The DOI matches its Crossref record; 138 of the 140 references it cites were found in a registry or an index.
Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities
Open accessAbstract
The idea of developing a system that can converse and understand human languages has been around since the 1200 s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple’s Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry, but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“All these additional cost overheads may be a prohibiting factor for smaller construction companies with limited financial resources (Saka et al. 2023b ).”
From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Saka et al. 2023b
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Computing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911
The Ethics of AI Ethics -- An Evaluation of Guidelines
Hagendorff, T. (2019) · arXiv · doi:10.48550/arxiv.1903.03425 · Cited by 1,907
Towards a semantic Construction Digital Twin: Directions for future research
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103179 · Cited by 1,477
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
National Institute of Standards and Technology
(1990) · Analytical Chemistry · doi:10.1021/ac00210a736 · Cited by 1,314
Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Akinade, O. O., & Ahmed, A. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.103299 · Cited by 1,015
Self-citationTransfer learning using computational intelligence: A survey
Jie Lu, B., Behbood, V., Peng, H., Zuo, H., Xue, S., & Zhang, G. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.01.010 · Cited by 971
Digital twin application in the construction industry: A literature review
Opoku, D.-G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102726 · Cited by 753
Big Data in the construction industry: A review of present status, opportunities, and future trends
Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinadé, O. O., Owolabi, H. A., Alaka, H., & Pasha, M. (2016) · Advanced Engineering Informatics · doi:10.1016/j.aei.2016.07.001 · Cited by 737
Self-citationDarko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103081 · Cited by 679
McTear, M., Callejas, Z., & Griol, D. (2016) · doi:10.1007/978-3-319-32967-3 · Cited by 594
A BIM-data mining integrated digital twin framework for advanced project management
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103564 · Cited by 553
Deep learning in the construction industry: A review of present status and future innovations
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A., Delgado, J. M. D., Akinadé, O. O., & Ahmed, A. A. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101827 · Cited by 544
Self-citationArtificial Intelligence (AI) Ethics: Ethics of AI and Ethical AI
Siau, K., & Wang, W. (2020) · Journal of Database Management · doi:10.4018/jdm.2020040105 · Cited by 533
Gorin, A. L., Riccardi, G., & Wright, J. H. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00040-x · Cited by 510
A research agenda for augmented and virtual reality in architecture, engineering and construction
Delgado, J. M. D., Oyedele, L. O., Demian, P., & Beach, T. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101122 · Cited by 491
Self-citationSafety Hazard Identification on Construction Projects
Carter, G. L., & Smith, S. D. (2006) · Journal of Construction Engineering and Management · doi:10.1061/(asce)0733-9364(2006)132:2(197 · Cited by 477
Salvaging building materials in a circular economy: a BIM-based whole-life performance estimator
Akanbi, L. A., Oyedele, L. O., Akinade, O. O., Ajayi, A. O., Davila Delgado, M., Bilal, M., & Bello, S. A. (2018) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2017.10.026 · Cited by 468
Self-citationCloud computing in construction industry: Use cases, benefits and challenges
Bello, S. A., Oyedele, L. O., Akinadé, O. O., Bilal, M., Delgado, J. M. D., Akanbi, L., Ajayi, A., & Owolabi, H. A. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103441 · Cited by 452
Self-citationLi, C. Z., Xue, F., Li, X., Hong, J., & Shen, Q. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.01.001 · Cited by 420
Hazard recognition and risk perception in construction
Perlman, A., Sacks, R., & Barak, R. (2013) · Safety Science · doi:10.1016/j.ssci.2013.11.019 · Cited by 417
Embodied conversational interface agents
Cassell, J. (2000) · Communications of the ACM · doi:10.1145/332051.332075 · Cited by 388
A systematic literature review: Opinion mining studies from mobile app store user reviews
Genc-Nayebi, N., & Abran, A. (2016) · Journal of Systems and Software · doi:10.1016/j.jss.2016.11.027 · Cited by 358
SuperAgent: A Customer Service Chatbot for E-commerce Websites
Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., & Zhou, M. (2017) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/p17-4017 · Cited by 357
A Social Virtual Reality Based Construction Safety Education System for Experiential Learning
Le, Q. T., Pedro, A., & Park, C. (2014) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0112-z · Cited by 327
Social Dialongue with Embodied Conversational Agents
Bickmore, T., & Cassell, J. (2005) · Text, Speech and Language Technology · doi:10.1007/1-4020-3933-6_2 · Cited by 324
Park, J., Kim, K., & Cho, Y. K. (2016) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001223 · Cited by 301
From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition
Morris, A. C., Maier, V., & Green, P. (2004) · Interspeech 2004 · doi:10.21437/interspeech.2004-668 · Cited by 290
The trouble with European data protection law
Koops, B. (2014) · International Data Privacy Law · doi:10.1093/idpl/ipu023 · Cited by 288
Artificial intelligence in green building
Debrah, C., Chan, A. P. C., & Darko, A. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104192 · Cited by 278
Morris, R. R., Kouddous, K., Kshirsagar, R., & Schueller, S. M. (2018) · Journal of Medical Internet Research · doi:10.2196/10148 · Cited by 273
Delgado, J. M. D., & Oyedele, L. O. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101332 · Cited by 272
Self-citationSchachner, T., Keller, R., & v Wangenheim, F. (2020) · Journal of Medical Internet Research · doi:10.2196/20701 · Cited by 267
Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review
Elmousalami, H. H. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001678 · Cited by 266
Construction risk knowledge management in BIM using ontology and semantic web technology
Ding, L., Zhong, B. T., Wu, S., & Luo, H. (2016) · Safety Science · doi:10.1016/j.ssci.2016.04.008 · Cited by 256
Reiter, E., & Belz, A. (2009) · International Conference on Computational Logic · doi:10.1162/coli.2009.35.4.35405 · Cited by 253
Intelligent personal assistants: A systematic literature review
Silva, A. D. B., Gomes, M. M., da Costa, C. A., Righi, R. D. R., Barbosa, J. L. V., Pessin, G., Doncker, G. D., & Federizzi, G. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113193 · Cited by 238
Service chatbots: A systematic review
Suhaili, S. M., Salim, N. B., & Jambli, M. N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115461 · Cited by 209
Deep learning model for Demolition Waste Prediction in a circular economy
Akanbi, L., Oyedele, A. O., Oyedele, L. O., & Salami, R. O. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.122843 · Cited by 208
Self-citationBenchmarking Natural Language Understanding Services for Building Conversational Agents
Liu, X., Eshghi, A., Swietojanski, P., & Rieser, V. (2021) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-15-9323-9_15 · Cited by 208
Real versus Template-Based Natural Language Generation: A False Opposition?
van Deemter, K., Theune, M., & Krahmer, E. (2005) · Computational Linguistics · doi:10.1162/0891201053630291 · Cited by 207
Augmented and Virtual Reality in Construction: Drivers and Limitations for Industry Adoption
Delgado, J. M. D., Oyedele, L. O., Beach, T., & Demian, P. (2020) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001844 · Cited by 205
Self-citationDesign for deconstruction using a circular economy approach: barriers and strategies for improvement
Akinadé, O. O., Oyedele, L. O., Oyedele, A. O., Delgado, J. M. D., Bilal, M., Akanbi, L., Ajayi, A., & Owolabi, H. A. (2019) · Production Planning & Control · doi:10.1080/09537287.2019.1695006 · Cited by 201
Self-citationO’Shaughnessy, D. D. (2015) · IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) · doi:10.1109/chilecon.2015.7400411 · Cited by 192
Big data architecture for construction waste analytics (CWA): A conceptual framework
Bilal, M., Oyedele, L. O., Akinadé, O. O., Ajayi, S. O., Alaka, H., Owolabi, H. A., Qadir, J., Pasha, M., & Bello, S. A. (2016) · Journal of Building Engineering · doi:10.1016/j.jobe.2016.03.002 · Cited by 191
Self-citationLuo, X., & Oyedele, L. O. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101357 · Cited by 191
Self-citationA Natural‐Language‐Based Approach to Intelligent Data Retrieval and Representation for Cloud BIM
Lin, J., Hu, Z., Zhang, J., & Yu, F. (2015) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12151 · Cited by 174
A framework for using mobile computing for information management on construction sites
Yuan, C., & Kamara, J. M. (2011) · Automation in Construction · doi:10.1016/j.autcon.2011.01.002 · Cited by 169
Dialogue management in the Mercury flight reservation system
Seneff, S., & Polifroni, J. H. (2000) · doi:10.3115/1117562.1117565 · Cited by 166
A Scientometric Review and Metasynthesis of Building Information Modelling (BIM) Research in Africa
Saka, A. B., & Chan, D. W. M. (2019) · Buildings · doi:10.3390/buildings9040085 · Cited by 164
Self-citationApplications of machine learning to BIM: A systematic literature review
Zabin, A., González, V. A., Zou, Y., & Amor, R. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101474 · Cited by 163
Data mining in the construction industry: Present status, opportunities, and future trends
Yan, H., Yang, N., Peng, Y., & Ren, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103331 · Cited by 161
Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots
McTear, M. (2021) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02176-3 · Cited by 155
Dendritic solutions to the credit assignment problem
Richards, B. A., & Lillicrap, T. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.08.003 · Cited by 155
Offsite construction: Developing a BIM-Based optimizer for assembly
Gbadamosi, A.-Q., Mahamadu, A., Oyedele, L. O., Akinadé, O. O., Manu, P., Mahdjoubi, L., & Aigbavboa, C. O. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.01.113 · Cited by 152
Self-citationAntwi‐Afari, M. F., Li, H., Yu, Y., & Kong, L. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.10.004 · Cited by 143
Conversational agents in business: A systematic literature review and future research directions
Bavaresco, R., Silveira, D., Reis, E., Barbosa, J., Righi, R., Costa, C., Antunes, R., Gomes, M., Gatti, C., Vanzin, M., et al. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100239 · Cited by 143
Oyedele, A. A., Ajayi, A., Oyedele, L. O., Bello, S. A., & Jimoh, K. O. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119233 · Cited by 137
Self-citationIntegration of virtually real construction model and design-for-safety-process database
Hadikusumo, B. H. W., & Rowlinson, S. (2002) · Automation in Construction · doi:10.1016/s0926-5805(01)00061-9 · Cited by 134
Luo, X., Oyedele, L. O., Ajayi, A., & Akinadé, O. O. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102283 · Cited by 125
Self-citationA building regulation question answering system: A deep learning methodology
Zhong, B., He, W., Huang, Z., Love, P. E. D., Tang, J., & Luo, H. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101195 · Cited by 119
A query expansion method for retrieving online BIM resources based on Industry Foundation Classes
Gao, G., Liu, Y.-S., Wang, M., Gu, M., & Yong, J. (2015) · Automation in Construction · doi:10.1016/j.autcon.2015.04.006 · Cited by 103
Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects
Ajayi, A., Oyedele, L. O., Owolabi, H. A., Akinadé, O. O., Bilal, M., Delgado, J. M. D., & Akanbi, L. (2019) · Risk Analysis · doi:10.1111/risa.13425 · Cited by 102
Self-citationDelgado, J. M. D., & Oyedele, L. O. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107836 · Cited by 102
Self-citationIvanov, S., & Webster, C. (2019) · Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality · doi:10.1108/978-1-78756-687-320191002 · Cited by 102
BIM competencies for delivering waste-efficient building projects in a circular economy
Ganiyu, S. A., Oyedele, L. O., Akinadé, O. O., Owolabi, H. A., Akanbi, L., & Gbadamosi, A. (2020) · Developments in the Built Environment · doi:10.1016/j.dibe.2020.100036 · Cited by 98
Self-citationManley, K. (2008) · Research Policy · doi:10.1016/j.respol.2008.07.013 · Cited by 97
The design brief as carrier of client information during the construction process
Ryd, N. (2003) · Design Studies · doi:10.1016/j.destud.2003.10.003 · Cited by 96
Ajayi, S. O., Oyedele, L. O., & Ilori, O. M. (2019) · Journal of Building Engineering · doi:10.1016/j.jobe.2019.02.008 · Cited by 93
Self-citationDelgado, J. M. D., & Oyedele, L. O. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101787 · Cited by 92
Self-citationClients’ assessment of architects’ performance in building delivery process: Evidence from Nigeria
Oyedele, L. O., & Tham, K. W. (2006) · Building and Environment · doi:10.1016/j.buildenv.2005.06.030 · Cited by 91
Self-citationA state-of-the-art review on artificial intelligence for Smart Buildings
Panchalingam, R., & Chan, K. C. (2019) · Intelligent Buildings International · doi:10.1080/17508975.2019.1613219 · Cited by 90
Natural-language-based intelligent retrieval engine for BIM object database
Wu, S., Shen, Q., Deng, Y., & Cheng, J. C. P. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.02.016 · Cited by 90
Conversational AI: An Overview of Methodologies, Applications & Future Scope
Kulkarni, P., Mahabaleshwarkar, A. S., Kulkarni, M., Sirsikar, N., & Gadgil, K. (2019) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea47591.2019.9129347 · Cited by 87
The application of PDA as mobile computing system on construction management
木本, 健., Kazuyoshi, E. N. D. O., 岩下, 智., & Fujiwara, M. (2004) · Automation in Construction · doi:10.1016/j.autcon.2004.09.003 · Cited by 87
Big data for Design Options Repository: Towards a DFMA approach for offsite construction
Gbadamosi, A.-Q., Oyedele, L. O., Mahamadu, A., Kusimo, H., Bilal, M., Delgado, J. M. D., & Muhammed-Yakubu, N. T. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103388 · Cited by 85
Self-citationNLP-Based Query-Answering System for Information Extraction from Building Information Models
Wang, N., Issa, R. R. A., & Anumba, C. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0001019 · Cited by 84
Analysis of architects' demotivating factors in design firms
Oyedele, L. O. (2012) · International Journal of Project Management · doi:10.1016/j.ijproman.2012.11.009 · Cited by 82
Self-citationAutomated staff assignment for building maintenance using natural language processing
Mo, Y., Zhao, D. F., Du, J., Syal, M., Aziz, A., & Li, H. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103150 · Cited by 82
Bilal, M., Oyedele, L. O., Kusimo, H., Owolabi, H. A., Akanbi, L., Ajayi, A., Akinadé, O. O., & Delgado, J. M. D. (2019) · Journal of Building Engineering · doi:10.1016/j.jobe.2019.100850 · Cited by 80
Self-citationGuidelines for applied machine learning in construction industry—A case of profit margins estimation
Bilal, M., & Oyedele, L. O. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.101013 · Cited by 79
Self-citationWagner, P., Beskow, J., Betz, S., Edlund, J., Gustafson, J., Eje Henter, G., Le Maguer, S., Malisz, Z., Székely, É., Tånnander, C., et al. (2019) · 10th ISCA Workshop on Speech Synthesis (SSW 10) · doi:10.21437/ssw.2019-19 · Cited by 74
Artificial intelligence accelerated by light
Wu, H., & Dai, Q. (2021) · Nature · doi:10.1038/d41586-020-03572-y · Cited by 71
Strategies to improve mental health and well-being within the UK construction industry
Campbell, M. A., & Gunning, J. G. (2020) · Proceedings of the Institution of Civil Engineers - Management Procurement and Law · doi:10.1680/jmapl.19.00020 · Cited by 70
Alaka, H. A., & Oyedele, L. O. (2016) · Journal of Building Engineering · doi:10.1016/j.jobe.2016.09.008 · Cited by 69
Self-citationArtificial intelligence-based voice assistant for BIM data management
Elghaish, F., Chauhan, J. K., Matarneh, S. T., Rahimian, F. P., & Hosseini, M. R. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104320 · Cited by 66
Saka, A. B., & Chan, D. W. M. (2019) · Architectural Engineering and Design Management · doi:10.1080/17452007.2019.1651247 · Cited by 63
Self-citationGbadamosi, A.-Q., Oyedele, L. O., Olawale, O. A., & Abioye, S. (2020) · Progress in Disaster Science · doi:10.1016/j.pdisas.2020.100130 · Cited by 63
Self-citationLin, T.-H., Huang, Y.-H., & Putranto, A. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104483 · Cited by 61
Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems
Filippidou, F., & Moussiades, L. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-49161-1_7 · Cited by 60
The Glass House Effect: Big Data, The New Oil, and the Power of Analogy
Hirsch, D. D. (2014) · bepress Legal Repository · Cited by 55
A Conversational Agent in Support of Productivity and Wellbeing at Work
Kimani, E., Rowan, K., McDuff, D., Czerwinski, M. P., & Mark, G. J. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925488 · Cited by 54
Work-related stress, psychophysiological strain, and recovery among on-site construction personnel
Nwaogu, J. M., & Chan, A. P. C. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103629 · Cited by 54
Attribute-Aware Generative Design With Generative Adversarial Networks
Yuan, C., & Moghaddam, M. (2020) · IEEE Access · doi:10.1109/access.2020.3032280 · Cited by 54
Konanahalli, A., Marinelli, M., & Oyedele, L. O. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2959914 · Cited by 53
Self-citationSustaining architects' and engineers' motivation in design firms
Oyedele, L. O. (2010) · Engineering Construction & Architectural Management · doi:10.1108/09699981011024687 · Cited by 50
Self-citationA knowledge model-based BIM framework for automatic code-compliant quantity take-off
Liu, H., Cheng, J. C. P., Gan, V. J. L., & Zhou, S. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104024 · Cited by 49
Competency-based measures for designing out construction waste: task and contextual attributes
Ajayi, S. O., Oyedele, L. O., Kadiri, K. O., Akinadé, O. O., Bilal, M., Owolabi, H. A., & Alaka, H. (2016) · Engineering Construction & Architectural Management · doi:10.1108/ecam-06-2015-0095 · Cited by 47
Self-citationBig data platform for health and safety accident prediction
Ajayi, A., Oyedele, L. O., Delgado, J. M. D., Akanbi, L., Bilal, M., Akinadé, O. O., & Olawale, O. A. (2018) · World Journal of Science Technology and Sustainable Development · doi:10.1108/wjstsd-05-2018-0042 · Cited by 47
Self-citationAjayi, A., Oyedele, L. O., Akinadé, O. O., Bilal, M., Owolabi, H. A., Akanbi, L., & Delgado, J. M. D. (2020) · Safety Science · doi:10.1016/j.ssci.2020.104656 · Cited by 46
Self-citationTransfer learning-based query classification for intelligent building information spoken dialogue
Wang, N., Issa, R. R. A., & Anumba, C. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104403 · Cited by 45
Effects of Building Information Modeling During Construction
Fan, S.-L. (2014) · Tamkang University Institutional Repository (TKUIR) · doi:10.6180/jase.2014.17.2.06 · Cited by 45
A global taxonomic review and analysis of the development of BIM research between 2006 and 2017
Saka, A. B., & Chan, D. W. M. (2019) · Construction Innovation · doi:10.1108/ci-12-2018-0097 · Cited by 44
Self-citationBIMASR: Framework for Voice-Based BIM Information Retrieval
Shin, S., & Issa, R. R. A. (2021) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002138 · Cited by 41
Methodological approach of construction business failure prediction studies: a review
Alaka, H., Oyedele, L. O., Owolabi, H. A., Ajayi, S. O., Bilal, M., & Akinadé, O. O. (2016) · Construction Management and Economics · doi:10.1080/01446193.2016.1219037 · Cited by 37
Self-citationLuo, X., & Oyedele, L. O. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102934 · Cited by 37
Self-citationBIM data model requirements for asset monitoring and the circular economy
Delgado, J. M. D., & Oyedele, L. O. (2020) · Journal of Engineering Design and Technology · doi:10.1108/jedt-10-2019-0284 · Cited by 36
Self-citationChatbot for construction firms using scalable blockchain network
Adel, K., Elhakeem, A., & Marzouk, M. M. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104390 · Cited by 36
Neural Text-to-Speech Adaptation from Low Quality Public Recordings
Hu, Q., Marchi, E., Winarsky, D., Stylianou, Y., Naik, D. K., & Kajarekar, S. S. (2019) · ISCA Speech Synthesis Workshop (SSW) · doi:10.21437/ssw.2019-5 · Cited by 31
Big Data with deep learning for benchmarking profitability performance in project tendering
Bilal, M., & Oyedele, L. O. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113194 · Cited by 31
Self-citationVoice-Based Intelligent Virtual Agents (VIVA) to Support Construction Worker Productivity
Linares-Garcia, D. A., Roofigari‐Esfahan, N., Pratt, K., & Jeon, M. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104554 · Cited by 31
Kovačević, M. A., Nie, J., & Davidson, C. H. (2007) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(2008)22:1(3 · Cited by 29
Spoken dialogue BIM systems – an application of big data in construction
Motawa, I. A. (2017) · Facilities · doi:10.1108/f-01-2016-0001 · Cited by 29
Strategies for effective management of health and safety in confined site construction
Spillane, J. P., & Oyedele, L. O. (2013) · Construction Economics and Building · doi:10.5130/ajceb.v13i4.3619 · Cited by 28
Self-citationBig Data Analytics System for Costing Power Transmission Projects
Delgado, J. M. D., Oyedele, L. O., Bilal, M., Ajayi, A., Akanbi, L., & Akinadé, O. O. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001745 · Cited by 28
Self-citationA VR-based training program for conveyor belt safety
Lucas, J., Thabet, W. Y., & Worlikar, P. (2008) · Journal of Information Technology in Construction · Cited by 27
Developing NGO competencies in post-disaster reconstruction: a theoretical framework
von Meding, J. K., Oyedele, L. O., & Cleland, D. J. (2009) · Research Portal (Queen's University Belfast) · Cited by 25
Self-citationExamining architects' performance in Nigerian private and public sectors building projects
Oyedele, L. O., & Tham, K. W. (2005) · Engineering Construction & Architectural Management · doi:10.1108/09699980510576899 · Cited by 24
Self-citationA deep learning approach to concrete water-cement ratio prediction
Bello, S. A., Oyedele, L. O., Olaitan, O. K., Olonade, K. A., Olajumoke, A. M., Ajayi, A., Akanbi, L., Akinadé, O. O., Sanni, M. L., & Bello, A.-L. (2022) · Results in Materials · doi:10.1016/j.rinma.2022.100300 · Cited by 24
Self-citationTowards a Refined Conceptual Framework Model for a Smart and Sustainable City Assessment
Agbali, M., Trillo, C., Fernando, T., Oyedele, L., Ibrahim, I. A., & Olatunji, V. O. (2019) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc246665.2019.9071697 · Cited by 23
Self-citationRecurrent neural network language generation for spoken dialogue systems
Wen, T.-H., & Young, S. J. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.06.008 · Cited by 21
State-of-the-Art Review on the Applicability of AI Methods to Automated Construction Manufacturing
Hatami, M., Flood, I., Franz, B. W., & Zhang, X. (2019) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784482438.047 · Cited by 21
A question answering system for project management applications
Cheng, J., Kumar, B., & Law, K. H. (2002) · Advanced Engineering Informatics · doi:10.1016/s1474-0346(03)00014-4 · Cited by 18
Secondary Research Methods in the Built Environment
Pirzadeh, P., Lingard, H. C., & Blismas, N. G. (2021) · Routledge eBooks · doi:10.1201/9781003000532 · Cited by 18
Building Intelligent Virtual Agents as Conversational Partners in Digital Construction Sites
Eiris, R., & Gheisari, M. (2018) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784481264.020 · Cited by 17
Impact of project briefing clarity on construction project performance
Vahabi, A., Nasirzadeh, F., & Mills, A. J. (2020) · International Journal of Construction Management · doi:10.1080/15623599.2020.1802681 · Cited by 15
Increasing Naturalness and Flexibility in Spoken Dialogue Interaction
Marchi, E., Siniscalchi, S. M., Cumani, S., Salerno, V. M., & Li, H. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-9323-9 · Cited by 15
Special issue on deep learning for natural language processing
Wei, W., Wu, J., & Zhu, C. (2020) · Computing · doi:10.1007/s00607-019-00788-3 · Cited by 10
Beyond Conversational Artificial Intelligence
Campbell, M. (2020) · Computer · doi:10.1109/mc.2020.3024401 · Cited by 8
Model for Predicting Architect's Performance in Building Delivery Process
Oyedele, L. O., Tham, K. W., Jaiyeoba, B., & Fadeyi, M. O. (2003) · Journal of Engineering Design and Technology · doi:10.1108/eb060896 · Cited by 7
Self-citationAdvanced BIM Platform Based on the Spoken Dialogue for End-User
Shin, S., Lee, C., & Issa, R. R. A. (2020) · Lecture notes in civil engineering · doi:10.1007/978-3-030-51295-8_11 · Cited by 5
Framework for Automatic Speech Recognition-Based Building Information Retrieval from BIM Software
Shin, S., Lee, C., & Issa, R. R. A. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482865.105 · Cited by 5
The Architecture of a Natural Language Generation System
Reiter, E., & Dale, R. (2000) · Cambridge University Press eBooks · doi:10.1017/cbo9780511519857.004 · Cited by 1
MuCAI'21: 2nd ACM Multimedia Workshop on Multimodal Conversational AI
Magalhães, J., Hauptmann, A. G., Sousa, R., & Santiago, C. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3478580 · Cited by 1
Research works printed but not linked: 6
- Olawumi, 2021, Scientometric review and analysis: A case example of smart buildings and smart cities, 147
- Wang, N., R.R. Issa, and C.J. Anumba, A Framework for Intelligent Building Information Spoken Dialogue System (iBISDS), in EG-ICE 2021 Workshop on Intelligent Computing in Engineering. 2021.
- Sheldon, A., T. Dobbs, A. Fabbri, N. Gardner, M.H. Haeusler, C. Ramos, and Y. Zavoleas, Putting the AR In (AR)chitecture, in International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). 2019, Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong. p. 475-484. · DOI
- Zhoui, H., M.O. Wong, H. Ying, and S.H. Lee, A Framework of a Multi-User Voice-Driven BIM-Based Navigation System for Fire Emergency Response, in 26th International Workshop on Intelligent Computing in Engineering (EG-ICE 2019). 2019.
- Lin, J.-R., Z. Hu, and J. Zhang, BIM oriented intelligent data mining and representation, in 30th CIB W78 International Conference. 2013.
- Davila Delgado, 2019, Robotics and automated systems in construction: Understanding industry-specific challenges for adoption. Journal of Building, Engineering, 26
Cited by
Sorted by citations
Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024) · Sustainability · doi:10.3390/su16031166 · Cited by 302
GPT models in construction industry: Opportunities, limitations, and a use case validation
Saka, A., Taiwo, R., Saka, N., Salami, B. A., Ajayi, S., Akande, K., & Kazemi, H. (2024) · Developments in the Built Environment · doi:10.1016/j.dibe.2023.100300 · Cited by 202
Self-citationDing, C., Ke, J., Levine, M., Granderson, J., & Zhou, N. (2024) · Nature Communications · doi:10.1038/s41467-024-50088-4 · Cited by 187
Ghimire, P., Kim, K., & Acharya, M. (2024) · Buildings · doi:10.3390/buildings14010220 · Cited by 175
Dynamic prompt-based virtual assistant framework for BIM information search
Zheng, J., & Fischer, M. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105067 · Cited by 154
Metadata
- Authors
- Saka, A. B., Oyedele, L. O., Akanbi, L. A., Ganiyu, S. A., Chan, D. W. M., & Bello, S. A.
- Affiliations
- Leeds Beckett University (GB)
- University of the West of England (GB)
- Teesside University (GB)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Oyedele, Lukumon O.
- ORCID
- Venue
- Advanced Engineering Informatics
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 55
- Pages
- 101869
- ISSN
- 1474-0346 (ISSN-L)
- OpenAlex
- W4313457971
- Cited by
- 161
- References
- 140
- Citations by year
- 2026: 39 · 2025: 50 · 2024: 50 · 2023: 22
- FWCI
- 19.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Advanced Engineering Informatics (publishedVersion, cc-by)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by)
- Leeds Beckett Repository (Leeds Beckett University) (submittedVersion)
- Teesside University Research Portal (Teesside University) (submittedVersion, cc-by)
- UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
- WestminsterResearch (University of Westminster) (submittedVersion, cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/S031480/1)
- Topics
- Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- conversational AI, architecture, engineering and construction, natural language processing, speech recognition, chatbots, construction productivity, implementation barriers, future research directions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review