Automated Machine Learning in the smart construction era:Significance and accessibility for industrial classification and regression tasks

TL;DR

This paper demonstrates how AutoML can be effectively applied to the construction industry, enabling professionals without data science expertise to develop ML models for project management and decision-making.

Contribution

It verifies the feasibility of AutoML in industrial construction datasets and provides a real-world case study to illustrate its accessibility and effectiveness.

Findings

AutoML outperforms traditional ML models in construction tasks.

AutoML enables construction professionals to create ML models without data science expertise.

Application of AutoML improves decision-making and project outcomes in construction.

Abstract

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