Prediction of concrete’s compressive strength using machine learning algorithms

机器学习 均方误差 人工智能 计算机科学 算法 地聚合物水泥 均方预测误差 骨料(复合) 抗压强度 数学 统计 复合材料 材料科学 聚合物
作者
Soumya Shrivastava,Tanya Shrivastava
出处
期刊:Materials Today: Proceedings [Elsevier]
被引量:2
标识
DOI:10.1016/j.matpr.2023.08.252
摘要

The study focuses on predicting the behavior of Lightweight Aggregate Geopolymer Concrete (LWAGC) using machine learning approaches. The performance measures Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to assess many machine learning models. The XGBoost algorithm outperformed other models, with the lowest MSE of 17.34 and the lowest MAE of 2.87. With an MSE of 34.02 and an MAE of 4.46, the LSTM-ANN Hybrid model likewise fared well. The LSTM-CNN Hybrid model, on the other hand, had a higher MSE of 293.92 and a MAE of 13.82, showing space for improvement. When compared to the top-performing algorithms, linear regression, ANN, and CNN models had greater MSE and MAE. These findings emphasize the accuracy with which machine learning approaches, notably XGBoost and LSTM-ANN Hybrid, anticipate LWAGC behavior. Precise prediction allows for the construction of lightweight concrete buildings, which have advantages such as lower density, higher fire resistance, and a lower carbon footprint. This study adds to the body of knowledge on LWAGC by proving the efficiency of machine learning in predicting the behavior of lightweight concrete. The findings may be used to improve the design of lightweight concrete buildings, encouraging sustainable and resilient construction methods in civil engineering. The training and testing data used in this study were divided into a 70% training set and a 30% testing set.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
HuSP完成签到,获得积分10
刚刚
刚刚
1秒前
zzcres完成签到,获得积分10
1秒前
anna发布了新的文献求助10
3秒前
勤奋梨愁发布了新的文献求助10
4秒前
4秒前
潘善若发布了新的文献求助10
4秒前
momo发布了新的文献求助10
5秒前
5秒前
9秒前
11秒前
诺奇完成签到,获得积分10
13秒前
潘善若发布了新的文献求助10
15秒前
乖猫要努力应助猪猪hero采纳,获得10
15秒前
18秒前
18秒前
科目三应助不学无术采纳,获得10
18秒前
MrSong完成签到,获得积分10
22秒前
22秒前
momo发布了新的文献求助10
22秒前
小曾应助安静的万声采纳,获得10
23秒前
高贵的飞阳完成签到,获得积分10
23秒前
小梦发布了新的文献求助10
24秒前
24秒前
25秒前
科研通AI5应助GooJohn采纳,获得10
26秒前
Ava应助yyy采纳,获得10
30秒前
善学以致用应助yyy采纳,获得10
30秒前
共享精神应助yyy采纳,获得10
30秒前
李健的小迷弟应助yyy采纳,获得10
30秒前
JamesPei应助yyy采纳,获得10
30秒前
共享精神应助yyy采纳,获得10
30秒前
潘善若发布了新的文献求助10
30秒前
LL完成签到,获得积分10
31秒前
hu完成签到,获得积分10
33秒前
33秒前
34秒前
久久应助CSPC001采纳,获得10
35秒前
TaoBijiang完成签到,获得积分10
36秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989263
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253814
捐赠科研通 3270066
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136