The Impact of Smartpm's Ai-Driven Analytics on Predicting and Mitigating Schedule Delays in Complex Infrastructure Projects

Rinkesh Gajera

doi:10.32628/IJSRSET24115101

Authors

Rinkesh Gajera Independent Researcher, USA Author

DOI:

https://doi.org/10.32628/IJSRSET24115101

Keywords:

Artificial Intelligence, Machine Learning, SmartPM

Abstract

This study explores the extent to which SmartPM's AI-driven analytics can predict and mitigate the potential impending schedule delays on complex infrastructure projects. From this study, the exhaustive research based on case study and associated data asserts that AI-driven predictive analytics can significantly leverage the outcomes of projects by bringing the identified potential delays to notice before such occurrences and provide actionable insight regarding the possible strategies to mitigate it.

Downloads

Download data is not yet available.

References

Suarez, E.G., Kennedy, J., Pugh, S.J. and Ishiyama, E.M., 2024. Fouling Management at TotalEnergies through Use of HTRI SMARTPM™: Case Study of a Project Proposal for Cleaning Schedule Optimization. Heat Transfer Engineering, 45(15), pp.1357-1368. DOI: https://doi.org/10.1080/01457632.2023.2255809

Ishiyama, E.M., Juhel, C., Aquino, B., Hagi, H., Pugh, S.J., Gomez Suarez, E., Kennedy, J. and Zettler, H.U., 2022. Advanced fouling management through use of HTRI SmartPM: case studies from total refinery CDU preheat trains. Heat Transfer Engineering, 43(15-16), pp.1365-1377. DOI: https://doi.org/10.1080/01457632.2021.1963542

Okafor, K.C., Ndinechi, M.C. and Misra, S., 2022. Cyber‐physical network architecture for data stream provisioning in complex ecosystems. Transactions on Emerging Telecommunications Technologies, 33(4), p.e4407. DOI: https://doi.org/10.1002/ett.4407

Lozano-Santamaria, F. and Macchietto, S., 2022. Assessment of a Dynamic Model for the Optimization of Refinery Preheat Trains under Fouling. Heat Transfer Engineering, 43(15-16), pp.1349-1364. DOI: https://doi.org/10.1080/01457632.2021.1963537

Chambon, A., Anxionnaz-Minvielle, Z., Cwicklinski, G., Guintrand, N., Buffet, A. and Vinet, B., 2020. Shell-and-tube heat exchanger geometry modification: An efficient way to mitigate fouling. Heat Transfer Engineering, 41(2), pp.170-177. DOI: https://doi.org/10.1080/01457632.2018.1522093

Ishiyama, E.M. and Pugh, S.J., 2020. Effect of flow distribution in parallel heat exchanger networks: Use of thermo-hydraulic channeling model in refinery operation. Heat Transfer Engineering.

Ishiyama, E.M., Falkeman, E., Wilson, D.I. and Pugh, S.J., 2020. Quantifying implications of deposit aging from crude refinery preheat train data. Heat Transfer Engineering. DOI: https://doi.org/10.1080/01457632.2018.1522072

Woschank, M., Rauch, E. and Zsifkovits, H., 2020. A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability, 12(9), p.3760. DOI: https://doi.org/10.3390/su12093760

Chergui, H., Blanco, L., Garrido, L.A., Ramantas, K., Kukliński, S., Ksentini, A. and Verikoukis, C., 2021. Zero-touch AI-driven distributed management for energy-efficient 6G massive network slicing. Ieee Network, 35(6), pp.43-49. DOI: https://doi.org/10.1109/MNET.111.2100322

Devan, M., Shanmugam, L. and Tomar, M., 2021. AI-Powered Data Migration Strategies for Cloud Environments: Techniques, Frameworks, and Real-World Applications. Australian Journal of Machine Learning Research & Applications, 1(2), pp.79-111.

Firouzi, F., Farahani, B. and Marinšek, A., 2022. The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT). Information Systems, 107, p.101840. DOI: https://doi.org/10.1016/j.is.2021.101840

Zulaikha, S., Mohamed, H., Kurniawati, M., Rusgianto, S. and Rusmita, S.A., 2020. Customer predictive analytics using artificial intelligence. The Singapore Economic Review, pp.1-12. DOI: https://doi.org/10.1142/S0217590820480021