Risk Associated with Management of Engineering Projects Using Intelligent Based Control System

Some abandoned Engineering projects observed in some parts of the country this present era has arisen as a result of these factors Scope Creep, Schedule Delays, Budget Overruns, Safety Hazards, Technical Challenges and Additional Risk Factors. This is surmounted by introducing reducing risk associated with management of engineering projects using intelligent based control system. To achieve this, it was done in this manner, characterizing and establishing the causes of the risk associated with management of engineering projects, designing a conventional SIMULINK model for the risk associated with management of engineering projects, designing management rule base that will reduce the percentage of the risk associated with management of engineering projects, training ANN in the management rule base to enhance the efficacy of reducing the percentage of the risk associated with management of engineering projects, developing an algorithm that will implement the process, designing a SIMULINK model for reducing risk associated with management of engineering projects using intelligent based control system and validating and justifying the percentage improvement in the reduction of risk associated with management of engineering projects with and without intelligent based control system. The results obtained are the conventional Scope Creep that caused risk associated with management of engineering project was12%. On the other hand, when an intelligent based control system was introduced in the system, the percentage of risk observed in management of engineering project was reduced to10.4%, the conventional Safety Hazards that caused high risk associated with management of engineering projects was7%. However, when an intelligent based control system was integrated in the system the risk associated with management of engineering projects reduced to6.07% and the conventional Additional Risk Factors that caused risk associated with management of engineering projects was32% while when an intelligent based control system was imbibed in the system, it wittingly reduced to27.75%. Finally, with these results obtained, it shows that the percentage improvement in the reduction of risk associated with management of engineering projects when an intelligent based control system was integrated in the system over the conventional competitor was 4.25%.

Reducing, risk, associated, management, engineering, project, intelligent, based, control, system

1. Project Management Institute. A Guide to the Project Management Body of Knowledge (PMBOK Guide). 6th Edition, Project Management Institute, 2017. [Google Scholar] [Crossref]

2. Chapman, C. Risk Assessment and Management.Routledge, 2008. [Google Scholar] [Crossref]

3. Baccarini, David P. “The Body of Knowledge for Risk Management in Projects.” Project Management Journal, vol. 41, no. 2, 2010, pp. 5–18. [Google Scholar] [Crossref]

4. Liu, S., & Wang, Y. (2012). A fuzzy logic based risk assessment model for construction projects. Expert Systems with Applications, 39(2), 1803-1812. [Google Scholar] [Crossref]

5. El-Gohary, A. M., & El-Sawy, A. (2014).Applying machine learning for risk assessment in construction projects. Automation in Construction, 41, 102-111. [Google Scholar] [Crossref]

6. Tabrizi, B. H., Moslemian, E., &Nasirzadeh, A. (2011).Risk assessment using artificial neural networks in construction projects. Neural Computing and Applications, 20(3), 403-411. [Google Scholar] [Crossref]

7. Wang, Y., Zhang, Y., & Liu, S. (2016). A hybrid approach for risk assessment of construction projects based on fuzzy logic and neural networks. Automation in Construction, 62, 1-11. [Google Scholar] [Crossref]

8. Amini, M. H., Tavakkoli, M., &Rezania, M. (2014).Applying artificial neural networks for risk assessment in construction projects. International Journal of Project Management, 32(6), 1028-1040. [Google Scholar] [Crossref]

9. Lee, I., & Kim, J. S. (2016).A risk assessment model using machine learning for construction projects. Automation in Construction, 70, 17-27. [Google Scholar] [Crossref]

10. Li, H., Liu, G., & Wang, J. (2018). A cloud-based framework for risk management in construction projects using BIM and machine learning. Automation in Construction, 87, 1-12. [Google Scholar] [Crossref]

11. Bendeković, J., &Šarčević, M. (2018). Machine learning approaches for risk assessment in complex engineering projects. International Journal of Project Management, 36(4), 590-6 [Google Scholar] [Crossref]

• Enhancing Formation Control of Multi Agent Systems Using Ann Based Technique
• Improving Sliding Mode Control with Chattering Reduction using Fuzzy Based Technique
• Cooking Quality, Fasting Blood Glucose, Glycemic Index and Load of High–Fiber Noodles Made from Wheat, Tiger Nut Residue and Cassava Flour Blends
• Matrix Rhythm Therapy Versus Interferential Therapy Combined with Lumbar Stabilization Exercises in Chronic Non-Specific Low Back Pain: A Randomized Comparative Trial
• Formulation and Sensory Evaluation of Functional Cake Prepared from Sweet Potato Powder