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学者姓名：刘景良
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Abstract ：
Structural damage detection is essential for civil infrastructure safety. The challenges in noise sensitivity, multi-scale feature extraction, and handling bidirectional temporal dependencies are often encountered by traditional methods such as vibration analysis and computer vision. Although potential solutions are offered by recent deep-learning advancements, limitations are frequently imposed by low interpretability and the incapability to adaptively prioritize crucial features within complex time-series data. To address these, a novel hybrid deep-learning framework is proposed. It is integrated with multi-scale convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanisms. Localized time-frequency features are captured from vibration signals by the CNN using multi-scale kernels. Bidirectional temporal dependencies are skillfully captured by the BiLSTM. The interpretability is improved by the attention mechanism through dynamic feature weighting. Experiments on a simulated steel frame demonstrate that detection accuracy and robustness can be enhanced by this framework. This work promotes structural health monitoring, providing a practical tool for engineering applications.
Keyword ：
attention mechanism attention mechanism deep learning deep learning interpretability interpretability structural health monitoring structural health monitoring vibration signal analysis vibration signal analysis
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| GB/T 7714 | Wu, Shengping , Liu, Jingliang . A Multi-Scale CNN-BiLSTM Framework with An Attention Mechanism for Interpretable Structural Damage Detection [J]. | INFRASTRUCTURES , 2025 , 10 (4) . |
| MLA | Wu, Shengping 等. "A Multi-Scale CNN-BiLSTM Framework with An Attention Mechanism for Interpretable Structural Damage Detection" . | INFRASTRUCTURES 10 . 4 (2025) . |
| APA | Wu, Shengping , Liu, Jingliang . A Multi-Scale CNN-BiLSTM Framework with An Attention Mechanism for Interpretable Structural Damage Detection . | INFRASTRUCTURES , 2025 , 10 (4) . |
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Engineering structures in operation are time-varying or nonlinear systems and the resultant response signals are usually non-stationary, closely-spaced and even mode-overlapped in frequency domain. For such structures, it is vital to identify time-varying modal parameters by a way of signal processing, which therefore provides basis for health monitoring, safety assessment and vibration control of engineering structures. However, two critical challenges arise in the time-dependent modal parameter identification: (1) the effective decomposition of closely-spaced and overlapped modes from non-stationary response signals of time-varying structures; (2) the precise extraction of time-varying modal parameters, e.g. instantaneous frequency (IF), from the decomposed components. To address these issues mentioned above, a new IF identification method consisting of generally demodulated and adaptive variational mode decomposition (GDAVMD) and multi-synchrosqueezing transform (MSST) is proposed for non-stationary signals of time-varying structures. In this method, an index of mean frequency is established at first as the fitness function of the particle swarm optimization algorithm to adaptively select the parameter combination including the number of modal components and penalty factor. Then, a generalized demodulation algorithm is performed to yield a generally demodulated variational mode decomposition tool that can be used for separating closely-spaced or mode-overlapped components. Following the successful non-stationary signal decomposition via the proposed GDAVMD method, MSST is introduced to identify IFs of the decomposed component signals due to its superiority on time-frequency energy concentration and computational efficiency. The effectiveness and accuracy of the proposed IF identification method are verified via two numerical examples and a steel cable with time-varying tension forces. The results demonstrate that the proposed GDAVMD algorithm behaves better than the standard variational mode decomposition (VMD) on the decomposition of multi-component signals with several overlapped modes on condition that the optimum parameter combination is predetermined. Moreover, its combination with MSST (GDAVMD+MSST) enables a more accurate IF estimation result than VMD+MSST.
Keyword ：
Instantaneous frequency Instantaneous frequency Mode overlapped Mode overlapped Multi-synchrosqueezing transform Multi-synchrosqueezing transform Non-stationary signal Non-stationary signal Particle swarm optimization Particle swarm optimization Variational modal decomposition Variational modal decomposition
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| GB/T 7714 | Liu, Jing-Liang , Chen, Rong , Qiu, Fu-Lian et al. Structural instantaneous frequency identification of non-stationary signals using GDAVMD and MSST [J]. | STRUCTURES , 2025 , 72 . |
| MLA | Liu, Jing-Liang et al. "Structural instantaneous frequency identification of non-stationary signals using GDAVMD and MSST" . | STRUCTURES 72 (2025) . |
| APA | Liu, Jing-Liang , Chen, Rong , Qiu, Fu-Lian , Yu, An-Hua , Zheng, Wen-Ting , Wu, Sheng-Ping . Structural instantaneous frequency identification of non-stationary signals using GDAVMD and MSST . | STRUCTURES , 2025 , 72 . |
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To address the challenge of accurately identifying instantaneous frequency (IF) for non-stationary response signals from time-varying structures, an innovative time-frequency analysis (TFA) method called locally optimized short-time fractional Fourier matching synchrosqueezing transform (LOSTFFIMST) is proposed. Firstly, a strategy of binary component separation is performed on the non-stationary response signals with rapidly and slowly time-varying components, leading to successful signal decomposition results. Secondly, the locally optimized short-time fractional Fourier transform achieves high-quality time-frequency representation of component signals by employing local slope and frequency bandwidth as metrics for optimal fractional rotation parameter selection. Finally, the iterative matching synchrosqueezing transform algorithm further refines frequency bands, enabling a sharper IF curve extraction through the maximum local modulus method. The effectiveness and accuracy of the proposed LOSTFFIMST method are validated through a numerical multi-component signal under diverse noise levels and a dynamic test on a steel cable subjected to rapidly and slowly time-varying tension forces. Furthermore, by integrating the proposed LOSTFFIMST algorithm with taut string theory, the extracted IFs of the time-varying steel cable are applied to inverse identification of cable tension forces. The outcomes reveal that the proposed LOSTFFIMST method not only outperforms other TFA techniques in terms of IF extraction for non-stationary signals that encompass both rapidly and slowly varying components, but also exhibits superior noise robustness. Moreover, the identified cable tension forces demonstrate excellent agreement with the measured time-varying tension forces, which in turn offers robust validation for the accuracy of the proposed IF extraction approach.
Keyword ：
Cable force Cable force Instantaneous frequency Instantaneous frequency Matching synchrosqueezing transform Matching synchrosqueezing transform Non-stationary signal Non-stationary signal Short-time fractional Fourier transform Short-time fractional Fourier transform Time-varying structure Time-varying structure
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| GB/T 7714 | Liu, Jing-Liang , Dai, Yi-Chen , Ren, Wei-Xin et al. LOSTFFIMST: A novel methodology for precise instantaneous frequency extraction in time-varying structures with applications to cable force identification [J]. | STRUCTURES , 2025 , 82 . |
| MLA | Liu, Jing-Liang et al. "LOSTFFIMST: A novel methodology for precise instantaneous frequency extraction in time-varying structures with applications to cable force identification" . | STRUCTURES 82 (2025) . |
| APA | Liu, Jing-Liang , Dai, Yi-Chen , Ren, Wei-Xin , Zheng, Wen-Ting . LOSTFFIMST: A novel methodology for precise instantaneous frequency extraction in time-varying structures with applications to cable force identification . | STRUCTURES , 2025 , 82 . |
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Traditional structural damage identification methods based on convolutional neural network (CNN) require extensive data collection and effective feature extraction from engineering structures, resulting in high computational costs and low recognition accuracy. To enhance the damage detection accuracy and computational efficiency, a new damage identification method is proposed based on improved one-dimensional deep separable convolutional neural network (1D-DSCNN) model. First, the traditional convolutional layers are replaced with depthwise separable convolutional layers to create a novel neural network model. Second, residual connections are incorporated into the depthwise separable convolutional blocks to accurately capture more damage features. The effectiveness and accuracy of the proposed method are validated via a numerical case of the IASC-ASCE SHM Benchmark structure model and two experimental tests on a four-story steel frame structure and a Qatar University grandstand simulator. The results demonstrate that the improved 1D-DSCNN model not only promotes damage identification accuracy, but also accelerates model convergence and decreases the number of model parameters in comparison to the Resnet-34 and conventional 1D-CNN models. Furthermore, the proposed new model exhibits strong noise robustness in terms of damage identification for steel frame structures.
Keyword ：
Convolutional neural network Convolutional neural network Damage identification Damage identification Deep learning Deep learning Depthwise separable convolution Depthwise separable convolution Frame structure Frame structure
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| GB/T 7714 | Liu, Jing-Liang , Chen, Long-Hui , Wei, Xiao-Jun . Damage identification for steel frame structures based on improved one-dimensional depthwise separable convolutional neural network [J]. | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING , 2025 . |
| MLA | Liu, Jing-Liang et al. "Damage identification for steel frame structures based on improved one-dimensional depthwise separable convolutional neural network" . | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2025) . |
| APA | Liu, Jing-Liang , Chen, Long-Hui , Wei, Xiao-Jun . Damage identification for steel frame structures based on improved one-dimensional depthwise separable convolutional neural network . | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING , 2025 . |
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为提高时变结构非平稳响应信号的瞬时频率识别精度，提出一种局部优化多重同步挤压-短时分数阶傅里叶变换（locally optimized multi-synchrosqueezing-short time fractional Fourier transform,LOMS-STFRFT）。该算法对短时分数阶傅里叶变换（short time fractional Fourier transform,STFRFT）进行局部旋转参数的优化选取，通过STFRFT得到时频分布投影在分数域上的时频系数矩阵；对时频系数矩阵进行瞬时频率估计和多次迭代；采用多重同步挤压算子对时频系数矩阵进行重排并通过局部模极大值法提取瞬时频率曲线。通过一个多分量信号数值算例和一个线性时变拉索试验验证了所提方法的精确性。研究结果表明：相比传统的多重同步挤压算法，LOMS-STFRFT针对时变结构非平稳信号的瞬时频率识别效果更佳。
Keyword ：
多重同步挤压变换 多重同步挤压变换 局部优化 局部优化 时变结构 时变结构 瞬时频率 瞬时频率 短时分数阶傅里叶变换 短时分数阶傅里叶变换
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| GB/T 7714 | 刘景良 , 戴逸宸 , 郑文婷 et al. 基于LOMS-STFRFT高精度识别时变结构非平稳响应信号的瞬时频率 [J]. | 振动工程学报 , 2025 , 38 (04) : 750-760 . |
| MLA | 刘景良 et al. "基于LOMS-STFRFT高精度识别时变结构非平稳响应信号的瞬时频率" . | 振动工程学报 38 . 04 (2025) : 750-760 . |
| APA | 刘景良 , 戴逸宸 , 郑文婷 , 廖飞宇 . 基于LOMS-STFRFT高精度识别时变结构非平稳响应信号的瞬时频率 . | 振动工程学报 , 2025 , 38 (04) , 750-760 . |
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本发明公开了一种基于改进YOLOv5模型量化清水混凝土表面气泡缺陷的语义分割方法，具体包含以下步骤：建立清水混凝土表面气泡缺陷的语义分割数据集；标记语义分割数据集，并将所述数据集划分为训练集、验证集；构建用于清水混凝土结构表面气泡缺陷识别的深度学习语义分割模型；训练和验证构建的语义分割模型；采用验证后的语义分割模型进行量化识别气泡缺陷；提取识别出图像中气泡缺陷的最大直径及面积。该方法无需昂贵的检测设备，模型能够实时量化清水混凝土表面气泡缺陷的程度。
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| GB/T 7714 | 徐诗涵 , 刘景良 , 郑文婷 et al. 基于YOLOv5模型量化清水混凝土表面气泡缺陷的语义分割方法 : CN202410901154.1[P]. | 2024-07-05 . |
| MLA | 徐诗涵 et al. "基于YOLOv5模型量化清水混凝土表面气泡缺陷的语义分割方法" : CN202410901154.1. | 2024-07-05 . |
| APA | 徐诗涵 , 刘景良 , 郑文婷 , 陈榕 , 梁宇斌 . 基于YOLOv5模型量化清水混凝土表面气泡缺陷的语义分割方法 : CN202410901154.1. | 2024-07-05 . |
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本发明公开一种基于改进YOLOv5模型的清水混凝土表面气泡缺陷目标检测方法，包括：1)收集目标数据集，包括通过网络查找、浇筑试块和现场采集，将其整理成清水混凝土表面气泡数据集；2)标记目标数据集，并将其划分为训练集、验证集和测试集；3)以YOLOv5模型为基础，对其进行改进，构建清水混凝土表面气泡缺陷目标检测模型；4)利用训练集和验证集对该模型进行训练和调参，直至模型的损失函数收敛，得到训练好的目标检测模型；5)将测试集的图像输入训练好的模型进行目标检测，输出模型检测结果。本发明通过对YOLOv5模型的改进，构建适用于混凝土表面气泡小目标检测的模型，利用该模型进行目标检测，能够提高小目标的检测能力，进一步提升检测精度。
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| GB/T 7714 | 刘景良 , 徐诗涵 , 廖飞宇 et al. 基于改进YOLOv5模型的清水混凝土表面气泡缺陷目标检测方法 : CN202410526103.5[P]. | 2024-04-29 . |
| MLA | 刘景良 et al. "基于改进YOLOv5模型的清水混凝土表面气泡缺陷目标检测方法" : CN202410526103.5. | 2024-04-29 . |
| APA | 刘景良 , 徐诗涵 , 廖飞宇 , 黄勇贵 , 郑文婷 . 基于改进YOLOv5模型的清水混凝土表面气泡缺陷目标检测方法 : CN202410526103.5. | 2024-04-29 . |
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Concrete-filled steel tube (CFST) members have been widely used in civil engineering due to their advanced mechanical properties. However, internal defects such as the concrete core voids and interface debonding in CFST structures are likely to weaken their load-carrying capacity and stiffness, which affects the safety and serviceability. Visualizing the inner defects of the concrete cores in CFST members is a critical requirement and a challenging task due to the obvious difference in the material mechanical parameters of the concrete core and steel tube in CFST members. In this study, a curved ray theory-based travel time tomography (TTT) with a least square iterative linear inversion algorithm is first introduced to quantitatively identify and visualize the sizes and positions of the concrete core voids in CFST members. Secondly, a numerical investigation of the influence of different parameters on the inversion algorithm for the defect imaging of CFST members, including the effects of the model weighting matrix, weighting factor and grid size on the void's imaging quality and accuracy, is carried out. Finally, an experimental study on six CFST specimens with mimicked concrete core void defects is performed in a laboratory and the mimicked defects are visualized. The results demonstrate that TTT can identify the sizes and positions of the concrete core void defects in CFST members efficiently with the use of optimal parameters.
Keyword ：
concrete-filled steel tube concrete-filled steel tube curved ray theory-based travel time tomography curved ray theory-based travel time tomography defect imaging defect imaging experimental study experimental study least square iterative linear inversion algorithm least square iterative linear inversion algorithm numerical study numerical study parameter analysis parameter analysis piezoelectric lead zirconate titanate piezoelectric lead zirconate titanate
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| GB/T 7714 | Zheng, Wenting , Xu, Bin , Xia, Zongjun et al. A Concrete Core Void Imaging Approach and Parameter Analysis of Concrete-Filled Steel Tube Members Using Travel Time Tomography: Multi-Physics Simulations and Experimental Studies [J]. | SENSORS , 2024 , 24 (8) . |
| MLA | Zheng, Wenting et al. "A Concrete Core Void Imaging Approach and Parameter Analysis of Concrete-Filled Steel Tube Members Using Travel Time Tomography: Multi-Physics Simulations and Experimental Studies" . | SENSORS 24 . 8 (2024) . |
| APA | Zheng, Wenting , Xu, Bin , Xia, Zongjun , Wang, Jiang , Liu, Jingliang , Yao, Yudi et al. A Concrete Core Void Imaging Approach and Parameter Analysis of Concrete-Filled Steel Tube Members Using Travel Time Tomography: Multi-Physics Simulations and Experimental Studies . | SENSORS , 2024 , 24 (8) . |
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鉴于恢复力曲面法(Restoring Force Surface, RFS)和随机森林(Random Forest, RF)模型在参数辨识领域的优越性，结合上述两种方法提出一种新的基于RFS-RF的局部非线性模型辨识方法。首先，针对局部非线性模型求解其动力响应。其次，根据获得的动力响应计算恢复力曲面与边际谱，然后再通过边际谱求解非线性指标。再次，通过多次改变结构的刚度和阻尼参数生成若干组非线性指标并建立随机森林模型。然后，将新的非线性指标作为预测集输入已经建立的随机森林模型并判断系统的非线性类型和非线性函数形式。最后，采用最小二乘法对局部非线性系统的待求参数进行精确识别。通过一个四层剪切型框架结构模型对所提方法进行验证，研究结果表明：基于RFS-RF的多自由度局部非线性模型辨识方法能够准确识别结构系统的非线性类型、函数形式以及未知参数。
Keyword ：
局部非线性 局部非线性 恢复力曲面 恢复力曲面 模型辨识 模型辨识 边际谱 边际谱 随机森林 随机森林
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| GB/T 7714 | 姜洋 , 马砚秋 , 陈榕 et al. RFS-RF的局部非线性模型辨识新方法 [J]. | 武夷学院学报 , 2024 , 43 (06) : 63-69 . |
| MLA | 姜洋 et al. "RFS-RF的局部非线性模型辨识新方法" . | 武夷学院学报 43 . 06 (2024) : 63-69 . |
| APA | 姜洋 , 马砚秋 , 陈榕 , 刘景良 , 张羲岭 . RFS-RF的局部非线性模型辨识新方法 . | 武夷学院学报 , 2024 , 43 (06) , 63-69 . |
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Civil engineering structures under ambient excitations are essentially time-varying and nonlinear structural systems and the resultant response signals exhibit non-stationarity. To reveal the time-varying characteristics of non-stationary signals, time frequency analysis methods with high resolutions are required. Although synchroextracting short time Fourier transform (SESTFT) is able to generate more energy concentrated time frequency representations and allow signal reconstruction, its major disadvantage is the window function is fixed. To address this issue, an enhanced synchroextracting wavelet transform (ESEWT) method is proposed to refine frequency bands by combing synchroextracting and continuous wavelet transform. After that, dynamic optimization (DO) is used to extract instantaneous frequency (IF) curves within the refined frequency bands. Two numerical examples and an experimental study case are investigated to illustrate the effectiveness and accuracy of the proposed method. The results demonstrate that the proposed ESEWT is capable of extracting frequency bands more accurately and its combination with DO identifies IFs of non-stationary signals better than current time frequency analysis methods such as SESTFT.
Keyword ：
Continuous wavelet transform Continuous wavelet transform dynamic optimization dynamic optimization instantaneous frequency instantaneous frequency synchroextracting transform synchroextracting transform wavelet ridge wavelet ridge
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| GB/T 7714 | Jiang, Yang , Wang, Xin-Yu , Zhang, Xi-Ling et al. Instantaneous frequency identification for nonstationary signals of time-varying structures using enhanced synchroextracting wavelet transform and dynamic optimization [J]. | JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL , 2024 , 43 (2) : 617-633 . |
| MLA | Jiang, Yang et al. "Instantaneous frequency identification for nonstationary signals of time-varying structures using enhanced synchroextracting wavelet transform and dynamic optimization" . | JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 43 . 2 (2024) : 617-633 . |
| APA | Jiang, Yang , Wang, Xin-Yu , Zhang, Xi-Ling , Zhang, Kai , Liu, Jing-Liang . Instantaneous frequency identification for nonstationary signals of time-varying structures using enhanced synchroextracting wavelet transform and dynamic optimization . | JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL , 2024 , 43 (2) , 617-633 . |
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