Dry unit weight of compacted soils prediction using GMDH-type neural network

被引:21
|
作者
Hassanlourad, Mahmoud [1 ]
Ardakani, Alireza [1 ]
Kordnaeij, Afshin [1 ]
Mola-Abasi, Hossein [2 ]
机构
[1] Imam Khomeini Int Univ, Dept Civil Engn, Qazvin, Iran
[2] Gonbadekavous Univ, Dept Civil Engn, Gonbadekavous, Iran
来源
EUROPEAN PHYSICAL JOURNAL PLUS | 2017年 / 132卷 / 08期
关键词
P-WAVE VELOCITY; SLAKE DURABILITY INDEX; FINE-GRAINED SOILS; COMPRESSIVE STRENGTH; IMPACT STRENGTH; BEHAVIOR; PARAMETERS; MODELS;
D O I
10.1140/epjp/i2017-11623-5
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Dry unit weight (gamma(d)) of soils is usually determined by in situ tests, such as rubber balloon, sand cone, nuclear density measurements, etc. The elastic wave method using compressional wave has been broadly used to determine various geotechnical parameters. In the present paper, the polynomial neural network (NN) is used to estimate the gamma(d) of compacted soils indirectly depending on P-wave velocity (V-p), moisture content (omega) and plasticity index (PI) as well as fine-grained particles (FC). Eight natural soil samples (88 data) were applied for developing a polynomial representation of model. To determine the performance of the proposed model, a comparison was carried out between the predicted and experimentally measured values. The results show that the developed GMDH-type NN has a great ability (R-2 = 0.942) to predict the gamma(d) of the compacted soils and is more efficient (53% to 73% improvement) than the previous reported methods. Finally, the derived model sensitivity analysis has been performed to evaluate the effect of each input variable on the proposed model output and shows that the P-wave velocity is the most influential parameter on the predicted gamma(d). [GRAPHICS] .
引用
收藏
页数:11
相关论文
共 50 条
  • [21] GMDH-type Neural Network for Remaining Useful Life Estimation of Equipment
    Zhao, Lin
    Wang, Yipeng
    Liu, Yuan
    Hao, Yong
    [J]. PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 10844 - 10847
  • [22] GMDH-type neural network algorithm with a feedback loop for structural identification of RBF neural network
    Kondo, Tadashi
    Pandya, Abhijit
    Nagashino, Hirofumi
    [J]. INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS, 2007, 11 (03) : 157 - 168
  • [23] Multiple graph kernel learning based on GMDH-type neural network
    Xu, Lixiang
    Bai, Lu
    Xiao, Jin
    Liu, Qi
    Chen, Enhong
    Wang, Xiaofeng
    Tang, Yuanyan
    [J]. INFORMATION FUSION, 2021, 66 (100-110) : 100 - 110
  • [24] Structural identification of the multi-layered neural networks by using GMDH-type neural network algorithm
    Kondo, T
    Pandya, AS
    Gilbar, T
    [J]. KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2, 2001, 69 : 89 - 94
  • [25] Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties
    Kim, Mintae
    Okuyucu, Osman
    Ordu, Ertugrul
    Ordu, Seyma
    Arslan, Ozkan
    Ko, Junyoung
    [J]. MATERIALS, 2022, 15 (18)
  • [26] GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms
    Dag, Osman
    Yozgatligil, Ceylan
    [J]. R JOURNAL, 2016, 8 (01): : 379 - 386
  • [27] Applying GMDH-type Neural Network and Particle warm Optimization for Prediction of Liquefaction Induced Lateral Displacements
    Jirdehi, Reza Ahmadi
    Mamoudan, Hamidreza Talebi
    Sarkaleh, Hossein Hassanpanah
    [J]. APPLICATIONS AND APPLIED MATHEMATICS-AN INTERNATIONAL JOURNAL, 2014, 9 (02): : 528 - 540
  • [28] Applying GMDH-Type Neural Network and Genetic Algorithm for Stock Price Prediction of Iranian Cement Sector
    Fallahi, Saeed
    Shaverdi, Meysam
    Bashiri, Vahab
    [J]. APPLICATIONS AND APPLIED MATHEMATICS-AN INTERNATIONAL JOURNAL, 2011, 6 (02): : 572 - 591
  • [29] Transfer Learning in GMDH-Type Neural Networks
    Abdullahi, Aminu
    Akter, Mukti
    [J]. MULTIMEDIA AND NETWORK INFORMATION SYSTEMS, 2019, 833 : 161 - 169
  • [30] Applying GMDH-type neural network for modeling and prediction of turbidity and free residual aluminium in drinking water
    Daghbandan, Alahyar
    Khalatbari, Saba
    Abbasi, Mohammad Mahdi
    [J]. DESALINATION AND WATER TREATMENT, 2019, 140 : 118 - 131