高技术通讯2025，Vol.35Issue(10)：1108-1119,12.DOI:10.3772/j.issn.1002-0470.2025.10.008
基于联合特征筛选与混合深度学习框架的项目成本估算方法
Project cost estimation method based on joint feature selection and hybrid deep learning framework
摘要
Abstract
Reliable estimation of construction costs is crucial for project planning and resource allocation.To address the challenges of high-dimensional data redundancy and heterogeneity in time-varying data in the construction engineer-ing domain,this study proposes a hybrid deep learning framework based on joint feature selection,which integrates multi-layer perceptron(MLP),temporal convolutional network(TCN),and bidirectional gated recurrent unit(BiGRU)for cost prediction.First,a dual-dimensional feature selection mechanism based on the Pearson-Spearman joint test is designed to eliminate redundant features and reduce data noise.Second,a hybrid framework with a parallel processing architecture,MLP-TCN-BiGRU,is constructed,where static features and temporal varia-bles are processed separately through dual pathways:the TCN layer captures the long-range dependencies of tempo-ral variables using dilated convolutions,while the BiGRU's bidirectional gating mechanism nonlinearly models the extracted features.Meanwhile,the MLP layer integrates static features and generates collaborative representations through a concatenation layer.Finally,an optimizer based on the Newton-Raphson method is introduced to achieve adaptive tuning of hyperparameters.Case studies demonstrate that the proposed method,through modeling implicit association patterns,achieves higher prediction accuracy in construction project cost estimation,providing project managers with a real-time risk-early warning and decision-support tool.关键词
成本估算/混合深度学习框架/时序卷积网络-双向门控循环单元/联合特征分析/参数优化Key words
cost estimation/hybrid deep learning framework/temporal convolutional network-bidirectional gated recurrent unit/joint feature analysis/parameter optimization引用本文复制引用
王艳丽,陈远..基于联合特征筛选与混合深度学习框架的项目成本估算方法[J].高技术通讯,2025,35(10):1108-1119,12.基金项目
河南省科技攻关(252102320160,242102321021)和河南省哲学社会科学教育强省研究(2025JYQS0666)资助项目. （252102320160,242102321021）
