ABSTRACT Predicting project cost overruns in the bidding stage has undergone significant changes with the application of state-of-the-art techniques. Both modeling techniques and domain knowledge should be integrated to enhance predictions of cost performance. This study developed an ensemble-learning classification model to predict the expected cost-overrun levels of public projects and derive explanatory factors and key predictors. A database of 234 public-sector projects in South Korea was used, including project characteristics (i.e., project delivery method, project types, cost, and schedule) in combination with bidding characteristics (i.e., award method, number of bidders, bid to estimate ratio, number of joint ventures). The results yielded an average accuracy of 61.41% for five model runs. Furthermore, information on the project type being constructed is an important contributor to prediction accuracy. Results of the model enable project owners and managers to screen projects that are expected to incur excessive cost overruns and to anticipate budget loss during the bidding stage and before contracts are finalized.

中文翻译：

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使用集成学习预测投标阶段的项目成本超支水平

摘要 随着最先进技术的应用，在投标阶段预测项目成本超支发生了重大变化。建模技术和领域知识都应该结合起来，以增强对成本绩效的预测。本研究开发了一个集成学习分类模型来预测公共项目的预期成本超支水平，并推导出解释因素和关键预测因素。使用了韩国 234 个公共部门项目的数据库，包括项目特征（即项目交付方法、项目类型、成本和进度）以及投标特征（即授予方法、投标人数量、投标估算）比例、合资企业数量）。结果表明，五个模型运行的平均准确率为 61.41%。此外，有关正在建设的项目类型的信息是预测准确性的重要因素。该模型的结果使项目所有者和经理能够筛选预计会导致过度成本超支的项目，并在投标阶段和合同最终确定之前预测预算损失。