智能低碳钢筋混凝土梁设计优化通过深度钢筋学习
Alireza Hosseinzadeh1, Mehdi Dehestani2
1Faculty of Civil Engineering, Babol Noshirvani University of Technology, 484, Babol, 47148-71167, Iran.
Scientific reports
|September 27, 2025
概括
本研究介绍了一种使用人工神经网络和深度强化学习的自动化框架,用于设计低碳钢筋混凝土梁,显著减少二氧化碳排放,同时确保结构完整性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 人工智能的人工智能
- 可持续材料 可持续材料
背景情况:
- 钢筋混凝土 (RC) 梁设计面临着由于水泥的碳足迹和缺乏自动化优化工具的挑战.
- 当前的设计方法往往不同时解决结构性能和环境影响.
研究的目的:
- 开发一个自动化的,两阶段的框架来设计可持续的,低碳的RC梁.
- 整合人工神经网络 (ANN) 和深度强化学习 (RL) 进行结构环境优化.
主要方法:
- 为预测混凝土的压力强度和二氧化碳排放 (R2 ≈分别为0.85和0.99) 选择了一个ANN.
- 深度RL,特别是近距离政策优化 (PPO),被用作在定制环境中的代理来优化RC光束设计.
- 该RL代理在13维的动作空间和26个变量状态空间中运行,平衡结构完整性与环境目标.
主要成果:
- 该ANN在预测关键设计参数方面表现出很高的准确性.
- 与A2C代理基准相比,PPO优化的设计实现了43.35-75.04%的较低二氧化碳排放.
- 该框架确保了ACI 318-19代码的合规性,优化了补充水泥材料 (SCM) 的使用,并提高了结构效率.
结论:
- 拟议的框架有效地自动化了可持续RC梁的设计.
- 集成ANN和深度RL为减少混凝土结构对环境的影响提供了切实可行的解决方案.
- 开源代码和Web接口促进了这些先进设计方法的采用.
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