Abstract: The pre-design phase of hospital projects plays a crucial role in ensuring the success of the overall project. However, it is often challenging to gather and analyze all the necessary information to make informed decisions during this phase. This paper proposes a methodological approach that utilizes artificial intelligence techniques to enhance the architectural pre-design phase of hospital projects.

The proposed approach aims to improve the efficiency and effectiveness of the pre-design phase by automating various tasks and providing valuable insights to architects and project stakeholders. The approach involves the development of an intelligent system that utilizes machine learning algorithms to analyze large amounts of data related to hospital design and construction.

The system will be trained on historical data from previous hospital projects, including architectural plans, construction schedules, and cost estimates. It will also incorporate real-time data from ongoing projects to continuously update its knowledge base. By analyzing this data, the system will be able to identify patterns, trends, and potential issues that can impact the design and construction process.

The intelligent system will also have the capability to generate design alternatives based on predefined criteria and constraints. Architects and project stakeholders can input their requirements, and the system will generate multiple design options that meet these criteria. This will significantly reduce the time and effort required to develop design alternatives manually.

Furthermore, the system will provide recommendations for optimizing the design based on factors such as cost, energy efficiency, and patient flow. It will consider various design parameters, such as room layout, equipment placement, and material selection, to suggest improvements that can enhance the functionality and performance of the hospital.

To validate the effectiveness of the proposed approach, a case study will be conducted using real-world hospital projects. The results will be compared with traditional pre-design methods to assess the benefits and limitations of the intelligent system.

In conclusion, this paper presents a methodological approach that utilizes artificial intelligence techniques to enhance the architectural pre-design phase of hospital projects. The proposed intelligent system has the potential to improve decision-making, increase efficiency, and optimize the design process, ultimately leading to better outcomes for hospital projects.