基于人工智能生命周期分析的地聚合物混凝土可持续性评价综述

V. Ramesh, B. Muthramu, D. Rebekhal
{"title":"基于人工智能生命周期分析的地聚合物混凝土可持续性评价综述","authors":"V. Ramesh,&nbsp;B. Muthramu,&nbsp;D. Rebekhal","doi":"10.1007/s43503-024-00045-3","DOIUrl":null,"url":null,"abstract":"<div><p>Geopolymer concrete is acknowledged as a sustainable alternative to conventional Portland cement concrete owing to its ability to reduce carbon emissions and reutilize industrial by-products. This paper reviews the application of Artificial Intelligence-based Life Cycle Analysis (LCA) techniques in the sustainability assessment of geopolymer concrete. The assessment covers the entire life cycle of geopolymer concrete, spanning from the extraction of raw materials to its ultimate disposal, with a particular focus on its environmental, economic, and social impacts. The incorporation of AI techniques into the LCA process offers notable advantages, such as the efficient management of large datasets, enhancement of data quality, prediction of environmental impacts, and facilitation of informed decision-making. Key sustainability metrics to be considered include environmental impacts such as carbon footprint and energy consumption, economic factors like cost-effectiveness, as well as social implications. The amalgamation of AI within the LCA framework provides a comprehensive and efficient approach to evaluating the sustainability of geopolymer concrete, thereby facilitating its application in sustainable construction practices.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00045-3.pdf","citationCount":"0","resultStr":"{\"title\":\"A review of sustainability assessment of geopolymer concrete through AI-based life cycle analysis\",\"authors\":\"V. Ramesh,&nbsp;B. Muthramu,&nbsp;D. Rebekhal\",\"doi\":\"10.1007/s43503-024-00045-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Geopolymer concrete is acknowledged as a sustainable alternative to conventional Portland cement concrete owing to its ability to reduce carbon emissions and reutilize industrial by-products. This paper reviews the application of Artificial Intelligence-based Life Cycle Analysis (LCA) techniques in the sustainability assessment of geopolymer concrete. The assessment covers the entire life cycle of geopolymer concrete, spanning from the extraction of raw materials to its ultimate disposal, with a particular focus on its environmental, economic, and social impacts. The incorporation of AI techniques into the LCA process offers notable advantages, such as the efficient management of large datasets, enhancement of data quality, prediction of environmental impacts, and facilitation of informed decision-making. Key sustainability metrics to be considered include environmental impacts such as carbon footprint and energy consumption, economic factors like cost-effectiveness, as well as social implications. The amalgamation of AI within the LCA framework provides a comprehensive and efficient approach to evaluating the sustainability of geopolymer concrete, thereby facilitating its application in sustainable construction practices.</p></div>\",\"PeriodicalId\":72138,\"journal\":{\"name\":\"AI in civil engineering\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s43503-024-00045-3.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AI in civil engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s43503-024-00045-3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI in civil engineering","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s43503-024-00045-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

地聚合物混凝土被认为是传统波特兰水泥混凝土的可持续替代品,因为它具有减少碳排放和再利用工业副产品的能力。本文综述了基于人工智能的生命周期分析技术在地聚合物混凝土可持续性评价中的应用。评估涵盖了地聚合物混凝土的整个生命周期,从原材料的提取到最终的处理,特别关注其对环境、经济和社会的影响。将人工智能技术纳入LCA过程具有显著的优势,例如有效管理大型数据集、提高数据质量、预测环境影响以及促进知情决策。要考虑的关键可持续性指标包括碳足迹和能源消耗等环境影响、成本效益等经济因素以及社会影响。人工智能在LCA框架内的融合提供了一种全面有效的方法来评估地聚合物混凝土的可持续性,从而促进其在可持续建筑实践中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A review of sustainability assessment of geopolymer concrete through AI-based life cycle analysis

Geopolymer concrete is acknowledged as a sustainable alternative to conventional Portland cement concrete owing to its ability to reduce carbon emissions and reutilize industrial by-products. This paper reviews the application of Artificial Intelligence-based Life Cycle Analysis (LCA) techniques in the sustainability assessment of geopolymer concrete. The assessment covers the entire life cycle of geopolymer concrete, spanning from the extraction of raw materials to its ultimate disposal, with a particular focus on its environmental, economic, and social impacts. The incorporation of AI techniques into the LCA process offers notable advantages, such as the efficient management of large datasets, enhancement of data quality, prediction of environmental impacts, and facilitation of informed decision-making. Key sustainability metrics to be considered include environmental impacts such as carbon footprint and energy consumption, economic factors like cost-effectiveness, as well as social implications. The amalgamation of AI within the LCA framework provides a comprehensive and efficient approach to evaluating the sustainability of geopolymer concrete, thereby facilitating its application in sustainable construction practices.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Effects of particle shape and gradation on shear strength and AI-based prediction of friction angle Predicting post-wildfire debris flow onset using machine learning models on multi-parameter experimental data Metaheuristic-based broad learning systems for compressive strength prediction of concrete structures Correction: Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach Novel computer vision algorithm for accurate multi-label classification of concrete bridge defects
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1