海南大学学报（自然科学版中英文）2026，Vol.44Issue(2)：228-237,10.DOI:10.65658/j.hndk.2025121501
基于机器学习的建筑工程成本动态预测模型研究
Research on a dynamic prediction model for construction engineering costs based on machine learning
摘要
Abstract
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.关键词
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Key words
machine learning/cost prediction/dynamic prediction/construction engineering/long short-term memory(LSTM)/random forest分类
建筑与水利引用本文复制引用
黄建瓯,郑志山..基于机器学习的建筑工程成本动态预测模型研究[J].海南大学学报（自然科学版中英文）,2026,44(2):228-237,10.基金项目
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). （2021J011104）
