基于机器学习的建筑工程成本动态预测模型研究OA
Research on a dynamic prediction model for construction engineering costs based on machine learning
针对建筑工程项目中普遍存在的成本超支问题,以及传统成本预测方法在应对项目动态变化方面的局限性,研究通过纳入材料价格波动、设计变更频率和劳动生产率变化等核心动态影响因素,构建了基于机器学习的动态成本预测模型,并系统比较了多元线性回归、支持向量回归、随机森林和长短期记忆网络 4种算法的预测性能,基于 50个历史项目数据进行了实证分析.结果表明:机器学习模型的预测精度显著优于传统方法,其中随机森林与长短期记忆网络表现优异,后者在捕捉成本时序特征方面展现独特优势.研究结果为项目全生命周期成本管控提供了具有实际应用价值的决策支持工具.
In response to the prevalent issue of cost overruns in construction projects and the inherent limitations of conventional cost prediction methods in accounting for project dynamics,this study establishes a machine learning-based dynamic cost prediction model by incorporating critical dynamic influencing factors such as material price fluctuations,design change frequency,and labor productivity variations.A systematic comparison was conducted on the predictive performance of four algorithms:multiple linear regression,support vector machines,random forests,and long short-term memory(LSTM)networks.The empirical results,derived from 50 historical project datasets,demonstrate that machine learning models offer superior predictive accuracy.Among these,random forests and LSTM networks exhibit the highest performance,with the LSTM model showing a marked proficiency in capturing temporal cost characteristics.This research delivers a practical decision-support tool for cost control throughout the project lifecycle.
黄建瓯;郑志山
莆田学院 土木工程学院,福建 莆田,351100中国联合工程有限公司,浙江 杭州,310052
建筑与水利
机器学习成本预测动态预测建筑工程长短期记忆网络随机森林
machine learningcost predictiondynamic predictionconstruction engineeringlong short-term memory(LSTM)random forest
《海南大学学报（自然科学版中英文）》 2026 (2)
228-237,10
本文得到福建省自然科学基金面上项目(2021J011104)的支持. This work was supported by the General Program of Fujian Provincial Natural Science Foundation(2021J011104).
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