Accurate estimation of construction costs and schedules is crucial for optimizing project planning and resource allocation. Most current approaches utilize traditional statistical analysis and machine learning techniques to process the vast amounts of data regularly generated in construction environments. However, these approaches do not adequately capture the intricate patterns in either time-dependent or time-independent data. Thus, a hybrid deep learning model (NN-BiGRU), combining Neural Network (NN) for time-independent and Bidirectional Gated Recurrent Unit (BiGRU) for time-dependent, was developed in this paper to estimate the final cost and schedule to completion of projects. The Optical Microscope Algorithm (OMA) was used to fine-tune the NN-BiGRU model (OMA-NN-BiGRU). The proposed model earned Reference Index (RI) values of 0.977 for construction costs and 0.932 for completion schedules. These findings underscore the potential of the OMA-NN-BiGRU model to provide highly accurate predictions, enabling stakeholders to make informed decisions that promote project efficiency and overall success.

中文翻译：

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混合深度学习模型，用于使用顺序和非顺序数据在建筑项目中进行准确的成本和进度估算


准确估算施工成本和进度对于优化项目规划和资源分配至关重要。当前大多数方法都利用传统的统计分析和机器学习技术来处理建筑环境中定期生成的大量数据。然而，这些方法并不能充分捕捉时间相关或时间无关数据中的复杂模式。因此，本文开发了一种混合深度学习模型 （NN-BiGRU），结合了与时间无关的神经网络 （NN） 和与时间相关的双向门控循环单元 （BiGRU），以估计项目的最终成本和时间表。光学显微镜算法 （OMA） 用于微调 NN-BiGRU 模型 （OMA-NN-BiGRU）。拟议的模型在施工成本方面的参考指数 （RI） 值为 0.977，在完工进度方面获得了 0.932 的参考指数 （RI） 值。这些发现强调了 OMA-NN-BiGRU 模型提供高度准确预测的潜力，使利益相关者能够做出明智的决策，从而提高项目效率和整体成功。