使用人工智能和模糊推理进行建筑物的定性可持续和经济评估的自动化方法方法方法
Nadeem Iqbal1, Mohamed Noureldin2
1Department of Civil Engineering, AALTO University, Espoo, 02150, Finland.
Scientific reports
|December 16, 2025
概括
本研究引入了一个AI框架,用于评估钢筋混凝土建筑物的环境影响和成本. 它准确地预测可持续性指标,并将它们分类为用户友好的类别.
科学领域:
- 建筑工程工程 建筑工程
- 可持续发展的建筑设计
- 在土木工程中的人工智能.
背景情况:
- 建筑行业面临着对环境保护和经济效率的压力.
- 整合生命周期评估 (LCA) 和生命周期成本评估 (LCCA) 对于评估建筑可持续性至关重要.
- 现有的LCA和LCCA方法可能是耗时和复杂的.
研究的目的:
- 开发一个基于人工智能的自动化框架,用于对钢筋混凝土 (RC) 结构的环境和经济评估.
- 预测关键的可持续性指标,包括体现的能源,碳排放和生命周期成本.
- 将复杂的数据转化为可访问的可持续经济类 (A-C).
主要方法:
- 创建了一个大型数据集用于训练机器学习 (ML) 模型.
- 使用了基于回归的ML模型,XGBoost显示了最高的预测准确性 (R2 ≈ 0.99).
- 采用了分类模型和模糊推理系统 (FIS) 来解释和分类到AC类.
主要成果:
- 人工智能框架准确地预测RC结构的LCA和LCCA指标.
- XGBoost模型实现了高预测精度 (R2 ≈ 0.99).
- 对案例研究的验证显示,数值预测和定性分类的准确性超过90%.
结论:
- 开发的AI框架是一个强大,可靠和计算效率高的工具,用于RC建筑的初步可持续性和成本评估.
- 该框架为评估环境和经济影响提供了一个可扩展的解决方案.
- 有可能将框架扩展到其他材料和结构系统.
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