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风险指数化人工神经网络用于预测灌运河线项目的持续时间和成本,使用基于调查的校准和python验证
Boshra Taha1,2, Ahmed H Ibrahim3, Asmaa A Soliman4
1Industrial Engineering Department, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, Saudi Arabia. Boshrataha@zu.edu.eg.
Scientific reports
|November 17, 2025
概括
本研究介绍了灌运河层项目风险驱动的预测模型,提高了估计项目持续时间和成本的准确性. 开发的机器学习框架为基础设施规划提供了实际的决策支持工具.
科学领域:
- 土木工程 土木工程是指土木工程.
- 项目管理 项目管理
- 人工智能的人工智能
背景情况:
- 灌运河层项目经常因固有的不确定性而面临延误和预算超支.
- 准确估计项目持续时间和成本对于成功的基础设施开发至关重要.
研究的目的:
- 开发和验证风险驱动的预测模型,用于估计灌运河层项目的项目持续时间和成本.
- 创建一个实用的决策支持工具,为工程师和规划人员使用一个综合框架.
主要方法:
- 使用分析层次过程修订重要性指数 (AHP-RII) 将93个风险因素减少到20个.
- 在5000个模拟场景中训练了一种多层感知人造神经网络 (ANN).
- 在八个现实项目中使用离开一个项目的交叉验证验证该模型.
主要成果:
- 该ANN模型实现了高精度,测试R平方值为0.82.82.
- 平均预测错误在时间方面为0.87个月,成本方面为EGP 102,500.
- 该模型被部署为一个用户友好的基于Python的桌面应用程序.
结论:
- 基于ANN的综合框架有效地将专家风险评估与机器学习相结合,以改善项目预测.
- 开发的模型为基础设施项目规划的早期阶段提供了实用和准确的决策支持工具.
- 这种方法提高了复杂工程项目的时间和成本估计的可靠性.
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