Construction cost forecasting is vital for tendering processes, enabling the evaluation of bidding offers to maximize revenues and avoid losses. In recent years, the automation of this forecasting process has gained attention due to the limitations of traditional approaches that rely on human experts, which can lead to subjective judgments. This paper introduces an ensemble learning decision-support framework that combines regression random forests and gradient-boosting regression trees through regression voting to automate cost estimation for residential and commercial projects. Evaluation of this approach using the dataset from San Francisco’s building inspection department in the United States demonstrated significant performance improvements over support vector regression. This paper highlights the importance of automating construction cost forecasting with artificial intelligence techniques for construction companies and is expected to encourage companies and building inspection departments worldwide to publish more datasets for the application of advanced deep learning models.

中文翻译：

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用于预测建筑成本的集成学习框架


施工成本预测对于招标流程至关重要，它能够评估投标报价以最大限度地提高收入并避免损失。近年来，由于依赖人类专家的传统方法的局限性，这种预测过程的自动化受到了关注，这可能导致主观判断。本文介绍了一个集成学习决策支持框架，该框架通过回归投票将回归随机森林和梯度提升回归树相结合，以自动估算住宅和商业项目的成本。使用美国旧金山建筑检查部门的数据集对这种方法进行评估，结果表明，与支持向量回归相比，性能有了显著提高。本文强调了使用人工智能技术对建筑公司进行自动化建筑成本预测的重要性，并有望鼓励全球公司和建筑检查部门发布更多数据集以应用高级深度学习模型。