OPTIMIZING THE REQUIREMENT OF MACHINE LEARNING TECHNIQUES FOR INFRASTRUCTURE CONSTRUCTING

Infrastructure construction is a complex and time-consuming process that involves various stages, such as planning, design, construction, and maintenance. The use of machine learning techniques in infrastructure construction has gained significant attention in recent years, due to its potential to optimize various stages of the construction process and improve efficiency. However, the use of machine learning techniques requires a thorough understanding of the requirements and limitations of these techniques. This paper aims to explore and optimize the requirements of machine learning techniques for infrastructure constructing. The paper first provides an overview of the challenges faced in infrastructure construction and the potential benefits of using machine learning techniques. It then discusses the different types of machine learning techniques commonly used in infrastructure construction, including supervised learning, unsupervised learning, and reinforcement learning. The paper also analyses the data requirements for machine learning techniques, including the type, quality, and quantity of data needed to train and validate models. Additionally, the techniques in infrastructure construction, including the computational power needed for training and deploying models. Furthermore, the paper discusses the challenges and limitations of using machine learning techniques in infrastructure construction, such as the interpretability and explain ability of models, the potential biases in data, and the ethical concerns associated with the use of autonomous systems. The case studies where machine learning techniques have been successfully used in infrastructure construction projects, such as predicting road conditions, optimizing construction schedules, and detecting defects in buildings. The paper concludes by summarizing the key findings and highlighting future research directions in this area. This paper provides a comprehensive overview of the requirements and challenges of using machine learning techniques in infrastructure construction and these techniques can be optimized for better performance and efficiency