Fang YuThis email address is being protected from spambots. You need JavaScript enabled to view it.
Department of Engineering Cost, College of Civil Engineering, Hunan City University, Yiyang 413000, China
Received: May 20, 2025 Accepted: August 24, 2025 Publication Date: September 21, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
This paper proposes an integrated optimization framework for ceramic recycled concrete partition walls by combining ABAQUS simulation and a deep learning model. A 3D model was developed in ABAQUS with appropriate boundary conditions and loading steps to simulate real conditions. Performance data from physical tests under various configurations were used to train an enhanced feed-forward neural network. Bayesian optimization was applied to refine design parameters. Results indicate strong agreement between ABAQUS simulations and experimental data, and the deep learning model shows high accuracy in predicting key performance indicators of the partition wall.
[1] Q. Y. Qiao, X. Y. Wang, W. C. Liu, and H. C. Yang, (2024) “Defect Detection in Grouting Sleeve Grouting Material by Piezoelectric Wave Method" Buildings 14(3): 629. DOI: 10.3390/buildings14030629.
[2] R.L. Qin, B. C. Xu, H. W. Chen, Q. P. Lu, C. P. Li, and J. R. e. a. Wang, (2022) “Qualitative and Quantitative Analysis of the Stability of Conductors in Riserless Mud Recovery System" Energies 15(20): 7657. DOI: 10.3390/en15207657.
[3] S. Samadi, S. L. Jin, D. Gruber, and H. Harmuth, (2021) “A Comparison of Two Damage Models for In verse Identification of Mode I Fracture Parameters: Case Study of a Refractory Ceramic" International Journal of Mechanical Sciences 197: 106345. DOI: 10.1016/j.ijmecsci.2021.106345.
[4] A. B. Shemirani, (2022) “Experimental and Numerical Studies of Concrete Bridge Decks Using Ultra High Performance Concrete and Reinforced Concrete" Computers and Concrete 29(6): 407–418. DOI: 10.12989/cac.2022.29.6.407.
[5] H.S. Suh, C. Kweon, B. Lester, S. Kramer, and W. C. Sun, (2023) “A Publicly Available PyTorch-ABAQUS UMATDeep-Learning Framework for Level-Set Plasticity" Mechanics of Materials 184: 104682. DOI: 10. 1016/j.mechmat.2023.104682.
[6] F. Tao, X. Liu, H. D. Du, and W. B. Yu, (2021) “Learning Composite Constitutive Laws via Coupling Abaqus and Deep Neural Network" Composite Structures 272: 114137. DOI: 10.1016/j.compstruct.2021.114137.
[7] A. B. Tawfik, S. Y. Mahfouz, and S. E. F. Taher, (2021) “Nonlinear ABAQUS Simulations for Notched Concrete Beams" Materials 14(23): 7349. DOI: 10.3390/ ma14237349.
[8] H. T. Wang, B. Liu, J. J. Lei, and N. Zhao, (2024) “Im proved Deep Neural Network for Predicting Structural Response of Stiffened Cylindrical Shells to Far-Field Underwater Explosion" Ocean Engineering 298: 117258. DOI: 10.1016/j.oceaneng.2024.117258.
[9] J. X. Wang, Y. J. Zheng, R. Luo, J. Ma, Y. J. Peng, S. Aslam, and W. K. Jia, (2021) “Prediction Method of Three-Dimensional Crack Propagation Path Based on Deep Learning Application" Advanced Engineering Materials 23(4): 2001043. DOI: 10.1002/adem. 202001043.
[10] Z. Q. Wang, M. Chen, and Y. Liao, (2023) “Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation" Applied Sciences Basel 13(8): 4683. DOI: 10.3390/app13084683.
[11] X. B. Yu, R. H. Wang, C. G. Dong, J. Y. Ji, and X. X. Zhen, (2023) “3D Implementation of Push-Out Test in ABAQUS Using the Phase-Field Method" Journal of Mechanical Science and Technology 37: 1731–1745. DOI: 10.1007/s12206-023-0314-z.
[12] R. Q. Zhao, M. L. Wang, and X. M. Guan, (2023) “Exploring Exact Effects of Various Factors on Chloride Dif fusion in Cracked Concrete: ABAQUS-Based Mesoscale Simulations" Materials 16(7): 2830. DOI: 10.3390/ ma16072830.
[13] Y. M. Liu, S. J. Chen, K. Sagoe-Crentsil, and W. H. Duan, (2021) “Predicting the Permeability of Consoli dated Silty Clay Via Digital Soil Reconstruction" Computers and Geotechnics 140: 104468. DOI: 10.1016/j.compgeo.2021.104468.
[14] X. B. Zuo, X. N. Li, Z. K. Zheng, Y. X. Zou, and Y. Y. Zhang, (2024) “Mesoscale Modeling and Simulation on the Chemo-Mechanical Behavior of Concrete in Sulfate Rich Environments" Engineering Fracture Mechanics 300: 109982. DOI: 10.1016/j.engfracmech.2024.109982.
[15] G. Z. Chen, D. R. Zhang, W. Xu, and J. Sun, (2022) “Modeling of Damping Characteristics of Rubber Geopoly merConcrete Based on Finite Element Simulation" Buildings 12(12): 2142. DOI: 10.3390/buildings12122142.
[16] A. F. Hu, S. L. Xie, T. Li, Z. R. Xiao, Y. Chen, and Y. Y. Chen, (2023) “Soil Parameter Inversion Modeling Using Deep Learning Algorithms and Its Application to Settlement Prediction: A Comparative Study" Acta Geotechnica 18: 5597–5618. DOI: 10.1007/s11440 023-01935-z.
[17] L. K. Idriss and M. Owais, (2024) “Global Sensitivity Analysis for Seismic Performance of Shear Wall with High Strength Steel Bars and Recycled Aggregate Concrete" Construction and Building Materials 411: 134498. DOI: 10.1016/j.conbuildmat.2023.134498.
[18] L. Keykhosropour and A. Lemnitzer, (2022) “Seismic Response Behavior of Deep Flexible Underground Structures in Sand-Insights from an Experimental-Numerical Investigation" Bulletin of Earthquake Engineering 20: 5205–5231. DOI: 10.1007/s10518-022-01388-x.
[19] X. Long, M. H. Mao, T. X. Su, Y. T. Su, and M. K. Tian, (2023) “Machine Learning Method to Predict Dynamic Compressive Response of Concrete-Like Material at High Strain Rates" Defense Technology 23: 100–111. DOI: 10.1016/j.dt.2022.02.003.
[20] D. P. Zhu, X. Liu, Z. L. Wang, and X. L. Cai, (2023) “Monitoring Soil-Pile Stripping Damage at Different Temperatures Via Piezoelectric Ceramic Sensors" Shock and Vibration 2023: 4051413. DOI: 10.1155/2023/4051413.
[21] A. Fuke, H. Yuan, K. Kang, S. L. Su, Y. F. Shin, and Y. Cheng, (2025) “Optimization of Photo-Fermentation Bio-Hydrogen Production from Corncob Via Genetic Algorithm Optimized Neural Network and Response Surface Method Model" International Journal of Hydrogen Energy 138: 1293–1302. DOI: 10.1016/j.ijhydene.2024.10.325.
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