Machine learning and finite element integration-driven surrogate model for fluid-structure interaction seismic response analysis of aqueduct structures
•Methodological Innovation: A synergistic TFSI modeling framework integrates multiphysics simulations and geometric parameterization, trained with 12,600 datasets.•Algorithm Advancement: The improved sand cat swarm optimization algorithm (ISCSOBP) achieves 78 % higher accuracy than traditional BP ne...
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| Vydané v: | Results in engineering Ročník 27; s. 106176 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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Elsevier B.V
01.09.2025
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| ISSN: | 2590-1230, 2590-1230 |
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| Abstract | •Methodological Innovation: A synergistic TFSI modeling framework integrates multiphysics simulations and geometric parameterization, trained with 12,600 datasets.•Algorithm Advancement: The improved sand cat swarm optimization algorithm (ISCSOBP) achieves 78 % higher accuracy than traditional BP neural networks, reducing computation time to 1 % of conventional FEM.•Engineering Impact: The surrogate model demonstrates high precision (maximum absolute error: 0.2 mm, relative error <3 %). The constructed integrated surrogate model has a maximum absolute error of <0.2 mm. Based on the analysis and calculations from 20 seismic waves, when the water level height-to-width ratio is <0.26, 80 % of the conditions have a seismic mitigation effect, while when it is greater than 0.52, 90 % of the conditions experience increased vibration. These findings provide a reference for the seismic design of aqueducts.
The fluid-structure interaction effects in aqueduct structures under seismic excitation constitute a critical challenge in hydraulic engineering seismic analysis. While conventional numerical approaches such as the TFSI model suffer from computational inefficiency, simplified theoretical frameworks such as the Housner model fail to accurately capture the dynamic coupling mechanisms between impulsive and sloshing masses in fluid-structure systems, resulting in a persistent efficiency-accuracy trade-off in dynamic response prediction. To address this, we propose a collaborative machine learning-finite element modeling framework: First, a geometric feature parameterization method converts Boundary Surface Equation-defined aqueduct geometries into machine-interpretable inputs. Second, multiphysics-coupled FEM simulations generate 12,600 training samples. Third, a parameter-optimized machine learning architecture establishes a surrogate model for FSI-governed seismic responses. Experimental results demonstrate the surrogate model achieves 1 % computational time of conventional FEM with below 3 % dynamic prediction errors, 78.7 % higher accuracy than baseline algorithms, and enhanced numerical stability. This breakthrough provides an innovative paradigm for efficient seismic assessment of complex hydraulic structures, substantially advancing the engineering practicality of aqueduct dynamic response prediction. |
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| AbstractList | •Methodological Innovation: A synergistic TFSI modeling framework integrates multiphysics simulations and geometric parameterization, trained with 12,600 datasets.•Algorithm Advancement: The improved sand cat swarm optimization algorithm (ISCSOBP) achieves 78 % higher accuracy than traditional BP neural networks, reducing computation time to 1 % of conventional FEM.•Engineering Impact: The surrogate model demonstrates high precision (maximum absolute error: 0.2 mm, relative error <3 %). The constructed integrated surrogate model has a maximum absolute error of <0.2 mm. Based on the analysis and calculations from 20 seismic waves, when the water level height-to-width ratio is <0.26, 80 % of the conditions have a seismic mitigation effect, while when it is greater than 0.52, 90 % of the conditions experience increased vibration. These findings provide a reference for the seismic design of aqueducts.
The fluid-structure interaction effects in aqueduct structures under seismic excitation constitute a critical challenge in hydraulic engineering seismic analysis. While conventional numerical approaches such as the TFSI model suffer from computational inefficiency, simplified theoretical frameworks such as the Housner model fail to accurately capture the dynamic coupling mechanisms between impulsive and sloshing masses in fluid-structure systems, resulting in a persistent efficiency-accuracy trade-off in dynamic response prediction. To address this, we propose a collaborative machine learning-finite element modeling framework: First, a geometric feature parameterization method converts Boundary Surface Equation-defined aqueduct geometries into machine-interpretable inputs. Second, multiphysics-coupled FEM simulations generate 12,600 training samples. Third, a parameter-optimized machine learning architecture establishes a surrogate model for FSI-governed seismic responses. Experimental results demonstrate the surrogate model achieves 1 % computational time of conventional FEM with below 3 % dynamic prediction errors, 78.7 % higher accuracy than baseline algorithms, and enhanced numerical stability. This breakthrough provides an innovative paradigm for efficient seismic assessment of complex hydraulic structures, substantially advancing the engineering practicality of aqueduct dynamic response prediction. |
| ArticleNumber | 106176 |
| Author | Huang, Liang Xu, Shizhan Li, Ge Guan, Yujian Gong, Shengjia Jiao, Weili |
| Author_xml | – sequence: 1 givenname: Liang surname: Huang fullname: Huang, Liang organization: College of Civil Engineering, Zhengzhou University, Zhengzhou, China – sequence: 2 givenname: Ge surname: Li fullname: Li, Ge email: lige2023@gs.zzu.edu.cn organization: College of Civil Engineering, Zhengzhou University, Zhengzhou, China – sequence: 3 givenname: Yujian surname: Guan fullname: Guan, Yujian organization: Henan Province Pu Lu Expressway Co., Ltd, Zhengzhou, China – sequence: 4 givenname: Weili surname: Jiao fullname: Jiao, Weili organization: Henan Province Pu Lu Expressway Co., Ltd, Zhengzhou, China – sequence: 5 givenname: Shengjia surname: Gong fullname: Gong, Shengjia organization: College of Civil Engineering, Zhengzhou University, Zhengzhou, China – sequence: 6 givenname: Shizhan surname: Xu fullname: Xu, Shizhan organization: College of Civil Engineering, Zhengzhou University, Zhengzhou, China |
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| Cites_doi | 10.1016/j.compfluid.2009.12.010 10.1109/SACI51354.2021.9465625 10.1016/j.rineng.2025.105774 10.1016/j.rineng.2025.105921 10.1111/mice.13457 10.1016/S0168-874X(02)00195-6 10.1002/eqe.141 10.1177/14759217211072237 10.1016/j.rineng.2024.101750 10.1177/8755293020919419 10.1016/j.jfluidstructs.2014.06.023 10.1063/5.0170316 10.1016/j.eswa.2024.124897 10.1080/15583058.2021.1936288 10.1061/(ASCE)0733-9445(2000)126:1(127) 10.1111/mice.13164 10.1007/s11831-024-10143-1 10.1016/j.soildyn.2018.10.015 10.1007/s11831-023-10043-w 10.1615/InterJFluidMechRes.v41.i2.40 10.1016/j.rineng.2023.101274 10.1016/j.cma.2018.10.046 10.1016/j.rineng.2025.104363 10.1109/TMAG.2014.2364031 10.1111/j.1467-8667.1990.tb00377.x 10.1061/(ASCE)0887-3801(2004)18:4(360) 10.1016/j.engappai.2025.111234 |
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| Keywords | Aqueduct Two-way fluid-structure interaction Surrogate model Seismic response Machine learning |
| Language | English |
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| References | Wang, Zhao, Chen (bib0020) 2009 Wang, Ma, Chen (bib0027) 2025 Capuano, Rimoli (bib0051) 2019; 345 (bib0057) 2018 Arnoux, Caillard, Gillon (bib0048) 2015; 51 Bayraktar, HÖKELEKLI, AKKÖSE (bib0006) 2023; 17 Kamarroudi, Hosseini, Hosseini (bib0015) 2021 Mamakli, Turan, Aktaş (bib0004) 2019 Shome (bib0056) 1999 Jiang, Zhao, Du (bib0043) 2022; 21 L. Shang, S. Zhang, J. Liu, Multi-objective sand cat swarm algorithm for reactive power optimization in distribution networks with wind, Solar, and energy storage, J. Nanjing Univ. Inf. Sci. Technol. 16(2) 204–211 Shafighfard, Kazemi, Bagherzadeh, Mieloszyk, Yoo (bib0033) 2024; 39 Dong, Hong, Deng (bib0053) 2023; 35 Harirchian, Hosseini, Novelli, Lahmer, Rasulzade (bib0025) 2024; 21 Sun, Liu, Zhang (bib0046) 2023; 46 Hejazi, Mohammadi (bib0014) 2019; 116 Jeng, Mo (bib0018) 2004; 18 Surana, Blackwell, Powell (bib0010) 2014; 50 Cheng, Jing, Li (bib0012) 2021; 174 Syama, Ramprabhakar, Anand, Guerrero (bib0047) 2023; 19 Wang, Liang, Chen, Wu (bib0026) 2025; 11 Kazemi, Asgarkhani, Ghanbari-Ghazijahani, Jankowski (bib0034) 2025; 156 Xie, Ebad, Padgett (bib0024) 2020; 36 . Cajander, Viarouge, Viarouge (bib0050) 2022 Hui, Yu (bib0044) 2024; 39 Vamvatsikos, Cornell (bib0054) 2002; 31 Kazemi, Asgarkhani, Jankowski (bib0035) 2024; 255 Rebouillat, Liksonov (bib0008) 2010; 39 Wang, Li, Shafieezadeh (bib0022) 2021 Chen, Su (bib0007) 2009 Biczo Z., Felde I., Szenasi S., et al. Distorsion prediction of additive manufacturing process using machine learning methods; Proceedings of the IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, F 2021. Campello (bib0002) 2017 Luco, Cornell (bib0055) 2000; 126 Kazemi, Asgarkhani, Shafighfard, Jankowski, Yoo (bib0032) 2025; 32 Xu, Hon, Zhang (bib0021) 2022 Wei, Liang, Bo (bib0052) 2013; 41 Abbasi, Kazemi, Badeenezhad, Moazamfard, Armand, Mohammadpour (bib0030) 2025; 25 Zhao, Wang, Ma (bib0039) 2022; 41 Wang, Wang, Zhao (bib0037) 2023; 34 Jiang, Zhao, Du (bib0036) 2020; 42 Sham, El-Shafie, Jaafar, S, Sherif, Ahmed (bib0028) 2025; 27 Liu (bib0023) 2021 Yanyu, Dahai (bib0038) 2011; 31 Mordanova, Felice (bib0005) 2018; 14 Huang (bib0042) 2010 Zhang, Wang, Liu (bib0011) 2022; 39 Blackwell (bib0009) 2013 Ganuga, Viswanathan, Sonar (bib0013) 2014; 41 Vanluchene, Sun (bib0017) 1990; 5 Kazemi, Shafighfard, Yoo (bib0031) 2024; 31 Gedik, Celik (bib0001) 2015 Wang, Li (bib0041) 2003; 39 Ravindra, Sankar, Rasappan, Majrafi, Rajan, Kumar (bib0029) 2025 Wang, Wang, Li (bib0019) 2009 Liang, Haotian, Andrew (bib0040) 2023; 18 Singh, Sadeghi, Peterson (bib0016) 2022 Sincraian, Drei, Milani (bib0003) 2017; 2 Abbasi (10.1016/j.rineng.2025.106176_bib0030) 2025; 25 Wang (10.1016/j.rineng.2025.106176_bib0027) 2025 Yanyu (10.1016/j.rineng.2025.106176_bib0038) 2011; 31 Mordanova (10.1016/j.rineng.2025.106176_bib0005) 2018; 14 Xie (10.1016/j.rineng.2025.106176_bib0024) 2020; 36 Wang (10.1016/j.rineng.2025.106176_bib0037) 2023; 34 Wang (10.1016/j.rineng.2025.106176_bib0019) 2009 Harirchian (10.1016/j.rineng.2025.106176_bib0025) 2024; 21 Arnoux (10.1016/j.rineng.2025.106176_bib0048) 2015; 51 Kazemi (10.1016/j.rineng.2025.106176_bib0031) 2024; 31 Cheng (10.1016/j.rineng.2025.106176_bib0012) 2021; 174 Sham (10.1016/j.rineng.2025.106176_bib0028) 2025; 27 Mamakli (10.1016/j.rineng.2025.106176_bib0004) 2019 Blackwell (10.1016/j.rineng.2025.106176_bib0009) 2013 (10.1016/j.rineng.2025.106176_bib0057) 2018 Hejazi (10.1016/j.rineng.2025.106176_bib0014) 2019; 116 Jiang (10.1016/j.rineng.2025.106176_bib0036) 2020; 42 Jiang (10.1016/j.rineng.2025.106176_bib0043) 2022; 21 Capuano (10.1016/j.rineng.2025.106176_bib0051) 2019; 345 Rebouillat (10.1016/j.rineng.2025.106176_bib0008) 2010; 39 Liang (10.1016/j.rineng.2025.106176_bib0040) 2023; 18 Bayraktar (10.1016/j.rineng.2025.106176_bib0006) 2023; 17 Xu (10.1016/j.rineng.2025.106176_bib0021) 2022 Singh (10.1016/j.rineng.2025.106176_bib0016) 2022 Shome (10.1016/j.rineng.2025.106176_bib0056) 1999 Gedik (10.1016/j.rineng.2025.106176_bib0001) 2015 Huang (10.1016/j.rineng.2025.106176_bib0042) 2010 Campello (10.1016/j.rineng.2025.106176_bib0002) 2017 Ravindra (10.1016/j.rineng.2025.106176_bib0029) 2025 Shafighfard (10.1016/j.rineng.2025.106176_bib0033) 2024; 39 Syama (10.1016/j.rineng.2025.106176_bib0047) 2023; 19 Sincraian (10.1016/j.rineng.2025.106176_bib0003) 2017; 2 Wei (10.1016/j.rineng.2025.106176_bib0052) 2013; 41 Hui (10.1016/j.rineng.2025.106176_bib0044) 2024; 39 Cajander (10.1016/j.rineng.2025.106176_bib0050) 2022 Vamvatsikos (10.1016/j.rineng.2025.106176_bib0054) 2002; 31 Liu (10.1016/j.rineng.2025.106176_bib0023) 2021 10.1016/j.rineng.2025.106176_bib0049 Kazemi (10.1016/j.rineng.2025.106176_bib0035) 2024; 255 Kazemi (10.1016/j.rineng.2025.106176_bib0034) 2025; 156 Sun (10.1016/j.rineng.2025.106176_bib0046) 2023; 46 Kazemi (10.1016/j.rineng.2025.106176_bib0032) 2025; 32 Zhang (10.1016/j.rineng.2025.106176_bib0011) 2022; 39 Zhao (10.1016/j.rineng.2025.106176_bib0039) 2022; 41 10.1016/j.rineng.2025.106176_bib0045 Wang (10.1016/j.rineng.2025.106176_bib0041) 2003; 39 Jeng (10.1016/j.rineng.2025.106176_bib0018) 2004; 18 Chen (10.1016/j.rineng.2025.106176_bib0007) 2009 Dong (10.1016/j.rineng.2025.106176_bib0053) 2023; 35 Wang (10.1016/j.rineng.2025.106176_bib0022) 2021 Luco (10.1016/j.rineng.2025.106176_bib0055) 2000; 126 Ganuga (10.1016/j.rineng.2025.106176_bib0013) 2014; 41 Surana (10.1016/j.rineng.2025.106176_bib0010) 2014; 50 Kamarroudi (10.1016/j.rineng.2025.106176_bib0015) 2021 Vanluchene (10.1016/j.rineng.2025.106176_bib0017) 1990; 5 Wang (10.1016/j.rineng.2025.106176_bib0026) 2025; 11 Wang (10.1016/j.rineng.2025.106176_bib0020) 2009 |
| References_xml | – volume: 39 start-page: 739 year: 2010 end-page: 746 ident: bib0008 article-title: Fluid-structure interaction in partially filled liquid containers: a comparative review of numerical approaches publication-title: Comput. Fluids – volume: 34 start-page: 109 year: 2023 end-page: 115 ident: bib0037 article-title: Aqueduct safety evaluation method based on cloud model and information fusion publication-title: J. Water Resour. Water Eng. – reference: Biczo Z., Felde I., Szenasi S., et al. Distorsion prediction of additive manufacturing process using machine learning methods; Proceedings of the IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, F 2021. – volume: 39 start-page: 1249 year: 2003 end-page: 1258 ident: bib0041 article-title: A beam segment element for dynamic analysis of large aqueducts publication-title: Finite Elem. Anal. De. – volume: 46 start-page: 308 year: 2023 end-page: 314 ident: bib0046 article-title: An adaptive t-distribution and Lévy flight-based sand cat swarm optimization algorithm publication-title: J. Liaoning Univ. Sci. Technol. – volume: 41 start-page: 102 year: 2022 end-page: 112 ident: bib0039 article-title: Prediction of aqueduct deformation based on time series decomposition and machine learning publication-title: J. Hydroel. Eng. – start-page: 255 year: 2022 ident: bib0021 article-title: Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach publication-title: Eng. Struct. – start-page: 25 year: 2019 ident: bib0004 article-title: Conservation-aimed evaluation of a historical aqueduct in Izmir publication-title: J. Architect. Eng. – start-page: 150 year: 2018 ident: bib0057 article-title: Seismic Design Code for Hydraulic Structures – volume: 51 year: 2015 ident: bib0048 article-title: Modeling finite-element constraint to run an electrical machine design optimization using machine learning publication-title: IEEE Trans. Magn. – volume: 31 start-page: 16 year: 2011 end-page: 22 ident: bib0038 article-title: Techniques and development trends of temperature control and crack prevention of large-scale concrete aqueducts publication-title: Adv. Sci. Technol. Water Resour. – volume: 27 year: 2025 ident: bib0028 article-title: Advances in AI-based rainfall forecasting: a comprehensive review of past, present, and future directions with intelligent data fusion and climate change models publication-title: Results Eng. – volume: 31 start-page: 491 year: 2002 end-page: 514 ident: bib0054 article-title: Incremental dynamic analysis publication-title: Earthq. Eng. Struct. Dyn. – year: 1999 ident: bib0056 article-title: Probabilistic Seismic Demand Analysis of Nonlinear Structures – volume: 14 start-page: 1 year: 2018 end-page: 13 ident: bib0005 article-title: Seismic assessment of archaeological heritage using discrete element method publication-title: Int. J. Architect. Herit. – volume: 2 year: 2017 ident: bib0003 article-title: DEM numerical approach for masonry aqueducts in seismic zone: two valuable Portuguese examples publication-title: Int. J. Masonry Res. Innov.n – volume: 18 start-page: 360 year: 2004 end-page: 372 ident: bib0018 article-title: Quick seismic response estimation of prestressed concrete bridges using artificial neural networks publication-title: J. Comput. Civil Eng. – volume: 21 year: 2024 ident: bib0025 article-title: Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures publication-title: Results Eng. – year: 2009 ident: bib0019 article-title: Artificial neural network prediction for seismic response of bridge structure publication-title: Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, Peoples R China – volume: 39 start-page: 3216 year: 2024 end-page: 3224 ident: bib0044 article-title: An improved sand cat swarm optimization algorithm with multi-strategies and its application publication-title: Control Decis. – volume: 41 start-page: 145 year: 2014 end-page: 168 ident: bib0013 article-title: Fluid-structure interaction modelling of internal structures in a sloshing tank subjected to resonancet publication-title: Int. Jo.Fluid Mech. Res. – volume: 41 start-page: 221 year: 2013 end-page: 228 ident: bib0052 article-title: Seismic isolation study of aqueduct structures based on the SIMULINK platform publication-title: J. Northwest A&F Univ. – year: 2013 ident: bib0009 article-title: Mathematical Models for Fluid-Solid Interaction and their Numerical Solutions – volume: 345 start-page: 363 year: 2019 end-page: 381 ident: bib0051 article-title: Smart finite elements: a novel machine learning application publication-title: Comput. Methods Appl. Mech. Eng. – year: 2010 ident: bib0042 article-title: Research on Semi-active Control of Large Aqueduct Structures – volume: 116 start-page: 637 year: 2019 end-page: 653 ident: bib0014 article-title: Investigation on sloshing response of water rectangular tanks under horizontal and vertical near fault seismic excitations publication-title: Soil Dyn. Earthq. Eng. – year: 2025 ident: bib0029 article-title: Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: leveraging machine learning and CFD approach publication-title: Results Eng. – volume: 32 start-page: 571 year: 2025 end-page: 603 ident: bib0032 article-title: Machine-learning methods for estimating performance of structural concrete members reinforced with fiber-reinforced polymers publication-title: Arch. Comput. Methods Eng. – year: 2017 ident: bib0002 article-title: Structural Analysis of the Pegões Aqueduct using the Finite Element Method, F – volume: 42 start-page: 12 year: 2020 end-page: 17 ident: bib0036 article-title: Analysis of the prediction of MLR-based monitoring model for Aqueduct deformation publication-title: J. China Three Gorges Univ. – start-page: 236 year: 2021 ident: bib0022 article-title: Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: exploring optimized machine learning models publication-title: Eng. Struct. – volume: 5 start-page: 207 year: 1990 end-page: 215 ident: bib0017 article-title: Neural networks in structural engineering publication-title: Microcomput. Civil Eng. – volume: 11 year: 2025 ident: bib0026 article-title: Data-driven shear capacity prediction of reinforced concrete deep beams with an uncertainty-aware model publication-title: ASCE-ASME J. Risk Uncert. Eng. Syst. Part A – volume: 35 year: 2023 ident: bib0053 article-title: Surrogate model-based deep reinforcement learning for experimental study of active flow control of circular cylinder publication-title: Phys. Fluids – year: 2022 ident: bib0050 article-title: Inductor Design Optimization Using FEA Supervised Machine Learning – volume: 31 start-page: 2049 year: 2024 end-page: 2078 ident: bib0031 article-title: Data-driven modeling of mechanical properties of fiber-reinforced concrete: a critical review publication-title: Arch. Comput. Methods Eng. – volume: 39 start-page: 824 year: 2022 end-page: 831 ident: bib0011 article-title: Comparative study of fluid-filled structure impacted by high-speed spherical fragments based on ALE,CEL and SPH publication-title: Chinese J. Comput. Mech. – volume: 255 year: 2024 ident: bib0035 article-title: Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls publication-title: Expert. Syst. Appl. – volume: 18 year: 2023 ident: bib0040 article-title: Seismic response analysis of double-trough aqueduct considering fluid-structure interaction effect publication-title: PLoS One – volume: 174 start-page: 41 year: 2021 end-page: 54 ident: bib0012 article-title: Dynamic response of concrete tanks under far-field, long-period earthquakes publication-title: Proc. Inst. Civil Eng. – year: 2025 ident: bib0027 article-title: Uncertainty-aware fuzzy knowledge embedding method for generalized structural performance prediction publication-title: Comput.-Aided Civil Infrastruct. Eng. – volume: 126 start-page: 127 year: 2000 end-page: 136 ident: bib0055 article-title: Effects of connection fractures on SMRF seismic drift demands publication-title: J. Struct. Eng. – volume: 19 year: 2023 ident: bib0047 article-title: A hybrid extreme learning machine model with lévy flight chaotic whale optimization algorithm for wind speed forecasting publication-title: Results Eng. – year: 2015 ident: bib0001 article-title: 3D Modeling and Structural Evaluation of Ancient Bozdogan (Valens) Aqueduct in Istanbul – reference: . – year: 2009 ident: bib0007 article-title: Application of ADINA to modeling of fluid-structure interaction in buried liquid-conveying pipeline publication-title: Proceedings of the 2nd International Conference on Information and Computing Science, Manchester, England – start-page: 171 year: 2022 ident: bib0016 article-title: A CFD-FEM based partitioned fluid structure interaction model to investigate surface cracks in elastohydrodynamic lubricated line contacts publication-title: Tribol. Int. – year: 2021 ident: bib0023 article-title: Seismic Performance Assessment of Highway Bridges and Networks Based on Data-Driven Methods – volume: 17 start-page: 472 year: 2023 end-page: 485 ident: bib0006 article-title: Influence of fluid–Structure interaction on seismic performance improvement of historical masonry aqueducts publication-title: Int. J. Architect. Herit. – volume: 36 start-page: 1769 year: 2020 end-page: 1801 ident: bib0024 article-title: The promise of implementing machine learning in earthquake engineering: a state-of-the-art review publication-title: Earthq. Spectra – start-page: 246 year: 2021 ident: bib0015 article-title: Influence of earthquake vertical excitations on sloshing-created P-Δ effect in elevated water tanks: experimental validation, numerical simulation and proposing a modification for Housner model publication-title: Eng. Struct. – volume: 39 start-page: 3573 year: 2024 end-page: 3594 ident: bib0033 article-title: Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams publication-title: Comput.-Aided Civil Infrastruct. Eng. – volume: 156 year: 2025 ident: bib0034 article-title: Ensemble machine learning models for estimating mechanical curves of concrete-timber-filled steel tubes publication-title: Eng. Appl. Artif. Intell. – year: 2009 ident: bib0020 article-title: Research on the prediction of seismic response for bridges based on neural network publication-title: proceedings of the International Conference on Earthquake Engineering - 1st Anniversary of Wenchuan Earthquake – reference: L. Shang, S. Zhang, J. Liu, Multi-objective sand cat swarm algorithm for reactive power optimization in distribution networks with wind, Solar, and energy storage, J. Nanjing Univ. Inf. Sci. Technol. 16(2) 204–211, – volume: 50 start-page: 184 year: 2014 end-page: 216 ident: bib0010 article-title: Mathematical models for fluid-solid interaction and their numerical solutions publication-title: J. Fluids Struct. – volume: 21 start-page: 2786 year: 2022 end-page: 2803 ident: bib0043 article-title: Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization publication-title: Struct. Health Monitor. – volume: 25 year: 2025 ident: bib0030 article-title: Assessing the impact of reverse osmosis plant operations on water quality index improvement through machine learning approaches and health risk assessment publication-title: Results Eng. – year: 2021 ident: 10.1016/j.rineng.2025.106176_bib0023 – volume: 42 start-page: 12 issue: 2 year: 2020 ident: 10.1016/j.rineng.2025.106176_bib0036 article-title: Analysis of the prediction of MLR-based monitoring model for Aqueduct deformation publication-title: J. China Three Gorges Univ. – volume: 39 start-page: 739 issue: 5 year: 2010 ident: 10.1016/j.rineng.2025.106176_bib0008 article-title: Fluid-structure interaction in partially filled liquid containers: a comparative review of numerical approaches publication-title: Comput. Fluids doi: 10.1016/j.compfluid.2009.12.010 – ident: 10.1016/j.rineng.2025.106176_bib0049 doi: 10.1109/SACI51354.2021.9465625 – year: 1999 ident: 10.1016/j.rineng.2025.106176_bib0056 – volume: 2 issue: 1 year: 2017 ident: 10.1016/j.rineng.2025.106176_bib0003 article-title: DEM numerical approach for masonry aqueducts in seismic zone: two valuable Portuguese examples publication-title: Int. J. Masonry Res. Innov.n – volume: 27 year: 2025 ident: 10.1016/j.rineng.2025.106176_bib0028 article-title: Advances in AI-based rainfall forecasting: a comprehensive review of past, present, and future directions with intelligent data fusion and climate change models publication-title: Results Eng. doi: 10.1016/j.rineng.2025.105774 – year: 2025 ident: 10.1016/j.rineng.2025.106176_bib0029 article-title: Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: leveraging machine learning and CFD approach publication-title: Results Eng. doi: 10.1016/j.rineng.2025.105921 – year: 2025 ident: 10.1016/j.rineng.2025.106176_bib0027 article-title: Uncertainty-aware fuzzy knowledge embedding method for generalized structural performance prediction publication-title: Comput.-Aided Civil Infrastruct. Eng. doi: 10.1111/mice.13457 – start-page: 150 year: 2018 ident: 10.1016/j.rineng.2025.106176_bib0057 – volume: 34 start-page: 109 issue: 3 year: 2023 ident: 10.1016/j.rineng.2025.106176_bib0037 article-title: Aqueduct safety evaluation method based on cloud model and information fusion publication-title: J. Water Resour. Water Eng. – volume: 39 start-page: 1249 issue: 13 year: 2003 ident: 10.1016/j.rineng.2025.106176_bib0041 article-title: A beam segment element for dynamic analysis of large aqueducts publication-title: Finite Elem. Anal. De. doi: 10.1016/S0168-874X(02)00195-6 – volume: 31 start-page: 491 issue: 3 year: 2002 ident: 10.1016/j.rineng.2025.106176_bib0054 article-title: Incremental dynamic analysis publication-title: Earthq. Eng. Struct. Dyn. doi: 10.1002/eqe.141 – volume: 41 start-page: 102 issue: 2 year: 2022 ident: 10.1016/j.rineng.2025.106176_bib0039 article-title: Prediction of aqueduct deformation based on time series decomposition and machine learning publication-title: J. Hydroel. Eng. – volume: 21 start-page: 2786 issue: 6 year: 2022 ident: 10.1016/j.rineng.2025.106176_bib0043 article-title: Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization publication-title: Struct. Health Monitor. doi: 10.1177/14759217211072237 – volume: 21 year: 2024 ident: 10.1016/j.rineng.2025.106176_bib0025 article-title: Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures publication-title: Results Eng. doi: 10.1016/j.rineng.2024.101750 – start-page: 246 year: 2021 ident: 10.1016/j.rineng.2025.106176_bib0015 article-title: Influence of earthquake vertical excitations on sloshing-created P-Δ effect in elevated water tanks: experimental validation, numerical simulation and proposing a modification for Housner model publication-title: Eng. Struct. – volume: 14 start-page: 1 year: 2018 ident: 10.1016/j.rineng.2025.106176_bib0005 article-title: Seismic assessment of archaeological heritage using discrete element method publication-title: Int. J. Architect. Herit. – volume: 36 start-page: 1769 issue: 4 year: 2020 ident: 10.1016/j.rineng.2025.106176_bib0024 article-title: The promise of implementing machine learning in earthquake engineering: a state-of-the-art review publication-title: Earthq. Spectra doi: 10.1177/8755293020919419 – year: 2017 ident: 10.1016/j.rineng.2025.106176_bib0002 – volume: 174 start-page: 41 issue: 1 year: 2021 ident: 10.1016/j.rineng.2025.106176_bib0012 article-title: Dynamic response of concrete tanks under far-field, long-period earthquakes publication-title: Proc. Inst. Civil Eng. – volume: 39 start-page: 3216 issue: 10 year: 2024 ident: 10.1016/j.rineng.2025.106176_bib0044 article-title: An improved sand cat swarm optimization algorithm with multi-strategies and its application publication-title: Control Decis. – year: 2009 ident: 10.1016/j.rineng.2025.106176_bib0007 article-title: Application of ADINA to modeling of fluid-structure interaction in buried liquid-conveying pipeline – year: 2009 ident: 10.1016/j.rineng.2025.106176_bib0019 article-title: Artificial neural network prediction for seismic response of bridge structure – volume: 50 start-page: 184 year: 2014 ident: 10.1016/j.rineng.2025.106176_bib0010 article-title: Mathematical models for fluid-solid interaction and their numerical solutions publication-title: J. Fluids Struct. doi: 10.1016/j.jfluidstructs.2014.06.023 – volume: 35 issue: 10 year: 2023 ident: 10.1016/j.rineng.2025.106176_bib0053 article-title: Surrogate model-based deep reinforcement learning for experimental study of active flow control of circular cylinder publication-title: Phys. Fluids doi: 10.1063/5.0170316 – volume: 255 year: 2024 ident: 10.1016/j.rineng.2025.106176_bib0035 article-title: Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2024.124897 – volume: 41 start-page: 221 issue: 01 year: 2013 ident: 10.1016/j.rineng.2025.106176_bib0052 article-title: Seismic isolation study of aqueduct structures based on the SIMULINK platform publication-title: J. Northwest A&F Univ. – year: 2013 ident: 10.1016/j.rineng.2025.106176_bib0009 – volume: 17 start-page: 472 issue: 3 year: 2023 ident: 10.1016/j.rineng.2025.106176_bib0006 article-title: Influence of fluid–Structure interaction on seismic performance improvement of historical masonry aqueducts publication-title: Int. J. Architect. Herit. doi: 10.1080/15583058.2021.1936288 – volume: 126 start-page: 127 issue: 1 year: 2000 ident: 10.1016/j.rineng.2025.106176_bib0055 article-title: Effects of connection fractures on SMRF seismic drift demands publication-title: J. Struct. Eng. doi: 10.1061/(ASCE)0733-9445(2000)126:1(127) – year: 2009 ident: 10.1016/j.rineng.2025.106176_bib0020 article-title: Research on the prediction of seismic response for bridges based on neural network – volume: 39 start-page: 3573 issue: 23 year: 2024 ident: 10.1016/j.rineng.2025.106176_bib0033 article-title: Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams publication-title: Comput.-Aided Civil Infrastruct. Eng. doi: 10.1111/mice.13164 – volume: 32 start-page: 571 issue: 1 year: 2025 ident: 10.1016/j.rineng.2025.106176_bib0032 article-title: Machine-learning methods for estimating performance of structural concrete members reinforced with fiber-reinforced polymers publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-024-10143-1 – volume: 39 start-page: 824 issue: 6 year: 2022 ident: 10.1016/j.rineng.2025.106176_bib0011 article-title: Comparative study of fluid-filled structure impacted by high-speed spherical fragments based on ALE,CEL and SPH publication-title: Chinese J. Comput. Mech. – volume: 116 start-page: 637 year: 2019 ident: 10.1016/j.rineng.2025.106176_bib0014 article-title: Investigation on sloshing response of water rectangular tanks under horizontal and vertical near fault seismic excitations publication-title: Soil Dyn. Earthq. Eng. doi: 10.1016/j.soildyn.2018.10.015 – start-page: 25 year: 2019 ident: 10.1016/j.rineng.2025.106176_bib0004 article-title: Conservation-aimed evaluation of a historical aqueduct in Izmir publication-title: J. Architect. Eng. – volume: 31 start-page: 2049 issue: 4 year: 2024 ident: 10.1016/j.rineng.2025.106176_bib0031 article-title: Data-driven modeling of mechanical properties of fiber-reinforced concrete: a critical review publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-023-10043-w – volume: 41 start-page: 145 issue: 2 year: 2014 ident: 10.1016/j.rineng.2025.106176_bib0013 article-title: Fluid-structure interaction modelling of internal structures in a sloshing tank subjected to resonancet publication-title: Int. Jo.Fluid Mech. Res. doi: 10.1615/InterJFluidMechRes.v41.i2.40 – year: 2010 ident: 10.1016/j.rineng.2025.106176_bib0042 – volume: 46 start-page: 308 issue: 04 year: 2023 ident: 10.1016/j.rineng.2025.106176_bib0046 article-title: An adaptive t-distribution and Lévy flight-based sand cat swarm optimization algorithm publication-title: J. Liaoning Univ. Sci. Technol. – year: 2022 ident: 10.1016/j.rineng.2025.106176_bib0050 – year: 2015 ident: 10.1016/j.rineng.2025.106176_bib0001 – volume: 19 year: 2023 ident: 10.1016/j.rineng.2025.106176_bib0047 article-title: A hybrid extreme learning machine model with lévy flight chaotic whale optimization algorithm for wind speed forecasting publication-title: Results Eng. doi: 10.1016/j.rineng.2023.101274 – start-page: 255 year: 2022 ident: 10.1016/j.rineng.2025.106176_bib0021 article-title: Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach publication-title: Eng. Struct. – start-page: 236 year: 2021 ident: 10.1016/j.rineng.2025.106176_bib0022 article-title: Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: exploring optimized machine learning models publication-title: Eng. Struct. – volume: 345 start-page: 363 year: 2019 ident: 10.1016/j.rineng.2025.106176_bib0051 article-title: Smart finite elements: a novel machine learning application publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2018.10.046 – volume: 25 year: 2025 ident: 10.1016/j.rineng.2025.106176_bib0030 article-title: Assessing the impact of reverse osmosis plant operations on water quality index improvement through machine learning approaches and health risk assessment publication-title: Results Eng. doi: 10.1016/j.rineng.2025.104363 – volume: 51 issue: 3 year: 2015 ident: 10.1016/j.rineng.2025.106176_bib0048 article-title: Modeling finite-element constraint to run an electrical machine design optimization using machine learning publication-title: IEEE Trans. Magn. doi: 10.1109/TMAG.2014.2364031 – volume: 5 start-page: 207 issue: 3 year: 1990 ident: 10.1016/j.rineng.2025.106176_bib0017 article-title: Neural networks in structural engineering publication-title: Microcomput. Civil Eng. doi: 10.1111/j.1467-8667.1990.tb00377.x – volume: 31 start-page: 16 issue: 2 year: 2011 ident: 10.1016/j.rineng.2025.106176_bib0038 article-title: Techniques and development trends of temperature control and crack prevention of large-scale concrete aqueducts publication-title: Adv. Sci. Technol. Water Resour. – start-page: 171 year: 2022 ident: 10.1016/j.rineng.2025.106176_bib0016 article-title: A CFD-FEM based partitioned fluid structure interaction model to investigate surface cracks in elastohydrodynamic lubricated line contacts publication-title: Tribol. Int. – volume: 18 start-page: 360 issue: 4 year: 2004 ident: 10.1016/j.rineng.2025.106176_bib0018 article-title: Quick seismic response estimation of prestressed concrete bridges using artificial neural networks publication-title: J. Comput. Civil Eng. doi: 10.1061/(ASCE)0887-3801(2004)18:4(360) – volume: 156 year: 2025 ident: 10.1016/j.rineng.2025.106176_bib0034 article-title: Ensemble machine learning models for estimating mechanical curves of concrete-timber-filled steel tubes publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2025.111234 – volume: 18 issue: 8 year: 2023 ident: 10.1016/j.rineng.2025.106176_bib0040 article-title: Seismic response analysis of double-trough aqueduct considering fluid-structure interaction effect publication-title: PLoS One – ident: 10.1016/j.rineng.2025.106176_bib0045 – volume: 11 issue: 1 year: 2025 ident: 10.1016/j.rineng.2025.106176_bib0026 article-title: Data-driven shear capacity prediction of reinforced concrete deep beams with an uncertainty-aware model publication-title: ASCE-ASME J. Risk Uncert. Eng. Syst. Part A |
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