AI-DRIVEN BLOCKCHAIN SOLUTIONS FOR ENHANCING DECISION-MAKING IN COMPLEX ENGINEERING PROJECTS

Abstract

The current state of technology is causing a surge in high-end technical items, which in turn are causing a wide range of client needs. As a result, engineering system design projects are expected to become more complicated. Consequently, engineers working on large-scale design projects need designers adept at collaborating across disciplines and solving complex challenges. The inefficiency of team communication and the execution of complicated projects is directly impacted when firm managers appoint designers with multidisciplinary knowledge to such projects. This is because designers need more excellent knowledge in each area, which will lead to communication issues. Futuristic technologies such as AI and blockchain have the potential to optimize and greatly improve procedures in the ever-changing realm of complicated engineering projects. This study delves into AI-driven Blockchain Solutions for Enhancing Decision-Making in Complex Engineering Projects (AI-BC-CEP) by examining the combined impact on efficiency, cost reduction, and data integrity. The particular artificial intelligence models used include Reinforcement Learning (RL) for decision-making optimization and machine learning (ML) for predictive maintenance, together with the steps taken to train and test these models using real-world or simulated data. The technological setup and smart contract systems used to record project decisions and data and the integration of blockchain technology guarantee secure and transparent communication across multidisciplinary teams. Blockchain technology generates a decentralized, immutable ledger of all monetary transactions to lessen the possibility of fraud and errors. Analytics powered by artificial intelligence may help with predictive maintenance, demand forecasting, and decision-making, which can improve operational efficiency and resource allocation. Some of the limitations and challenges mentioned in the article are privacy, interoperability, and scalability. This research delves deeply into whether blockchain technology and artificial intelligence collaborate to take on the complexities of modern engineering tasks. The experimental results demonstrate that the proposed AI-BC-CEP model increases the cost reduction ratio by 21.2%, predictive maintenance ratio by 95.6%, decision-making ratio by 85.3%, and demand forecast by 97.4% compared to other existing models.