journal article·2025-09-17Open access

Integration of Industry 5.0 Technologies in the Concrete Industry: An Analysis of the Impact of AI-Based Virtual Assistants

Carlos Torregrosa Bonet, Francisco Antonio Lloret Abrisqueta, Antonio Guerrero González

Applied Sciences· Volume 15· Issue 18

Abstract

The construction industry, traditionally lagging behind in terms of digitalization, faces significant challenges in its transition to Industry 4.0, which is characterized by the use of advanced technologies such as artificial intelligence (AI), the Industrial Internet of Things (IIoT), and cloud computing. This article presents the development and implementation of an AI-based virtual assistant, designed to optimize the operation and maintenance of concrete production plants. The assistant helps reduce the margin of human error, improve operational efficiency, and facilitate continuous training for operators. These advancements foster a more collaborative and digitalized environment, while also generating environmental, economic, and social benefits: reduced material and energy waste, lower carbon footprint, increased workplace safety, and strengthened organizational resilience. The results show high accuracy in voice transcription (96%) and a 100% success rate in responding to technical queries, demonstrating its effectiveness as a support tool in industrial settings. Based on these findings, it is concluded that the incorporation of AI-based virtual assistants promotes a more sustainable and responsible production model, aligned with the Sustainable Development Goals of the 2030 Agenda, and anticipates the principles of Industry 5.0 by promoting symbiotic collaboration between humans and technology. This innovation represents a key advancement in transforming the concrete industry, contributing to productivity, environmental sustainability, and workplace well-being in the sector.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: BIM and Construction Integration · Digital Transformation in Industry

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

FRUMECAR S.L., 30820 Alcantarilla, Spain

Department of Automation, Electrical Engineering and Electronic Technology, Polytechnic University of Cartagena, 30203 Cartagena, Spain

Funding

Ministerio de Ciencia, Innovación y Universidades

References (63)

Show source-provided references
1. Gamil, Y., and Cwirzen, A. (2022). Digital Transformation of Concrete Technology—A Review. Front. Built Environ., 8.10.3389/fbuil.2022.835236 ↗
2. Alshboul, O., Al Mamlook, R.E., Shehadeh, A., and Munir, T. (2024). Empirical exploration of predictive maintenance in concrete manufacturing: Harnessing machine learning for enhanced equipment reliability in construction project management. Comput. Ind. Eng., 190.10.1016/j.cie.2024.110046 ↗
3. Abioye, S.O., Oyedele, L.O., Akanbi, L., Ajayi, A., Delgado, J.M.D., Bilal, M., Akinade, O.O., and Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. J. Build. Eng., 44.10.1016/j.jobe.2021.103299 ↗
4. Cyber-Physical Systems in the Context of Industry 4.0: A Review, Categorization and Outlook10.1007/s10796-022-10252-x ↗
5. Internet of things for smart factories in industry 4.0, a review10.1016/j.iotcps.2023.04.006 ↗
6. Fan, J., Chen, L., and Chen, K. (2024). Digitalizing Industrialized Construction Projects: Status Quo and Future Development. Appl. Sci., 14.10.3390/app14135456 ↗
7. Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.010.1016/j.matt.2023.04.016 ↗
8. Pereira, R., Lima, C., Pinto, T., and Reis, A. (2023). Virtual Assistants in Industry 4.0: A Systematic Literature Review. Electronics, 12.10.3390/electronics12194096 ↗
9. Applications of augmented reality for construction productivity improvement: A systematic review10.1108/SASBE-06-2022-0128 ↗
10. Use of the Internet of Things in the Construction Industry and Facility Management: Usage Examples Overview10.1016/j.procs.2023.01.460 ↗
11. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach10.1109/TII.2014.2349359 ↗
12. Future of industry 5.0 in society: Human-centric solutions, challenges and prospective research areas10.1186/s13677-022-00314-5 ↗
13. Alves, J., Lima, T.M., and Gaspar, P.D. (2023). Is Industry 5.0 a Human-Centred Approach? A Systematic Review. Processes, 11.10.3390/pr11010193 ↗
14. Pizoń, J., and Gola, A. (2023). Human–Machine Relationship—Perspective and Future Roadmap for Industry 5.0 Solutions. Machines, 11.10.3390/machines11020203 ↗
15. Tóth, A., Nagy, L., Kennedy, R., Bohuš, B., Abonyi, J., and Ruppert, T. (2023). The human-centric Industry 5.0 collaboration architecture. MethodsX, 11.10.1016/j.mex.2023.102260 ↗
16. Li, C., Park, J., Kim, H., and Chrysostomou, D. (2021, January 8–11). How can I help you? An Intelligent Virtual Assistant for Industrial Robots. Proceedings of the Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, Boulder, CO, USA. HRI ’21 Companion.10.1145/3434074.3447163 ↗
17. Baduge, S.K., Thilakarathna, S., Perera, J.S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., and Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Autom. Constr., 141.10.1016/j.autcon.2022.104440 ↗
19. Interoperability for Industrial Cyber-Physical Systems: An Approach for Legacy Systems10.1109/TII.2017.2740434 ↗
20. Data-driven smart manufacturing10.1016/j.jmsy.2018.01.006 ↗
21. Yasutomi, A.Y., Mori, H., and Ogata, T. (June, January 30). A Peg-in-hole Task Strategy for Holes in Concrete. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China.10.1109/ICRA48506.2021.9561370 ↗
22. Redefining Human-Machine Collaboration: Industry 5.0 to Improve Safety and Efficiency10.1109/TLA.2025.11072501 ↗
23. Urrea, C., and Kern, J. (2025). Recent Advances and Challenges in Industrial Robotics: A Systematic Review of Technological Trends and Emerging Applications. Processes, 13.10.3390/pr13030832 ↗
24. Voice User Interface based control for Industrial machine tools10.1016/j.procir.2023.09.238 ↗
25. AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions10.1109/ACCESS.2022.3201144 ↗
26. Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., and Romero, D. (2023, January 17–21). Fostering Human-AI Collaboration with Digital Intelligent Assistance in Manufacturing SMEs. Proceedings of the Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures, Trondheim, Norway.10.1007/978-3-031-43688-8 ↗
27. Autonomous concrete crack detection using deep fully convolutional neural network10.1016/j.autcon.2018.11.028 ↗
28. Gonzalez-Moret, V., Zhang, J., García de Soto, B., and Brilakis, I. (2024, January 3–5). Computer Vision as Key to an Automated Concrete Production Control. Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC 2024), Lille, France.
29. Lockey, S., Gillespie, N., Holm, D., and Someh, I.A. (2021, January 5). A Review of Trust in Artificial Intelligence: Challenges, Vulnerabilities and Future Directions. Proceedings of the Hawaii International Conference on System Sciences, Kauai, HI, USA.10.24251/HICSS.2021.664 ↗
30. Review of Interfaces for Industrial Human-Robot Interaction10.1007/s43154-020-00005-6 ↗
31. Implementing Virtual Reality technology for safety training in the precast/ prestressed concrete industry10.1016/j.apergo.2020.103286 ↗
32. Systematic Literature Review on Augmented Reality-Based Maintenance Applications in Manufacturing Centered on Operator Needs10.1007/s40684-022-00444-w ↗
33. Gärtler, M., and Schmidt, B. (2021, January 5). Practical Challenges of Virtual Assistants and Voice Interfaces in Industrial Applications. Proceedings of the Hawaii International Conference on System Sciences, Kauai, HI, USA.10.24251/HICSS.2021.494 ↗
34. Schmidt, B., Borrison, R., Cohen, A., Dix, M., Gärtler, M., Hollender, M., Klöpper, B., Maczey, S., and Siddharthan, S. (2018, January 8–12). Industrial Virtual Assistants: Challenges and Opportunities. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore. UbiComp ’18.10.1145/3267305.3274131 ↗
35. Challenges of Human-Centered Manufacturing in the Aspect of Industry 5.0 Assumptions10.1016/j.ifacol.2023.10.1562 ↗
36. Bolton, T., Dargahi, T., Belguith, S., Al-Rakhami, M.S., and Sodhro, A.H. (2021). On the Security and Privacy Challenges of Virtual Assistants. Sensors, 21.10.3390/s21072312 ↗
37. Enhancing conversational agents for successful operation: A multi-perspective evaluation approach for continuous improvement10.1007/s12525-023-00662-3 ↗
38. Edge Computing in IoT-Based Manufacturing10.1109/MCOM.2018.1701231 ↗
39. Salhaoui, M., Guerrero-González, A., Arioua, M., Ortiz, F.J., El Oualkadi, A., and Torregrosa, C.L. (2019). Smart Industrial IoT Monitoring and Control System Based on UAV and Cloud Computing Applied to a Concrete Plant. Sensors, 19.10.3390/s19153316 ↗
40. Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph10.1016/j.engappai.2023.106798 ↗
41. Digital twin-based industrial cloud robotics: Framework, control approach and implementation10.1016/j.jmsy.2020.07.013 ↗
42. Edge Computing and Digital Twin Based Smart Manufacturing10.1016/j.ifacol.2021.08.098 ↗
43. Una revisión sobre el rol de la inteligencia artificial en la industria de la construcción
44. A predictive maintenance approach in manufacturing systems via AI-based early failure detection10.1016/j.mfglet.2023.08.125 ↗
45. Ge, X., Goodwin, R.T., Gregory, J.R., Kirchain, R.E., Maria, J., and Varshney, L.R. (2019). Accelerated Discovery of Sustainable Building Materials. arXiv.
46. Probabilistic selection and design of concrete using machine learning10.1017/dce.2023.5 ↗
47. Li, Z., and Radlińska, A. (2022). Artificial Intelligence in Concrete Materials: A Scientometric View. Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure, CRC Press.
48. Técnica de inteligencia artificial para la selección de concreto en construcciones residenciales. Una revisión sistemática10.59282/reincisol.V3(5)1490-1514 ↗
49. Machine learning in concrete science: Applications, challenges, and best practices10.1038/s41524-022-00810-x ↗
50. Manzoor, B., Othman, I., Durdyev, S., Ismail, S., and Wahab, M.H. (2021). Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review. Appl. Syst. Innov., 4.10.3390/asi4030052 ↗
51. Industry 5.0 and sustainability: An overview of emerging trends and challenges for a green future10.1016/j.igd.2024.100173 ↗
52. Sustainability perspective in fib Model Code 2020: Contribution of concrete structures to sustainability and the Sustainable Development Goals10.1002/suco.202300022 ↗
53. AI-Assisted optimisation of green concrete mixes incorporating recycled concrete aggregates10.1016/j.conbuildmat.2023.131851 ↗
54. Wang, S., Xia, P., Chen, K., Gong, F., Wang, H., Wang, Q., Zhao, Y., and Jin, W. (2023). Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review. J. Build. Eng., 80.10.1016/j.jobe.2023.108065 ↗
55. Joshi, D.A., Menon, R., Jain, R., and Kulkarni, A. (2023). Deep learning based concrete compressive strength prediction model with hybrid meta-heuristic approach. Expert Syst. Appl., 233.10.1016/j.eswa.2023.120925 ↗
56. Mohammed Naved, M.A., and Ahmad, T. (2024). Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization. Cogent Eng., 11.10.1080/23311916.2023.2297491 ↗
57. Glavaš, H., Hadzima-Nyarko, M., Ademović, N., and Hanák, T. (2024, January 12). Improving Concrete Plant Operations and Maintenance with Digital Twin Technology. Proceedings of the 33rd International Conference on Organization and Technology of Maintenance (OTO 2024), Osijek, Croatia.10.1007/978-3-031-80597-4 ↗
58. Shahrokhishahraki, M., Malekpour, M., Mirvalad, S., and Faraone, G. (2024). Machine learning predictions for optimal cement content in sustainable concrete constructions. J. Build. Eng., 82.10.1016/j.jobe.2023.108160 ↗
59. Liu, Q., Ma, Y., Chen, L., Pedrycz, W., Skibniewski, M.J., and Chen, Z.S. (2024). Artificial intelligence for production, operations and logistics management in modular construction industry: A systematic literature review. Inf. Fusion, 109.10.1016/j.inffus.2024.102423 ↗
60. Skatulla, S., and Beushausen, H. (2022, January 26–28). Image Segmentation on Concrete Damage for Augmented Reality Supported Inspection Tasks. Proceedings of the Advances in Information Technology in Civil and Building Engineering, Cape Town, South Africa.
61. A review of digital assistants in production and logistics: Applications, benefits, and challenges10.1080/00207543.2024.2330631 ↗
62. A Computerized Operator Support System Prototype10.1177/1541931214581397 ↗
63. Real-time assistance to manual assembly through depth camera and visual feedback10.1016/j.procir.2019.03.303 ↗