Leveraging AI-Driven Accounting for Sustainable Safety of Critical Infrastructure Projects

Authors

  • Kenneth O. Ogirri American Electrical Power – Tulsa, Oklahoma, USA

DOI:

https://doi.org/10.5281/zenodo.15139730

Keywords:

AI-driven accounting, Sustainable safety, Critical infrastructure projects, Optimization, Automation

Abstract

Artificial intelligence (AI) is rapidly optimizing and revolutionizing various spheres of life. Its presence in and impact on accounting and project management is well known. This study avers that AI-driven accounting optimizes and revolutionizes the sustainable safety of critical infrastructure projects. To prove this stance, data are drawn from survey questionnaire, observation, introspection, and library and internet print materials for evidence. Survey design and mixed method alongside their associated plausible descriptive and statistical tools are employed. The results of the analysis show majority of the respondents, and extant studies confirming that AI-driven accounting allows for the integration of the cutting edge technologies into accounting activities; efficient scenario planning and supportive decision-making; automated and optimized sustainability metrics and reporting; predictive analysis and automated management of risks; and transparent stakeholder engagement; improved analysis and reporting of data; automation of regulation and reporting. The study concludes that AI-driven accounting increases the contributions of accounting to the sustainable safety of critical infrastructure projects to a large extent. Stakeholders are charged to ensure increased adoption of AI techniques for accounting and project management, towards attaining sustainable safety of critical infrastructure projects.

References

Akinloye, A., Anwansedo, S., & Akinwande, O. T. (2024). AI-driven threat detection and response systems for secure national infrastructure networks: a comprehensive review. International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), XIII (VII), 82-92. DOI: 10.51583/IJLTEMAS

Akinola, A. P. (2024). Leveraging cost-effective AI and smart technologies for rapid infrastructural development in USA. African Journal of Advances in Science and Technology Research, 15(1), 59-71. https://doi.org/10.62154/rktd4f30

Akinola, A. P., Thuraka, B., & Okpeseyi, S. B. A. (2024). Achieving housing affordability in the U.S. through sustained use of AI and robotic process automation for prefabricated modular construction. African Journal of Advances in Science and Technology Research, 15(1), 122-134. https://doi.org/10.62154/53t99n63

Binhammad, M., Alqaydi, S., Othman, A., & Abuljadayel, L. H. (2024). The role of AI in cyber security: Safeguarding digital identity. Journal of Information Security, 15, 245-278. https://doi.org/10.4236/jis.2024.152015

Carvalho, T. P., et al. (2019). A review of machine learning and Internet of Things (IoT) in smart maintenance. Computers in Industry, 107, 100-117.

Kalnawat, A., Dhabliya, D., Vydehi, K., Dhablia, A., & Kumar, S. D. (2024). Safeguarding critical infrastructures: Machine learning in cybersecurity. (ICECS'24) E3S Web of Conferences, 491, 02025. https://doi.org/10.1051/e3sconf/202449102025

Na, J., et al. (2020). Predictive maintenance for water management using artificial intelligence: A systematic review. Journal of Environmental Management, 258, 110038.

National Strategy for Artificial Intelligence Bangladesh (2020). Information and communication technology division government of the People’s Republic of Bangladesh.

Odunayo, A. (2024). Leveraging artificial intelligence (AI) for the maintenance of science laboratory equipment. African Journal of Advances in Science and Technology Research, 16(1), 131-148. DOI: https://doi.org/10.62154/ajastr.2024.016.010453

Ojo, B., Ogborigbo, J. C., & Okafor, M. O. (2024). Innovative solutions for critical infrastructure resilience against cyber-physical attacks. World Journal of Advanced Research and Reviews, 22(03), 1651–1674. DOI: https://doi.org/10.30574/wjarr.2024.22.3.1921

Okusi, O. (2024). Leveraging AI and machine learning for the protection of critical national infrastructure. Asian Journal of Research in Computer Science, 17(10), 1-11, no.AJRCOS.124252. DOI: https://doi.org/10.9734/ajrcos/2024/v17i10505

Olejnik, S., et al. (2020). The importance of AI-based maintenance in critical infrastructure. Sustainability, 12(23), 10092.

Pasupuleti, V. (2023, Dec.). Ascertaining the extent to which AI is revolutionizing architecture: Exploratory survey. Journal of Innovations in Engineering & Tech. Res. (JIETR2023), 2(2), 65-77.

Pasupuleti, V., Kodete, C. S., Thuraka, B., & Sangaraju, V. V. (2024). Impact of AI on architecture: An exploratory thematic analysis. African Journal of Advances in Science and Technology Research, 16(1), 117-130. DOI: https://doi.org/10.62154/ajastr.2024.016.010453

Rashid A. B., Ashfakul, K. K., Hassan, A., & Mehedy, H. B. (2023). Artificial intelligence in the military: an overview of the capabilities, applications, and challenges. International Journal of Intelligent Systems, 2023, 1–31. https://doi.org/10.1155/2023/8676366

Ren, X., et al. (2021). Predictive maintenance using deep learning: A case study in wind energy. IEEE Transactions on Industrial Informatics, 17(8), 5486-5495.

Salako, B. A. (2023). Strategies for implementation of construction management planning tools for effective project delivery in Abuja, Nigeria. Mtech thesis submitted to the Postgraduate School, Federal University of Technology, Minna, Nigeria.

Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics. AAAI Fall Symposium: AI for Prognostics, 1-8.

Shawana, T. A. (2022). Carbon emissions as threats to environmental sustainability: Exploring conventional and technology-based remedies. African Journal of Environmental Sciences and Renewable Energy, 8 (1). https://publications.afropolitanjournals.com/index.php/ajesre/article/view/736

Tabassi, A. A., Argyropoulou, M., Roufechaei, K. M. & Argyropoulou, R. (2016). Leadership behavior of project managers in sustainable construction projects. Procedia Computer Science, 100, 724–730.

Thapaliya, S., & Bokani, A. (2024). Leveraging artificial intelligence for enhanced cybersecurity: Insights and innovations. Sadgamaya, vol.1, iss.1, 46-53.

Thuraka, B., Pasupuleti, V., Malisetty, S., & Ogirri, K. O. (2024). Leveraging artificial intelligence and strategic management for success in inter/national projects in US and beyond. Journal of Engineering Research and Reports, 26 (8), 49-59. https://doi.org/10.9734/jerr/2024/v26i81228.

Volk, M. (2024). A safer future: Leveraging the AI power to improve the cybersecurity in critical infrastructures. ELEKTROTEHNIŠKI VESTNIK, 91(3), 73-94.

Wiese, T. L. (2024). Predictive maintenance using artificial intelligence in critical infrastructure: A decision-making framework. International Journal of Engineering, Business and Management (IJEBM), 8(4), 1-4. DOI: https://dx.doi.org/10.22161/ijebm.8.4.1

Yu, C. (2024). “AI as critical infrastructure: Safeguarding national security in the age of artificial intelligence,” Preprint, pp. 1-16. DOI: http:// doi.org/10.31219/osf.io/u4kdg

Zhang, Q., et al. (2019). Deep learning for predictive maintenance of industrial equipment: A review. IEEE Access, 7, 62624-62634.

Zonta, T., et al. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889.

Downloads

Published

2025-04-01