Application of Fully Connected Neural Network-Based PyTorch in Concrete Compressive Strength Prediction

被引:0
作者
Dong, Xuwei [1 ]
Liu, Yang [1 ]
Dai, Jinpeng [2 ]
机构
[1] Lanzhou Jiaotong Univ, Key Lab Optoelect Technol & Intelligent Control, Minist Educ, Lanzhou 730070, Peoples R China
[2] Lanzhou Jiaotong Univ, Natl & Prov Joint Engn Lab Rd & Bridge Disaster Pr, Lanzhou 730070, Peoples R China
基金
中国国家自然科学基金;
关键词
CLASSIFICATION; REGRESSION;
D O I
10.1155/2024/8048645
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Compressive strength of concrete is an important parameter in the design of concrete structures and the prediction of their durability. Therefore, it is of great significance to predict the compressive strength of concrete. In this study, a fully connected neural network model is developed using the PyTorch framework to predict the compressive strength of concrete and compared with six other machine learning models. These models are multiple linear regression, K-nearest neighbor regression, support vector machine, decision tree, random forest, light gradient boosting machine, and artificial neural network. The model is trained using 4,253 data with seven input parameters, including cement (C), fly ash (F), mineral powder (K), fine aggregate (FA), coarse aggregate (CA), water reducer admixture (WRA), and water (W). Three thousand six hundred twenty-one data in the datasets are used to train the prediction model after data cleaning, and 632 data are used to validate the model. The results show that the fully connected neural network model based on PyTorch frame can predict the compressive strength of concrete with higher accuracy. Therefore, it is a reliable and useful method to optimize the artificial network model. So, it has important application value in practice. The strength of concrete can be predicted in advance, making the project more efficient and reducing costs. Besides, by adjusting the mix ratio, combining the strength prediction results in different environments and industries to ensure the quality of construction.
引用
收藏
页数:15
相关论文
共 43 条
  • [1] Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
  • [2] Mathematical modeling techniques to predict the compressive strength of high-strength concrete incorporated metakaolin with multiple mix proportions
    Ahmed, Hemn Unis
    Abdalla, Aso A.
    Mohammed, Ahmed S.
    Mohammed, Azad A.
    [J]. CLEANER MATERIALS, 2022, 5
  • [3] Multivariable models including artificial neural network and M5P-tree to forecast the stress at the failure of alkali-activated concrete at ambient curing condition and various mixture proportions
    Ahmed, Hemn Unis
    Mohammed, Ahmed S.
    Mohammed, Azad A.
    [J]. NEURAL COMPUTING & APPLICATIONS, 2022, 34 (20) : 17853 - 17876
  • [4] Al-Rfou R., 2016, arXiv, DOI DOI 10.48550/ARXIV.1605.02688
  • [5] Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete
    Al-Shamiri, Abobakr Khalil
    Yuan, Tian-Feng
    Kim, Joong Hoon
    [J]. MATERIALS, 2020, 13 (05)
  • [6] Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms
    Barkhordari, Mohammad Sadegh
    Armaghani, Danial Jahed
    Mohammed, Ahmed Salih
    Ulrikh, Dmitrii Vladimirovich
    [J]. BUILDINGS, 2022, 12 (02)
  • [7] Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression
    Beskopylny, Alexey N.
    Stel'makh, Sergey A.
    Shcherban', Evgenii M.
    Mailyan, Levon R.
    Meskhi, Besarion
    Razveeva, Irina
    Chernil'nik, Andrei
    Beskopylny, Nikita
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (21):
  • [8] Estimation of compressive strength of high-strength concrete with recycled aggregate using non-destructive test and numerical analysis
    Cho, Y. S.
    Baek, S. K.
    Lee, Y. T.
    Kim, S. H.
    Hong, S. U.
    [J]. MATERIALS RESEARCH INNOVATIONS, 2014, 18 : 270 - 277
  • [9] Prediction of concrete materials compressive strength using surrogate models
    Emad, Wael
    Mohammed, Ahmed Salih
    Kurda, Rawaz
    Ghafor, Kawan
    Cavaleri, Liborio
    Qaidi, Shaker M. A.
    Hassan, A. M. T.
    Asteris, Panagiotis G.
    [J]. STRUCTURES, 2022, 46 : 1243 - 1267
  • [10] Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
    Feng, De-Cheng
    Liu, Zhen-Tao
    Wang, Xiao-Dan
    Chen, Yin
    Chang, Jia-Qi
    Wei, Dong-Fang
    Jiang, Zhong-Ming
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2020, 230