在使用人工智能建造单轨系统时预测和评估温室气体排放
Teng Li1, Eryu Zhu2, Zhengwei Bai1
1Civil and Engineering, Beijing Jiaotong University, Beijing, China.
Environmental science and pollution research international
|January 15, 2024
概括
这项研究预测了温室气体 (GHG) 排放对于武侯单轨1号线施工阶段使用BP神经网络和粒子群优化. 确定了影响排放的关键因素,用于各站和各段.
科学领域:
- 环境科学 环境科学
- 土木工程 土木工程是指土木工程.
- 计算智能是一种计算智能.
背景情况:
- 温室气体 (GHG) 排放是基础设施开发中的一个关键问题.
- 准确的排放预测和分析对于可持续的建筑实践至关重要.
研究的目的:
- 预测湖单轨1号线建设阶段的温室气体总排放量.
- 通过Slacks-Based Measure (SBM) 超效率方法分析温室气体排放效率.
- 通过灰色关系分析 (GRA) 识别和排名影响温室气体排放的相关因素.
主要方法:
- 利用BP神经网络,通过粒子群优化算法进行排放预测.
- 应用SBM超效率方法来评估排放效率.
- 使用GRA来确定各种因素对温室气体排放的影响.
主要成果:
- 预计各站和各段的温室气体总排放量分别为29300和21000.
- 站点和部分的高技术,纯技术和规模效率被观察到.
- 确定了主要的排放影响因素:对于车站,金属材料消耗 (0.9731) 和成本 (0.9486);对于断面,成本 (0.9766) 和混凝土消耗 (0.9581).
结论:
- 该研究成功预测了温室气体排放,并确定了单轨铁路建设的关键影响因素.
- 这些发现为减轻类似基础设施项目的环境影响提供了有价值的见解.
- 优化预测模型和效率分析对于可持续的运输发展至关重要.
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