Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network

被引:18
|
作者
MolaAbasi, Hossein [1 ]
Saberian, Mohammad [2 ]
Kordnaeij, Afshin [3 ]
Omer, Jousha [4 ]
Li, Jie [2 ]
Kharazmi, Parisa [5 ]
机构
[1] Gonbad Kavous Univ, Dept Civil Engn, Gonbad, Iran
[2] RMIT Univ, Sch Engn, Melbourne, Vic, Australia
[3] Imam Khomeini Int Univ, Dept Civil Engn, Ghazvin, Iran
[4] Univ Kingston, Civil Engn, London, England
[5] Golestan Univ, Dept Civil Engn, Gorgan, Golestan, Iran
关键词
Stabilisation; zeolite; cement; unconfined compression test; stress-strain behaviour; GMDH; CONTROLLING STRENGTH; NATURAL ZEOLITE; PARAMETERS;
D O I
10.1080/01694243.2019.1571659
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Stabilizing sand with cement is considered to be one of the most cost-effective and useful methods of in-situ soil improvement, and the effectiveness is often assessed using unconfined compressive tests. In certain cases, zeolite and cement blends have been used; however, even though this is a fundamental issue that affects the settlement response of a soil, very few attempts have been made to assess the stress-strain behaviour of the improved soil. Also, the majority of previous studies that predicted the unconfined compressive strength (UCS) of zeolite cemented sand did not examine the effect of the soil improvement variables and strain concurrently. Therefore, in this paper, an initiative is taken to predict the relationships for the stress-strain behaviour of cemented and zeolite-cemented sand. The analysis is based on using the unconfined compression test results and Group Method of Data Handling (GMDH) type Neural Network (NN). To achieve this end, 216 stress-strain diagrams resulting from unconfined compression tests for different cement and zeolite contents, relative densities, and curing times are collected and modelled via GMDH type NN. In order to increase the accuracy of the predictions, the parameters associated with successive stress and strain increments are considered. The results show that the suggested two and three hidden layer models appropriately characterise the stress-strain variations to produce accurate results. Moreover, the UCS values derived from this method are much more accurate than those provided in previous approaches. Moreover, the UCS values derived from this method are much more accurate than those provided in previous approaches which simply proposed the UCS values based on the content of the chemical binders, compaction, and/or curing time, not considering the relationship between stress and strain. Finally, GMDH models can be considered to be a powerful method to determine the mechanical properties of a soil including the stress-strain relationships. The other novelty of the work is that the accuracy of the prediction of the strain-stress behaviour of zeolite-cement-sand samples using the GMDH models is much higher than that of the other models.
引用
收藏
页码:945 / 962
页数:18
相关论文
共 11 条
  • [1] Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network
    MolaAbasi, Hossein
    Saberian, Mohammad
    Kordnaeij, Afshin
    Omer, Jousha
    Li, Jie
    Kharazmi, Parisa
    [J]. Journal of Adhesion Science and Technology, 2019, 33 (09): : 945 - 962
  • [2] Prediction of Zeolite-Cemented Sand Tensile Strength by GMDH type Neural Network
    MolaAbasi, Hossein
    Khajeh, Aghileh
    Semsani, Safoura Naderi
    Kordnaeij, Afshin
    [J]. JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY, 2019, 33 (15) : 1611 - 1625
  • [3] Simulation of stress-strain behavior of fine sand using artificial neural network
    Hsieh, Shun-Chieh
    Chen, Yie-Ruey
    Shih, Po-Yi
    [J]. PROCEEDINGS OF THE SIXTEENTH (2006) INTERNATIONAL OFFSHORE AND POLAR ENGINEERING CONFERENCE, VOL 2, 2006, : 583 - 587
  • [4] Polynomial neural network model to estimate the stress-strain behavior of zeolite-cement injected sand
    Kordnaeij, Afshin
    Moayed, Reza Ziaie
    Jafarpour, Peyman
    Mansoori, Alireza
    MolaAbasi, Hossein
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2023, 378
  • [5] A Study on Stress-Strain Behaviour of Geotube Structure Filled with Silty Sand Under Low Confining Pressure by Triaxial Compression Test
    Kim, Hyeong-Joo
    Park, Tae-Woong
    Kim, Ki-Hong
    [J]. JOURNAL OF THE KOREAN GEOSYNTHETIC SOCIETY, 2022, 21 (04): : 69 - 78
  • [6] Parameterization-based neural network: predicting non-linear stress-strain response of composites
    Feng, Haotian
    Prabhakar, Pavana
    [J]. ENGINEERING WITH COMPUTERS, 2024, 40 (03) : 1621 - 1635
  • [7] Sequential simulation and neural network in the stress-strain curve identification over the large strains using tensile test
    Jenik, Ivan
    Kubik, Petr
    Sebek, Frantisek
    Hulka, Jiri
    Petruska, Jindrich
    [J]. ARCHIVE OF APPLIED MECHANICS, 2017, 87 (06) : 1077 - 1093
  • [8] Predicting the multiaxial stress-strain behavior of polyethylene terephthalate (PET) at different strain rates and temperatures above Tg by using an Artificial Neural Network
    Teng, Fei
    Menary, Gary
    Malinov, Savko
    Yan, Shiyong
    Stevens, John Boyet
    [J]. MECHANICS OF MATERIALS, 2022, 165
  • [9] Predicting stress-strain behavior of normal weight and lightweight aggregate concrete exposed to high temperature using LSTM recurrent neural network
    Tanhadoust, A.
    Yang, T. Y.
    Dabbaghi, F.
    Chai, H. K.
    Mohseni, M.
    Emadi, S. B.
    Nasrollahpour, S.
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2023, 362
  • [10] DEVELOPMENT OF THE RAMBERG-OSGOOD MECHANICAL STRESS-STRAIN CURVE USING THE ARTIFICIAL NEURAL NETWORK METHOD TO EVALUATE MECHANICAL BEHAVIOUR OF 316L STAINLESS STEEL IN THE LIQUID LEAD
    Stoica, Livia
    Radu, Vasile
    Nitu, Alexandru
    [J]. JOURNAL OF SCIENCE AND ARTS, 2023, (02): : 537 - 552