A Creep Model of Steel Slag–Asphalt Mixture Based on Neural Networks

蠕动 沥青 车辙 人工神经网络 超参数 材料科学 结构工程 计算机科学 人工智能 冶金 复合材料 工程类
作者
Bei Deng Bei Deng,Guowei Zeng,Rui Ge
出处
期刊:Applied sciences [Multidisciplinary Digital Publishing Institute]
卷期号:14 (13): 5820-5820
标识
DOI:10.3390/app14135820
摘要

To characterize the complex creep behavior of steel slag–asphalt mixture influenced by both stress and temperature, predictive models employing Back Propagation (BP) and Long Short-Term Memory (LSTM) neural networks are described and compared in this paper. Multiple stress repeated creep recovery tests on AC-13 grade steel slag–asphalt mix samples were conducted at different temperatures. The experimental results were processed into a group of independent creep recovery test results, then divided into training and testing datasets. The K-fold cross-validation was applied to the training datasets to fine-tune the hyperparameters of the neural networks effectively. Compared with the experimental curves, both the effects of BP and LSTM models were investigated, and the broad applicability of the models was proven. The performance of the trained LSTM model was observed by a 95% confidence interval around the fit errors, thereby the creep strain intervals for the testing dataset were obtained. The results suggest that the LSTM model had enhanced prediction compared the BP model for creep deformation trends of steel slag–asphalt mixture at various temperatures. Due to the potent generalization strength of artificial intelligence technology, the LSTM model can be further expanded for forecasting road rutting deformations.
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
check003完成签到,获得积分10
刚刚
2秒前
2秒前
2秒前
智慧女孩发布了新的文献求助10
2秒前
叶叶叶发布了新的文献求助10
2秒前
3秒前
Ava应助yang采纳,获得10
3秒前
4秒前
Heartlark完成签到,获得积分10
4秒前
砍柴少年发布了新的文献求助10
4秒前
海东来应助zjw1997采纳,获得30
4秒前
小崔完成签到,获得积分10
5秒前
wisdom发布了新的文献求助10
6秒前
6秒前
7秒前
大菊完成签到,获得积分10
8秒前
zero完成签到,获得积分10
8秒前
shao发布了新的文献求助10
9秒前
10秒前
爱吃榴莲的芒果完成签到,获得积分10
11秒前
一杯月光完成签到,获得积分10
12秒前
Dr彭0923完成签到,获得积分10
12秒前
FashionBoy应助Leemon33采纳,获得10
13秒前
巨炮叔叔完成签到,获得积分10
14秒前
15秒前
垃圾桶完成签到,获得积分10
16秒前
16秒前
无名完成签到,获得积分10
16秒前
17秒前
是小浩啊完成签到,获得积分10
17秒前
18秒前
18秒前
龙猫完成签到,获得积分10
18秒前
vardy发布了新的文献求助10
19秒前
垃圾桶发布了新的文献求助10
19秒前
20秒前
李健应助微笑的冥幽采纳,获得10
20秒前
21秒前
21秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
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 3969917
求助须知:如何正确求助?哪些是违规求助? 3514626
关于积分的说明 11175060
捐赠科研通 3249928
什么是DOI,文献DOI怎么找? 1795165
邀请新用户注册赠送积分活动 875617
科研通“疑难数据库(出版商)”最低求助积分说明 804891