Optimizing Construction Project Scheduling Using Machine Learning and AI-Based Predictive Analytics for Cost and Time Efficiency

The paper examines how Hybrid Machine Learning and Real-Time Data Integration can be used to optimize the construction project schedule using the Google Cloud AI Platform. By using the high-end machine learning methods, such as neural networks to predict analytics and reinforcement learning to schedule adaptively, the research will become cost and time-efficient. Data on construction sites, including weather conditions, labor performance, and availability of materials in real-time, are also included to allow the construction model to be updated continuously and the decision-making to be modified. The proposed approach is better than the conventional methods of scheduling, which show that project delays and cost overruns will be significantly reduced. The approach features resource management that is optimized by combining resource allocation with robust machine learning algorithms and dynamism in a real-time fashion to minimize time discrepancies and inefficiency. The ability to scale the Google Cloud AI Platform also increases the capacity of the system, which is applicable in a wide range of many and large-scale construction projects. This approach can be defined as an important innovation in AI-based project management.