Predicting mechanical properties of self-healing concrete with Trichoderma Reesei Fungus using machine learning

被引:8
|
作者
Chiadighikaobi, Paschal Chimeremeze [1 ]
Hematibahar, Mohammad [2 ]
Kharun, Makhmud [2 ]
Stashevskaya, Nadezhda A. [2 ]
Camara, Kebba [3 ]
机构
[1] Afe Babalola Univ, Dept Civil Engn, Ado Ekiti, Nigeria
[2] Moscow State Univ Civil Engn, Reinforced Concrete & Stone Struct, Moscow, Russia
[3] Gambia Port Author, Estate & Civil Engn Dept, Banjul, Gambia
来源
COGENT ENGINEERING | 2024年 / 11卷 / 01期
关键词
Self-healing concrete; Trichoderma Reesei Fungus; fungi; compressive strength; machine learning;
D O I
10.1080/23311916.2024.2307193
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Trichoderma Reesei is a mesophilic and filamentous fungus. It is an anamorph of the fungus Hypocrea jecorina, in addition, T. reesei can secrete large amounts of cellulolytic enzymes and form dextrose PDA (potato dextrose agar) and potato injection. After the preparation of fungi, it is added to the cracked samples. The experimental samples were 150 mm(3) cubic compression and 70 mm x 30 mm x 15 mm cracks on the surface of each cube. Different fungi water extracts were used with 0, 50.5, 6.37, and 8.42 liters of water per ml. The results show that the addition of 8.42 (ml) of the mushroom extract with one liter of water has the maximum compressive strength with more than 18.99 MPa for 28 days, 16.7 for 14 days, and 14.5 for 7 days. In this study, linear regression, lasso regression, and rigid regression have been used to predict compressive strength, also the cooperation between mushroom juice per milliliter and compressive strength has been predicted. To find the accuracy, Correlation Coefficient (R2), Mean Absolute Errors (MAE), and Root Mean Square Error have been used. The results of machine learning show that the results of linear regression and rigid regression R-2 were more than 0.98. In addition, the relationship compressive strength prediction results showed that R-2 for fungi broth with one liter of water was 5.05 mL was more than 0.98. Finally, this study shows that the fungus Trichoderma reesei is an effective agent for curing concrete and improving the compressive strength of concrete.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] EFFECT OF BACTERIA BACILLUS PSEUDOFIRMUS AND FUNGUS TRICHODERMA REESEI ON SELF-HEALING ABILITY OF CONCRETE
    Zakova, Hana
    Pazderka, Jiri
    Racova, Zuzana
    Ryparova, Pavla
    CONTEMPORARY MATERIALS AND TECHNOLOGIES IN CIVIL ENGINEERING 2018, 2019, 21 : 42 - 45
  • [2] Machine learning algorithms on self-healing concrete
    Shrikant M. Harle
    Asian Journal of Civil Engineering, 2025, 26 (4) : 1381 - 1394
  • [3] Prediction of the self-healing properties of concrete modified with bacteria and fibers using machine learning
    Pessoa C.L.E.
    Peres Silva V.H.
    Stefani R.
    Asian Journal of Civil Engineering, 2024, 25 (2) : 1801 - 1810
  • [4] Enhancement of self-healing to mechanical properties of concrete
    Abd Elzahra, Israa H.
    Al-Sherrawi, Mohannad H.
    INTERNATIONAL CONFERENCE ON ADVANCED ENGINEERING AND TECHNOLOGY (ICAET 2020), 2021, 1117
  • [5] Mechanical Properties of Sustainable,Self-healing Porous Asphalt Concrete
    ERIK SCHLANGEN
    MARTIN V D V
    MARCO POOT
    武汉理工大学学报, 2010, 32 (17) : 22 - 25
  • [6] Investigation on the mechanical properties of Bacillus subtilis self-healing concrete
    Tie, Yuanyuan
    Ji, Yongcheng
    Zhang, Hongzhao
    Jing, Bingyan
    Zeng, Xinya
    Yang, Peili
    HELIYON, 2024, 10 (14)
  • [7] Towards machine learning approaches for predicting the self-healing efficiency of materials
    Wang, Wenjun
    Moreau, Nicolette G.
    Yuan, Yingfang
    Race, Paul R.
    Pang, Wei
    COMPUTATIONAL MATERIALS SCIENCE, 2019, 168 : 180 - 187
  • [8] Conditions for CaCO3 Biomineralization by Trichoderma Reesei with the Perspective of Developing Fungi-Mediated Self-Healing Concrete
    Van Wylick, Aurelie
    Rahier, Hubert
    De Laet, Lars
    Peeters, Eveline
    GLOBAL CHALLENGES, 2024, 8 (01)
  • [9] Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches
    Huang, Xu
    Sresakoolchai, Jessada
    Qin, Xia
    Ho, Yiu Fan
    Kaewunruen, Sakdirat
    MATERIALS, 2022, 15 (13)
  • [10] The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches
    Zhuang, Xiaoying
    Zhou, Shuai
    CMC-COMPUTERS MATERIALS & CONTINUA, 2019, 59 (01): : 57 - 77