  This paper explores the application of automated machine learning (AutoML)
techniques to the construction industry, a sector vital to the global economy.
Traditional ML model construction methods were complex, time-consuming, reliant
on data science expertise, and expensive. AutoML shows the potential to
automate many tasks in ML construction and to create outperformed ML models.
This paper aims to verify the feasibility of applying AutoML to industrial
datasets for the smart construction domain, with a specific case study
demonstrating its effectiveness. Two data challenges that were unique to
industrial construction datasets are focused on, in addition to the normal
steps of dataset preparation, model training, and evaluation. A real-world
application case of construction project type prediction is provided to
illustrate the accessibility of AutoML. By leveraging AutoML, construction
professionals without data science expertise can now utilize software to
process industrial data into ML models that assist in project management. The
findings in this paper may bridge the gap between data-intensive smart
construction practices and the emerging field of AutoML, encouraging its
adoption for improved decision-making, project outcomes, and efficiency
