桥梁与隧道标志 桥梁与隧道工程 河内土木工程大学（HUCE）
研究方向 04

人工智能与数字孪生

用于损伤检测、材料性能预测和结构数字孪生的机器学习、神经网络、深度学习与符号回归。

26项论著26篇论文2019⁠–⁠202610人有论著4位教师在简介中登记2项课题

概况

在本教研室，人工智能主要用于两类问题：基于实测数据的结构损伤检测，以及根据配合比预测材料性能。其特点是偏重通过遗传编程和符号回归得到可供工程师在设计中直接使用的显式（“白箱”）公式[19, 20, 24]。

主要研究主题

基于神经网络与深度学习的损伤检测

将传递率函数（两个测点响应谱之比）与神经网络和机器学习相结合，应用于Ca-Non桥和Nam O桁架桥的损伤检测[2, 3]。将间隙平滑模态曲率与卷积神经网络和深度学习相结合，应用于Bo Nghi梁桥；对于板结构，则将二维曲率模态与Faster R-CNN相结合[4, 6, 14]。将前馈神经网络与全局优化算法相结合，用于损伤的检测与量化[7, 8]。

数字孪生

一个数字孪生框架结合有限元模型、模型修正、信号处理和MobileNetV2网络，利用挠度数据检测板的轻度损伤[21]。

材料性能预测

基于164个样本的数据集，采用XGBoost、随机森林和支持向量机预测了透水混凝土的抗压强度和有效孔隙率[10]。遗传编程给出了钢纤维混凝土抗压强度和透水混凝土渗透系数的显式公式；对于预应力空心板，则采用数据驱动模型预测其抗剪强度[17, 19, 20]。

课题

基于振动响应和人工神经网络的损伤检测校级课题，以及结合人工智能与数据驱动方法预测透水水泥混凝土力学行为的部级课题（2023—2024年）属于该方向。

科研项目 (2)

课题名称照录教师简介（越南文）。

  • Principal investigator (2023⁠–⁠2024). Nghiên cứu xây dựng mô hình dự báo ứng xử cơ học của bê tông xi măng rỗng dựa trên tiếp cận lý thuyết kết hợp kỹ thuật trí tuệ nhân tạo và định hướng dữ liệu. Code: B2023-XDA-03. Ministry-level project, Ministry of Education and Training (đã nghiệm thu)
  • Nghiên cứu phương pháp phát hiện và đánh giá hư hỏng của kết cấu dựa vào ứng xử dao động và mạng Nơ ron nhân tạo. Đại học Xây dựng

发展方向

以下方向依据2023—2026年的论文、科研课题以及教师简介中列出的研究方向归纳；本页不涉及规划或经费。

  1. 深度学习用于无损检测

    2026年的一篇论文采用探地雷达（GPR）与YOLOv11，以完全无损和自动化的方式检测钢筋混凝土板中的钢筋锈蚀[22]。

  2. 神经网络结合遗传算法用于施工

    2026年的ANN–GA框架基于675个数值模拟算例进行训练，预测并优化冷却水管参数，以控制大体积混凝土承台的温度场并防止开裂[26]。

  3. 以显式公式替代黑箱模型

    针对高填充悬浮液模量的约束符号回归以及骨料堆积密实度显式公式（2026年）延续了将机器学习转化为公式的方向[24, 25]。教师简介将“构建结构数字孪生模型”和“人工智能与物联网在结构监测中的应用”列为研究方向。

该方向研究人员

在该方向有论著 (10)

简介中登记该方向的教师 (4)

在简介中登记该方向的现任教师；在团队页面按方向筛选以及各方向卡片中显示的也是这一组。

该方向论著 (26)

归入该方向的全部期刊论文、会议论文、书籍章节和著作，按时间由近及远排列。本教研室成员以粗体标出，* 为通讯作者；题名保留原文。

2026

  1. [26]Dinh-Manh Le, Van-Minh Le*, Thai-Son Vu, Huong-Duong Nguyen (2026). Artificial intelligence-based prediction of cooling pipe system parameters for thermal field control and crack prevention in mass concrete structures. Structural Concrete, 1-30. https://doi.org/10.1002/suco.70818
  2. [25]Vu Thai Son, Sengaloun Keoalounxay, Nguyen Ngoc Tuyen, Tran Viet Hung (2026). Predicting the relative shear storage modulus of highly filled bidisperse particulate suspensions by machine learning and constrained symbolic regression. Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE, 20(3), 127-146. https://doi.org/10.31814/stce.huce2026-20(3)-07
  3. [24]Thai-Son Vu, Sengaloun Keoalounxay, Quoc-Bao Nguyen* (2026). An Explicit Formula for the Dry Packing Density of Aggregate Gradations Derived from the de Larrard Compressible Packing Model by Symbolic Regression. Eng, 7(9), 493. https://doi.org/10.3390/eng7090493
  4. [23]Quy, N. D.*, & Le, B. D. (2026). Real-time vibration monitoring and anomaly detection using autoencoder and wireless IoT sensor node. In Lecture Notes in Networks and Systems (pp. 260-267). Springer, Cham. https://doi.org/10.1007/978-3-032-18316-3_29
  5. [22]Bui, H. T.*, Tan, K. H.* (2026). Intelligent corrosion detection in reinforced concrete slabs using GPR and YOLOv11: A fully non-destructive and automated deep learning framework. Construction and Building Materials, 506, 145014. https://doi.org/10.1016/j.conbuildmat.2025.145014

2025

  1. [21]Nguyen, H. D., Nguyen, H., & Gao, X. (2025). A digital twin framework with MobileNetV2 for damage detection in slab structures. Fracture and Structural Integrity, 19(72), 121-136. https://doi.org/10.3221/IGF-ESIS.72.09
  2. [20]Ba Anh Le*, Hoang Quan Nguyen, Bao Viet Tran, Thai Son Vu, Thi Loan Bui (2025). Symbolic regression model for predicting compression strength of steel fiber-reinforced concrete. Transportation Research Procedia, 85, 94-101 (SDCAT 2023). https://doi.org/10.1016/j.trpro.2025.03.138

2024

  1. [19]Ba-Anh Le*, Thai-Son Vu, Hoang-Quan Nguyen, Viet-Hung Vu (2024). A Genetic Programming-Assisted Analytical Formula for Predicting the Permeability of Pervious Concrete. Engineering, Technology & Applied Science Research, 14(3), 14775-14780. https://doi.org/10.48084/etasr.7619
  2. [18]Nguyen, Q. B.*, Nguyen, H. H., & Ha, M. H. (2024). An efficient framework for optimization of nonlinear steel trusses with continuous variables using LightGBM. International Journal of GEOMATE, 26(116), 85-92. https://doi.org/10.21660/2024.116.4398
  3. [17]Fan, S., Nguyen, T. N. H., Ren, H., & Wang, P.* (2024). Data-driven shear strength predictions of prestressed concrete hollow-core slabs. Journal of Building Engineering, 95, 110343. https://doi.org/10.1016/j.jobe.2024.110343
  4. [16]Hoang-Quan Nguyen, Ba-Anh Le*, Bao-Viet Tran, Thai-Son Vu, Thi-Loan Bui (2024). Deep artificial neural network-powered phase field model for predicting damage characteristic in brittle composite under varying configurations. Machine Learning: Science and Technology, 5, 025062. https://doi.org/10.1088/2632-2153/ad52e8
  5. [15]Ba-Anh Le, Bao-Viet Tran*, Thai-Son Vu, Viet-Hung Vu, Van-Hung Nguyen (2024). Predicting the Compressive Strength of Pervious Cement Concrete based on Fast Genetic Programming Method. Arabian Journal for Science and Engineering, 49(4), 5487–5504. https://doi.org/10.1007/s13369-023-08396-2

2023

  1. [14]Nguyen, H. D., & Abdel Wahab, M.* (2023). Damage detection in slab structures based on two-dimensional curvature mode shape method and Faster R-CNN. Advances in Engineering Software, 176, 103371. https://doi.org/10.1016/j.advengsoft.2022.103371
  2. [13]Dao, H., Khuc, T.*, Truong, Q., Dinh, C., & Nguyen, A. (2023). Pavement crack segmentation using an attention-based deep learning model. Lecture Notes in Civil Engineering, 727-737. Springer, Singapore. https://doi.org/10.1007/978-981-99-2345-8_75
  3. [12]Nguyen, T. H.*, Nguyen, H. D., & Vu, A. T. (2023). Solving Engineering Optimization Problems Using Machine Learning Classification-Assisted Differential Evolution. Proceedings of the International Conference of Steel and Composite for Engineering Structures. Lecture Notes in Civil Engineering, Springer, 1-23. https://doi.org/10.1007/978-3-031-24041-6_1

2022

  1. [11]Lê Bá Anh, Vũ Thái Sơn, Nguyễn Văn Hưng, Nguyễn Thị Hương Giang, Trần Bảo Việt (2022). Kết hợp phát triển giải tích và kỹ thuật trí tuệ nhân tạo để xây dựng phương trình thấm Kozeny-Carman cho vật liệu bê tông xi măng rỗng. Hội nghị Cơ học toàn quốc lần thứ XI, Tập 2, 470-476.
  2. [10]Ba-Anh Le, Viet-Hung Vu, Soo-Yeon Seo, Bao-Viet Tran*, Tuan Nguyen-Sy, Minh-Cuong Le, Thai-Son Vu (2022). Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods. KSCE Journal of Civil Engineering, 26, 4664–4679. https://doi.org/10.1007/s12205-022-1918-z
  3. [9]Vũ Thái Sơn, Vũ Việt Hưng, Nguyễn Tuấn Cường, Trương Đình Thảo Anh, Trần Bảo Việt (2022). Xây dựng công thức dự báo hệ số thấm của bê tông rỗng dựa trên định hướng dữ liệu. Tạp chí Khoa học Giao thông Vận tải, 73(2), 176-188. https://doi.org/10.47869/tcsj.73.2.7

2021

  1. [8]Ho, L. V., Nguyen, D. H., Mousavi, M., De Roeck, G., Bui-Tien, T., Gandomi, A. H., & Wahab, M. A. (2021). A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks. Computers & Structures, 252, 106568.
  2. [7]Ho, L. V., Nguyen, H. D., De Roeck, G., Bui-Tien, T., & Abdel Wahab, M.* (2021). Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm. Journal of Zhejiang University-SCIENCE A, 22(6), 467-480. https://doi.org/10.1631/jzus.A2000316
  3. [6]Nguyen, D. H., Bui-Tien, T., Roeck, G. D., & Wahab, M. A. (2021). Damage detection in structures using modal curvatures gapped smoothing method and deep learning. Structural Engineering and Mechanics, 77(1), 47-56.

2020

  1. [5]Nguyen, T. T., Pham Duy, H., Pham Thanh, T., & Vu, H. H. (2020). Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence. Advances in Civil Engineering, 2020.
  2. [4]Nguyen, D. H., Nguyen, Q. B., Bui-Tien, T., De Roeck, G., & Wahab, M. A. (2020). Damage detection in girder bridges using modal curvatures gapped smoothing method and Convolutional Neural Network: Application to Bo Nghi bridge. Theoretical and Applied Fracture Mechanics, 109, 102728.
  3. [3]Nguyen, D. H., Tran-Ngoc, H., Bui-Tien, T., De Roeck, G., & Wahab, M. A. (2020). Damage detection in truss bridges using transmissibility and machine learning algorithm: Application to Nam O bridge. Smart Structures and Systems, 26(1), 35-47.

2019

  1. [2]Nguyen H.D., Bui T.T., De Roeck G., Abdel Wahab M. (2019) Damage Detection in Ca-Non Bridge Using Transmissibility and Artificial Neural Networks. Structural Engineering and Mechanics. An International Journal
  2. [1]Nguyen H.D., Bui T.T., De Roeck G., Abdel Wahab M. (2019) Damage Detection in Simply Supported Beam Using Transmissibility and Auto-Associative Neural Network. In: Abdel Wahab M. (eds) . Proceedings of the 1st International Conference on Numerical Modelling in Engineering. NME 2018.

编制方法：现任和退休教师简介中的每项论著均根据题名和发表载体归入8个方向中的一个或多个；同一论著可出现在多个方向中。