","journal-title":"Build. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"103896","DOI":"10.1016\/j.autcon.2021.103896","article-title":"Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers","volume":"131","author":"Koc","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"04021073","DOI":"10.1061\/(ASCE)CO.1943-7862.0002088","article-title":"Estimating Residual Value of Heavy Construction Equipment Using Ensemble Learning","volume":"147","year":"2021","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"04021022","DOI":"10.1061\/(ASCE)CO.1943-7862.0002027","article-title":"Predicting the Occurrence of Construction Disputes Using Machine Learning Techniques","volume":"147","author":"Ayhan","year":"2021","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Amin, M.N., Iqtidar, A., Khan, K., Javed, M.F., Shalabi, F.I., and Qadir, M.G. (2021). Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete. Crystals, 11.","DOI":"10.3390\/cryst11070779"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"122908","DOI":"10.1109\/ACCESS.2020.3007206","article-title":"Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System","volume":"8","author":"You","year":"2020","journal-title":"IEEE Access"},{"key":"ref_65","first-page":"65","article-title":"Neuro-Fuzzy Models for Constructability Analysis","volume":"9","author":"Barai","year":"2004","journal-title":"J. Inf. Technol. Constr."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"04020030","DOI":"10.1061\/(ASCE)CP.1943-5487.0000911","article-title":"Advanced Sound Classifiers and Performance Analyses for Accurate Audio-Based Construction Project Monitoring","volume":"34","author":"Lee","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"103008","DOI":"10.1016\/j.jobe.2021.103008","article-title":"Automated classification of building structures for urban built environment identification using machine learning","volume":"43","author":"Zhou","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.measurement.2019.03.001","article-title":"The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by ANN and GP","volume":"141","author":"Bagheri","year":"2019","journal-title":"Measurement"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"103827","DOI":"10.1016\/j.autcon.2021.103827","article-title":"Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression","volume":"129","author":"Shehadeh","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1080\/09613218.2019.1692648","article-title":"Data-driven occupant actions prediction to achieve an intelligent building","volume":"48","author":"Pereira","year":"2019","journal-title":"Build. Res. Inf."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"107226","DOI":"10.1016\/j.buildenv.2020.107226","article-title":"Segmenting areas of potential contamination for adaptive robotic disinfection in built environments","volume":"184","author":"Hu","year":"2020","journal-title":"Build. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kim, J.-M., Bae, J., Son, S., Son, K., and Yum, S.-G. (2021). Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques. Sustainability, 13.","DOI":"10.3390\/su13095304"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Yu, Y., Li, H., Yang, X., and Umer, W. (2018, January 20\u201325). Estimating construction workers\u2019 physical workload by fusing computer vision and smart insole technologies. Proceedings of the 35th International Symposium on Automation and Robotics in Construction and International AEC\/FM Hackathon: The Future of Building Things (ISARC 2018), Berlin, Germany.","DOI":"10.22260\/ISARC2018\/0168"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"107836","DOI":"10.1016\/j.asoc.2021.107836","article-title":"Deep learning with small datasets: Using autoencoders to address limited datasets in construction management","volume":"112","author":"Delgado","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Charoenkwan, P., and Homkong, N. (2017, January 2\u20133). CSDeep: A crushed stone image predictor based on deep learning and intelligently selected features. Proceedings of the 2017 2nd International Conference on Information Technology (INCIT), Nakhonpathom, Thailand.","DOI":"10.1109\/INCIT.2017.8257857"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"102529","DOI":"10.1016\/j.jobe.2021.102529","article-title":"Smart performance-based design for building fire safety: Prediction of smoke motion via AI","volume":"43","author":"Su","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Norrdine, A., and Motzko, C. (2020, January 2\u20136). An internet of things based transportation cart for smart construction site. Proceedings of the 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), Rhodes, Greece.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00042"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1080\/00038628.2019.1611537","article-title":"A learning automated 3D architecture synthesis model: Demonstrating a computer governed design of minimal apartment units based on human perceptual and physical needs","volume":"62","author":"Polak","year":"2019","journal-title":"Archit. Sci. Rev."},{"key":"ref_79","first-page":"26","article-title":"Artificial intelligence and augmented reality: A possible continuum for the enhancement of built heritage","volume":"14","author":"Spallone","year":"2021","journal-title":"Disegnarecon"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"103338","DOI":"10.1016\/j.autcon.2020.103338","article-title":"Unsupervised reconstruction of Building Information Modeling wall objects from point cloud data","volume":"120","author":"Bassier","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"551","DOI":"10.5194\/isprs-archives-XLII-2-W9-551-2019","article-title":"Towards Deep Learning for Architecture: A Monument Recognition Mobile App","volume":"XLII-2\/W9","author":"Palma","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Lomio, F., Farinha, R., Laasonen, M., and Huttunen, H. (2018, January 26\u201328). Classification of Building Information Model (BIM) Structures with Deep Learning. Proceedings of the 2018 7th European Workshop on Visual Information Processing (EUVIP), Tampere, Finland.","DOI":"10.1109\/EUVIP.2018.8611701"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"012003","DOI":"10.1088\/1755-1315\/825\/1\/012003","article-title":"Research on Energy-efficiency Building Design Based on BIM and Artificial Intelligence","volume":"825","author":"Long","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_84","unstructured":"\u0160atrevi\u010ds, V., Ku\u013cikovskis, G., and O\u0161s, O. (2020, January 13\u201316). Commercialization Potential for Deep Machine Learning Technology Using Line Scan Camera. Proceedings of the 24th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2020), Virtual."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Ayadi, M.I., Maizate, A., Ouzzif, M., and Mahmoudi, C. (2019, January 14\u201317). Deep learning in building management systems over NDN: Use case of forwarding and HVAC control. Proceedings of the 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Atlanta, GA, USA.","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00200"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Keshavarzi, M., Afolabi, O., Caldas, L., Yang, A.Y., and Zakhor, A. (April, January 29). GenScan: A Generative Method for Populating Parametric 3D Scan Datasets. Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia Online and Global (CAADRIA 2021), Hong Kong, China.","DOI":"10.52842\/conf.caadria.2021.1.091"},{"key":"ref_87","unstructured":"Mei Yee, J.N., Khean, N., Madden, D., Fabbri, A., Gardner, N., Hank Haeusler, M., Zavoleas, Y., and Engineering Sydney, A. (2019, January 15\u201318). OPTIMISING IMAGE CLASSIFICATION Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections within the Architectural, Engineering and Construction (AEC) Industry. Proceedings of the 24th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Wellington, New Zealand."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1111\/risa.13425","article-title":"Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects","volume":"40","author":"Ajayi","year":"2020","journal-title":"Risk Anal."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"8501","DOI":"10.3233\/JIFS-189670","article-title":"Influence of virtual reality and 3D printing on architectural innovation evaluation based on quality of experience evaluation using fuzzy logic","volume":"40","author":"Yang","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"21","DOI":"10.24084\/repqj05.204","article-title":"Building automation by intelligent control of its environment","volume":"1","author":"Sierra","year":"2007","journal-title":"Renew. Energy Power Qual. J."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Muqeem, S., Bin Idrus, A., Khamidi, M.F., Siah, Y.K., and Saqib, M. (2012, January 12\u201314). Application of Fuzzy expert systems for construction labor productivity estimation. Proceedings of the 2012 International Conference on Computer & Information Science (ICCIS), Kuala Lampur, Malaysia.","DOI":"10.1109\/ICCISci.2012.6297298"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ssci.2016.11.008","article-title":"Fuzzy probabilistic expert system for occupational hazard assessment in construction","volume":"93","author":"Amiri","year":"2017","journal-title":"Saf. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1080\/01446199300000039","article-title":"An expert system for assessing the performance of RC beams and slabs","volume":"11","author":"Koo","year":"1993","journal-title":"Constr. Manag. Econ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.autcon.2006.09.005","article-title":"Evaluating sub-contractors performance using EFNIM","volume":"16","author":"Ko","year":"2007","journal-title":"Autom. Constr."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/S0378-7206(96)01058-0","article-title":"Case-based reasoning for intelligent support of construction negotiation","volume":"30","author":"Li","year":"1996","journal-title":"Inf. Manag."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/978-3-030-81716-9_11","article-title":"Applications of Deep Learning in Intelligent Construction","volume":"21","author":"Zhang","year":"2022","journal-title":"Struct. Integr."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s13349-021-00490-z","article-title":"Deep learning-based visual defect-inspection system for reinforced concrete bridge substructure: A case of Thailand\u2019s department of highways","volume":"11","author":"Kruachottikul","year":"2021","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"103400","DOI":"10.1016\/j.autcon.2020.103400","article-title":"Flexible and transportable robotic timber construction platform\u2013TIM","volume":"120","author":"Wagner","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Krieg, O.D., and Lang, O. (2019, January 21\u201324). Adaptive automation strategies for robotic prefabrication of parametrized mass timber building components. Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019), Banff, AB, Canada.","DOI":"10.22260\/ISARC2019\/0070"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.autcon.2018.04.004","article-title":"Productivity of digital fabrication in construction: Cost and time analysis of a robotically built wall","volume":"92","author":"Hunhevicz","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.autcon.2012.12.011","article-title":"Robot-based construction automation: An application to steel beam assembly (Part II)","volume":"32","author":"Jung","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_102","first-page":"363","article-title":"Development of an Anthropomorphic End-effector for Collaborative use on Construction Sites","volume":"Volume 2","author":"Firth","year":"2020","journal-title":"RE: Anthropocene, Design in the Age of Humans: Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2020"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"04020136","DOI":"10.1061\/(ASCE)CO.1943-7862.0001931","article-title":"Intelligent Hoisting with Car-Like Mobile Robots","volume":"146","author":"Li","year":"2020","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"125380","DOI":"10.1016\/j.jclepro.2020.125380","article-title":"Environmental and cost assessment of customized modular wall components production based on an adaptive formwork casting mechanism: An experimental study","volume":"286","author":"Kontovourkis","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"04021136","DOI":"10.1061\/(ASCE)CO.1943-7862.0002165","article-title":"Comparing Natural Language Processing Methods to Cluster Construction Schedules","volume":"147","author":"Hong","year":"2021","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"04019004","DOI":"10.1061\/(ASCE)CO.1943-7862.0001625","article-title":"Accident Case Retrieval and Analyses: Using Natural Language Processing in the Construction Industry","volume":"145","author":"Kim","year":"2019","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Sonetti, G., Naboni, E., and Brown, M. (2018). Exploring the Potentials of ICT Tools for Human-Centric Regenerative Design. Sustainability, 10.","DOI":"10.3390\/su10041217"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"04019003","DOI":"10.1061\/(ASCE)CP.1943-5487.0000807","article-title":"Development of Automatic-Extraction Model of Poisonous Clauses in International Construction Contracts Using Rule-Based NLP","volume":"33","author":"Lee","year":"2019","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, Y., Hei, X., Zhu, L., Zhao, Q., and Wang, Y. (2018, January 16\u201319). A RMM based word segmentation method for Chinese design specifications of building stairs. Proceedings of the 2018 14th International Conference on Computational Intelligence and Security (CIS), Hangzhou, China.","DOI":"10.1109\/CIS2018.2018.00068"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.enbuild.2019.07.029","article-title":"Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning","volume":"199","author":"Zhang","year":"2019","journal-title":"Energy Build."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.scs.2018.11.021","article-title":"Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities","volume":"45","author":"Ulyanin","year":"2019","journal-title":"Sustain. Cities Soc."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1080\/24725854.2021.1922785","article-title":"Inverse reinforcement learning to assess safety of a workplace under an active shooter incident","volume":"53","author":"Aghalari","year":"2021","journal-title":"IISE Trans."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"104069","DOI":"10.1016\/j.autcon.2021.104069","article-title":"Automating look-ahead schedule generation for construction using linked-data based constraint checking and reinforcement learning","volume":"134","author":"Soman","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Egwim, C.N., Alaka, H., Egunjobi, O.O., Gomes, A., and Mporas, I. (2022). Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics. J. Eng. Des. Technol.","DOI":"10.1108\/JEDT-05-2022-0238"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Egwim, C.N., Alaka, H., Pan, Y., Balogun, H., Ajayi, S., Hye, A., and Egunjobi, O.O. (2023). Ensemble of ensembles for fine particulate matter pollution prediction using big data analytics and IoT emission sensors. J. Eng. Des. Technol.","DOI":"10.1108\/JEDT-07-2022-0379"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"104192","DOI":"10.1016\/j.autcon.2022.104192","article-title":"Artificial intelligence in green building","volume":"137","author":"Debrah","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"630124","DOI":"10.3389\/frma.2021.630124","article-title":"AI Research Funding Portfolios and Extreme Growth","volume":"6","author":"Rahkovsky","year":"2021","journal-title":"Front. Res. Metrics Anal."},{"key":"ref_118","unstructured":"K\u0131lk\u0131\u015f, \u015e. (2021). Sustainable Mega City Communities, Butterworth-Heinemann."},{"key":"ref_119","unstructured":"Egwim, C.N., and Alaka, H. (2002, January 19\u201323). A Comparative Study on Machine Learning Algorithms for Predicting Construction Projects Delay. Proceedings of the Edmic 2021: Environmental Design And Management International Conference: Confluence Of Theory And Practice In The Built Environment: Beyond Theory Into Practice, Helsinki, Finland."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1108\/FEBE-05-2022-0017","article-title":"Systematic review of critical drivers for delay risk prediction: Towards a conceptual framework for BIM-based construction projects","volume":"3","author":"Egwim","year":"2022","journal-title":"Front. Eng. Built Environ."},{"key":"ref_121","unstructured":"PwC (2023, December 22). The Potential Impact of Artificial Intelligence on UK Employment and the Demand for Skills. A Report by PwC for the Department for Business, Energy and Industrial Strategy. BEIS Res. Rep. Number 2021\/042, no. August, 2021. Available online: www.pwc.com\/structure."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s40171-021-00272-y","article-title":"Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy","volume":"22","author":"Johnson","year":"2021","journal-title":"Glob. J. Flex. Syst. Manag."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"100300","DOI":"10.1016\/j.dibe.2023.100300","article-title":"GPT models in construction industry: Opportunities, limitations, and a use case validation","volume":"17","author":"Saka","year":"2024","journal-title":"Dev. Built Environ."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"101869","DOI":"10.1016\/j.aei.2022.101869","article-title":"Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities","volume":"55","author":"Saka","year":"2023","journal-title":"Adv. Eng. Inform."}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/17\/1\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:43:38Z","timestamp":1760132618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/17\/1\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"references-count":124,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["en17010182"],"URL":"https:\/\/doi.org\/10.3390\/en17010182","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]}}}