Cost overruns in maintenance projects should be monitored and effectively managed by construction professionals using proactive systems. To establish more effective proactive systems for addressing cost overruns in maintenance projects, this paper presents a topological approach for machine learning-based prediction, integrated into various machine learning models to enhance the feature selection process. Project data from 1807 public schools renovated between 2016 and 2022 was collected to test the proposed mathematical method. The results indicate that the proposed method demonstrates superior performance in 6 out of 7 machine learning algorithms and hybrid models, achieving higher accuracy. This method will enable construction professionals to establish and achieve more efficient proactive systems for managing cost problems in maintenance projects. In addition, this paper will open new doors for developing effective machine-learning models without using optimization methods for other construction issues such as time, quality, or safety.

中文翻译：

---

使用粗略的拓扑方法预测公立学校建筑的维护成本超支


维护项目中的成本超支应由施工专业人员使用主动系统进行监控和有效管理。为了建立更有效的主动系统来解决维护项目中的成本超支问题，本文提出了一种基于机器学习的预测拓扑方法，该方法集成到各种机器学习模型中以增强特征选择过程。收集了 2016 年至 2022 年间翻修的 1807 所公立学校的项目数据，以测试拟议的数学方法。结果表明，所提出的方法在 7 种机器学习算法和 Hybrid 模型中的 6 种中表现出卓越的性能，实现了更高的准确性。这种方法将使建筑专业人员能够建立和实现更有效的主动系统，以管理维护项目中的成本问题。此外，本文将为开发有效的机器学习模型打开新的大门，而无需对其他结构问题（如时间、质量或安全）使用优化方法。