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

翻译：本文探讨了自动化机器学习技术在建筑业——这一全球经济关键领域中的应用。传统机器学习模型构建方法复杂、耗时、依赖数据科学专业知识且成本高昂。自动化机器学习展现出自动化机器学习模型构建的诸多环节并生成性能更优模型的潜力。本文旨在验证将自动化机器学习应用于智能建造领域工业数据集的可行性，并通过具体案例研究证明其有效性。除常规的数据集准备、模型训练与评估步骤外，本文重点关注工业建筑数据集特有的两类数据挑战。通过一个真实场景中的建筑项目类型预测案例，展示了自动化机器学习的可及性。借助自动化机器学习，不具备数据科学背景的建筑业专业人员如今可利用软件将工业数据处理为辅助项目管理的机器学习模型。本文的研究成果有望弥合数据密集型智能建造实践与新兴自动化机器学习领域之间的鸿沟，促进其应用于优化决策、项目成效与工作效率。