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Abstract ：
Civil structures are susceptible to performance deterioration during their service lives. Therefore, it is essential to periodically evaluate structural conditions for preventing potential catastrophic failure. Data-driven methods have been widely adopted for this purpose, which often utilize deep learning (DL) algorithms that generally require extensive training data considering various condition scenarios. However, finite element (FE) simulation data are practically affected by uncertainties such as modeling errors and operational environmental variations, limiting the applicability of supervised DL algorithms. Due to this, an unsupervised learning framework has been proposed for structural condition evaluation by establishing a correlation model for the response data measured at different locations of a healthy structure using bidirectional long short-term memory (BiLSTM) networks. During the training process, the optimal hyperparameters of a BiLSTM network is objectively, instead of 'subjectively', determined through Bayesian optimization (BO) without requiring labeled measurement data, improving the network generalization performance. During the testing process, response data from healthy and unknown scenarios are input into the BO-BiLSTM network, and the errors between the reconstructed and actual data are taken as the latent features. Then, the feature similarity between the unknown scenarios and the healthy structure is calculated using the Wasserstein distance as a structural condition indicator. The feasibility of the proposed method has been validated using the IASC-ASCE benchmark frame and an experimental steel frame, demonstrating that the proposed condition indicator is sensitive to structural damage and robust to different noise levels. As structural degradation developed, the condition indicators for the two frames increased from 0.090 and 0.583 to 1.182 and 0.825, respectively. The structural conditions were successfully evaluated without the labeled measurement data, validating the engineering applicability of the proposed method.
Keyword ：
Bayesian optimization Bayesian optimization Bidirectional long-short term memory networks Bidirectional long-short term memory networks Structural condition evaluation Structural condition evaluation Unsupervised learning Unsupervised learning Wasserstein distance Wasserstein distance
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| GB/T 7714 | Zheng, Jin-Ling , Fang, Sheng-En . Structural condition evaluation using unsupervised Bayesian optimized BiLSTM networks [J]. | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING , 2025 . |
| MLA | Zheng, Jin-Ling 等. "Structural condition evaluation using unsupervised Bayesian optimized BiLSTM networks" . | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2025) . |
| APA | Zheng, Jin-Ling , Fang, Sheng-En . Structural condition evaluation using unsupervised Bayesian optimized BiLSTM networks . | JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING , 2025 . |
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The increasing demand for sustainable construction materials has spurred interest in seawater sea-sand concrete (SWSSC) as a substitute for natural resources. However, SWSSC's durability faces challenges from aggressive chloride and sulfate ions existing in seawater, causing structural degradation. Therefore, this study has prepared seven SWSSC formulations with the different ultrafine metakaolin (UMK) and nano-TiO2 (NT) dosages using untreated seawater and sea sand. The SWSSC specimens fabricated using these formulations were evaluated through the wet-dry sulfate cycling, chloride permeability and water permeability tests to assess their durability performance. The advanced microstructural analyses, including the Fourier transform infrared spectroscopy, the scanning electron microscopy and the X-ray diffraction, were also employed to examine the effects of the UMK and NT on the pore refinement, the phase evolution and the functional group changes within the concrete matrix. The test results have revealed that a combined addition of 15 wt% UMK and 0.5 wt% NT significantly reduced the chloride ion permeation and the water permeability, enhancing the initial impermeability of the modified concrete. However, after the 60 cycles of sulfate exposure, the specimens with the 15 wt% UMK addition (with or without NT) lost their strengths, while the unmodified concrete specimens retained the higher residual strengths. The formulation with the addition of 5 wt% UMK and 0.5 wt% NT demonstrated the resistance improvement up to the 60 cycles, although the specimen strength was slightly lower than that of unmodified SWSSC specimen. These findings highlighted the need for optimizing the UMK and NT dosages in order to balance initial impermeability and long-term durability of SWSSC.
Keyword ：
Durability Durability Fourier transform infrared spectroscopy Fourier transform infrared spectroscopy Nano-TiO2 Nano-TiO2 Scanning electron microscopy Scanning electron microscopy Seawater sea-sand concrete Seawater sea-sand concrete Ultrafine metakaolin Ultrafine metakaolin X-ray diffraction X-ray diffraction
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| GB/T 7714 | Luo, Qing-Hai , Fang, Sheng-En . Influence of ultrafine metakaolin and nano-TiO2 on the durability and microstructure of seawater sea- sand concrete [J]. | CONSTRUCTION AND BUILDING MATERIALS , 2025 , 473 . |
| MLA | Luo, Qing-Hai 等. "Influence of ultrafine metakaolin and nano-TiO2 on the durability and microstructure of seawater sea- sand concrete" . | CONSTRUCTION AND BUILDING MATERIALS 473 (2025) . |
| APA | Luo, Qing-Hai , Fang, Sheng-En . Influence of ultrafine metakaolin and nano-TiO2 on the durability and microstructure of seawater sea- sand concrete . | CONSTRUCTION AND BUILDING MATERIALS , 2025 , 473 . |
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To improve the mode decomposition capacity for underdetermined and unclear modes, a modified blind source separation (MBSS) method has been proposed, where a multi-synchroextracting transform algorithm with a sliding window is proposed for a higher sparsity time-frequency spectrum. The proposed transform algorithm incorporates an iterative formula of the instantaneous frequency with a sliding window. Then, it is embedded into the existing novel blind source separation (NBSS) method to highly improve the modal decomposition accuracy. The feasibility of the proposed method has been verified against a numerical 3DOF mass-spring-damper system, a numerical three-story frame structure, and an experimental five-story steel frame. The analysis results demonstrate that the proposed MBSS method can well decompose the acceleration signals, providing better precisions than the NBSS method under the circumstance of unclear and underdetermined modes. Moreover, the proposed method has higher decomposition accuracy for close modes.
Keyword ：
Mode decomposition Mode decomposition Modified blind source separation Modified blind source separation Multi-synchroextracting transform Multi-synchroextracting transform Sparse matrix Sparse matrix Structural modal analysis Structural modal analysis
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| GB/T 7714 | Li, Yu-Zu , Fang, Sheng-En . A modified blind source separation algorithm for underdetermined structural modal analysis [J]. | ENGINEERING STRUCTURES , 2025 , 325 . |
| MLA | Li, Yu-Zu 等. "A modified blind source separation algorithm for underdetermined structural modal analysis" . | ENGINEERING STRUCTURES 325 (2025) . |
| APA | Li, Yu-Zu , Fang, Sheng-En . A modified blind source separation algorithm for underdetermined structural modal analysis . | ENGINEERING STRUCTURES , 2025 , 325 . |
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Abstract ：
Safety evaluation is a pivotal issue for operational civil structures during their service lives. Recently, deep learning-based evaluation strategies have emerged, and such methods often require a substantial amount of training samples to prevent overfitting. However, this precondition is often difficult to satisfy in practice due to insufficient samples. Hence, a Bayesian ensemble neural network (BENN) has been proposed to overcome this drawback. Firstly, the network parameters of a Bayesian neural network (BNN) are established on probability distribution estimation to consider the uncertainties in a structure, which is divided into several substructures for evaluation. A multiple sampling strategy on the network parameter distributions yields different deterministic NNs. Secondly, the Bagging ensemble learning has been adopted to treat a BNN as a base learner, whose prediction will be used for an ensemble prediction of a substructure. A BENN is actually the ensemble of several BNNs (base learners). Specifically, the membership degree of each base learner's predictions is calculated and normalized to derive the corresponding weight. The ensemble prediction is obtained through the weighted summation of the predictions of all base learners. Meanwhile, the entropy that measures the structural uncertainty of each substructure, with corresponding weights calculated via the entropy weight method to construct an overarching structural state indicator. The effectiveness of the BENNs is validated through the numerical simulations and practical experiments conducted on a frame structure. As the structural degradation increased, the state indicator decreased from 16.88 to 14.59 for the numerical frame, as well as from 16.93 to 16.23 for the experimental frame.
Keyword ：
Bagging ensemble learning Bagging ensemble learning Bayesian ensemble neural network Bayesian ensemble neural network Entropy weight method Entropy weight method Structural safety evaluation Structural safety evaluation Variational inference Variational inference
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| GB/T 7714 | Zheng, Jin-Ling , Fang, Sheng-En . Structural safety evaluation using Bayesian ensemble neural networks [J]. | ENGINEERING STRUCTURES , 2025 , 328 . |
| MLA | Zheng, Jin-Ling 等. "Structural safety evaluation using Bayesian ensemble neural networks" . | ENGINEERING STRUCTURES 328 (2025) . |
| APA | Zheng, Jin-Ling , Fang, Sheng-En . Structural safety evaluation using Bayesian ensemble neural networks . | ENGINEERING STRUCTURES , 2025 , 328 . |
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Abstract ：
The cables of a cable-stayed bridge are susceptible to structural degradation due to environmental corrosion and fatigue, which directly affects the safety and operational performance of the bridge. As the process of the degradation in practice is very slow, it is difficult to be monitored during the bridge service life. Hence, this study aims to develop a novel semi-Markov process based digital twin (DT) framework for safety evaluation of cable- stayed bridges considering cable corrosion. The framework encompasses a physical twin layer, a DT layer and the information interaction medium. The physical twin layer mainly comprises the bridge physical entity and its associated monitoring system that provides a variety of perceptual data for DT modeling. In the DT layer, the DT model acts as a virtual counterpart of the physical bridge for mirroring and forecasting the bridge's mechanical behaviors. The information interaction medium plays a crucial role in the bidirectional information communication between the physical and digital twin layers. Two types of information interaction media have been utilized including a cable force influence matrix and a semi-Markov process. The former enables updating the DT model to precisely match the data measured from the physical bridge. Meanwhile, the semi-Markov process depicts the probability of the bridge's condition considering the cable corrosion during the different service periods. The proposed procedure can predict the bridge state and evaluate the safety by comparing the predicted state with the monitored values. The proposed framework has been successfully validated on a real-world cable- stayed bridge. The results showed the proposed DT framework was reliable and effective for evaluating the bridge condition.
Keyword ：
A semi-Markov process A semi-Markov process Cable force influence matrix Cable force influence matrix Cable-stayed bridges Cable-stayed bridges Digital twin Digital twin Safety evaluation Safety evaluation
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| GB/T 7714 | Guo, Xin-Yu , Fang, Sheng-En , Zhu, Xinqun et al. A semi-Markov process based digital twin for safety evaluation of cable-stayed bridges with cable corrosion [J]. | ADVANCED ENGINEERING INFORMATICS , 2025 , 65 . |
| MLA | Guo, Xin-Yu et al. "A semi-Markov process based digital twin for safety evaluation of cable-stayed bridges with cable corrosion" . | ADVANCED ENGINEERING INFORMATICS 65 (2025) . |
| APA | Guo, Xin-Yu , Fang, Sheng-En , Zhu, Xinqun , Li, Jianchun . A semi-Markov process based digital twin for safety evaluation of cable-stayed bridges with cable corrosion . | ADVANCED ENGINEERING INFORMATICS , 2025 , 65 . |
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Abstract ：
Extracting sensitive damage features from structural response signals is crucial for damage identification methods based on pattern classification. To this end，a hybrid network that combines a deep belief networks（DBN）and a long-short term memory（LSTM）network is proposed through a hybrid learning mechanism to utilize the merits of both networks in the aspects of extracting high-order abstract features and considering data sequence correlations. First，transmissibility data from response signals are sequentially input into the DBN to achieve the initial data compression and feature extraction，reducing the redundant information in the responses. Then，the extracted feature sequences are input into the LSTM network to consider the correlation between the different responses for acquiring the relevant sensitive damage features. Finally，a classification layer with the Softmax function is used to classify the features output by the LSTM network. Thereby，different structural damage patterns can be identified. The damage identification results on a three-dimensional experimental steel frame demonstrate that the hybrid learning mechanism can better train the network parameters，and the fine-tuning on the whole hybrid network contributes to the subsequent damage feature classification. Under the pollution of numerical or measured noises，the hybrid network can still effectively perform the data compression，feature extraction and classification. The various damage scenarios of the experimental frame are well identified. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.
Keyword ：
Structural frames Structural frames
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| GB/T 7714 | Fang, Sheng-En , Liu, Yang . Structural damage identification via a deep belief memory network [J]. | Journal of Vibration Engineering , 2024 , 37 (11) : 1917-1924 . |
| MLA | Fang, Sheng-En et al. "Structural damage identification via a deep belief memory network" . | Journal of Vibration Engineering 37 . 11 (2024) : 1917-1924 . |
| APA | Fang, Sheng-En , Liu, Yang . Structural damage identification via a deep belief memory network . | Journal of Vibration Engineering , 2024 , 37 (11) , 1917-1924 . |
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The key to damage pattern recognition lies in digging and classifying damage features from the response data of civil structures. To this end，a stack auto-encoder network with several auto-encoder hidden layers and a Softmax classification layer is built for analyzing frame structures. A hybrid learning mechanism is adopted to combining unsupervised and supervised learning strategies. Finite element analysis is used to generate the transmissibility function samples corresponding to different scenarios of a frame structure. The transmissibility samples are then divided into training，validation，and test sets. The parameters of the auto-encoder hidden layers，such as the weights and bias，are determined by a pre-training strategy in order to avoid the phenomenon of network over fitting. A fine-tuning step is employed to adjust the pre-trained network parameters，and the network hyper parameters are further adjusted based on the validation set. The measured transmissibility data are input into the network to evaluate the damage of the frame structure. The analysis results show that the proposed method can effectively extract and classify the damage features. Both the single and double damage scenarios at the frame joints were identified with higher accuracy and better anti-noise ability than the traditional shallow neural network. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.
Keyword ：
damage identification damage identification frame structure frame structure hybrid learning mechanism hybrid learning mechanism stacked auto-encoder stacked auto-encoder transmissibility functions transmissibility functions
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| GB/T 7714 | Fang, S.-E. , Liu, Y. , Zhang, X.-H. . Structural damage identification incorporating transmissibility functions with stacked auto-encoders; [结 合 传 递 比 与 栈 式 自 编 码 器 的 结 构 损 伤 识 别] [J]. | Journal of Vibration Engineering , 2024 , 37 (9) : 1460-1467 . |
| MLA | Fang, S.-E. et al. "Structural damage identification incorporating transmissibility functions with stacked auto-encoders; [结 合 传 递 比 与 栈 式 自 编 码 器 的 结 构 损 伤 识 别]" . | Journal of Vibration Engineering 37 . 9 (2024) : 1460-1467 . |
| APA | Fang, S.-E. , Liu, Y. , Zhang, X.-H. . Structural damage identification incorporating transmissibility functions with stacked auto-encoders; [结 合 传 递 比 与 栈 式 自 编 码 器 的 结 构 损 伤 识 别] . | Journal of Vibration Engineering , 2024 , 37 (9) , 1460-1467 . |
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Abstract ：
Traditional modeling approaches are difficult to reflect the slight changes of bridge system parameters and responses. Due to this，digital twins are adopted as the high fidelity mapping models for a bridge system. Firstly，the definition of digital twins comprises three parts of a physical twin layer，a digital twin layer and an information interaction medium. The digital twin model inside the digital twin layer is the virtual mapping of the bridge physical entity，and the real-time information transmission between the two layers is achieved by the information interaction medium. Secondly，in view of practical applications，three modeling principles of structural informatization，information digitization and data modelization are proposed to realize the informatization and visualization of the bridge physical entity. Thereby，the digital twin model with high fidelity is established for the cable-stayed bridge. Lastly，the monitoring data of a back-stay cable of an actual bridge are adopted as the perceptual information，and the changed cable parameters are fed to the digital twin model for twin model updating and response prediction. The analyses results demonstrate that the proposed digital twin modeling method can effectively reflect the parameter changes of the actual bridge. Then the corresponding slight variations of the cable force，the tower top deviation and the mid-span deflection of the main girder are predicted by the twin model. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.
Keyword ：
Cables Cables Cable stayed bridges Cable stayed bridges Data visualization Data visualization Mapping Mapping
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| GB/T 7714 | Fang, Shengen , Guo, Xinyu . System Updating and Response Prediction of a Cable-Stayed Bridge Based on Digital Twins [J]. | Journal of Vibration, Measurement and Diagnosis , 2024 , 44 (1) : 11-17 and 193 . |
| MLA | Fang, Shengen et al. "System Updating and Response Prediction of a Cable-Stayed Bridge Based on Digital Twins" . | Journal of Vibration, Measurement and Diagnosis 44 . 1 (2024) : 11-17 and 193 . |
| APA | Fang, Shengen , Guo, Xinyu . System Updating and Response Prediction of a Cable-Stayed Bridge Based on Digital Twins . | Journal of Vibration, Measurement and Diagnosis , 2024 , 44 (1) , 11-17 and 193 . |
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Vulnerability analysis of long-span structures explores the weakness regions, which should be concerned during structural design and operation stages. However, the vulnerability analysis procedure under regular loads is conventionally a tough task in virtue of structural complexity and uncertainties. Therefore, a Bayesian networks (BNs) framework has been developed for hierarchical vulnerability evaluation of long-span structures under regular loads. External loads, structural systems and components are defined as network nodes, and mechanical and risk causalities are simultaneously considered during network establishment. The causality strength between two nodes is quantitatively expressed by a conditional probability table. The state probability inference of nodal variables is accomplished after inputting the observed state of a damaged component as the evidence into the established BN. A new component importance coefficient is defined and calculated on the inferred nodal state probabilities. A component vulnerability index is further defined to predict the most likely failure sequence of components. In addition, a system vulnerability measure is proposed for evaluating the safety risk of the system. The proposed method has been verified against an experimental space truss model and an actual cable-stayed bridge. The component importance of the truss members and the cables was well evaluated with the predictions of their most likely failure sequences. The estimated system vulnerability could indicate the safety risk of the long-span structures due to the damaged components
Keyword ：
Bayesian Networks Bayesian Networks Component importance coefficient Component importance coefficient Component vulnerability index Component vulnerability index Hierarchical vulnerability evaluation Hierarchical vulnerability evaluation System vulnerability measure System vulnerability measure
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| GB/T 7714 | Fang, Sheng-En , Yu, Qi-Kang . Bayesian networks based hierarchical vulnerability evaluation of long-span structures [J]. | ENGINEERING STRUCTURES , 2024 , 306 . |
| MLA | Fang, Sheng-En et al. "Bayesian networks based hierarchical vulnerability evaluation of long-span structures" . | ENGINEERING STRUCTURES 306 (2024) . |
| APA | Fang, Sheng-En , Yu, Qi-Kang . Bayesian networks based hierarchical vulnerability evaluation of long-span structures . | ENGINEERING STRUCTURES , 2024 , 306 . |
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为实现结构易损性的智能化推理，提出以贝叶斯网络(Bayesian networks, BNs)重构桁架结构的易损性分析体系。首先，分别定义外荷载、体系和杆件为网络顶层父节点、中间节点和底层子节点，节点间通过有向弧表示因果关系，形成BN拓扑；接着，提出以杆件“大损伤”替代传统的“概念移除”方式，考虑杆件参数及外荷载的不确定性，结合抽样实现对节点间条件概率表的学习，完成BN的构建；然后将特定杆件的观测状态作为证据输入BN,同步推理其他杆件的节点状态概率，进而计算杆件的重要性系数，并定义所有杆件的重要性系数之和为体系的易损性系数；最后，提出杆件易损指标，作为预测桁架体系最可能失效路径的依据。研究结果表明：所提方法能更合理地评价各杆件在体系中的重要性，推理得到的杆件破坏路径与试验观测一致，所计算的试验桁架体系易损性系数远小于杆件数目，表明当前荷载组合下特定杆件的损伤引起体系发生连续性倒塌的可能性较小。
Keyword ：
体系易损性系数 体系易损性系数 杆件易损指标 杆件易损指标 杆件重要性系数 杆件重要性系数 结构工程 结构工程 贝叶斯网络 贝叶斯网络
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| GB/T 7714 | 方圣恩 , 俞其康 , 张笑华 et al. 基于贝叶斯网络的结构易损性智能推理 [J]. | 自然灾害学报 , 2024 , 33 (03) : 130-136 . |
| MLA | 方圣恩 et al. "基于贝叶斯网络的结构易损性智能推理" . | 自然灾害学报 33 . 03 (2024) : 130-136 . |
| APA | 方圣恩 , 俞其康 , 张笑华 , 林友勤 . 基于贝叶斯网络的结构易损性智能推理 . | 自然灾害学报 , 2024 , 33 (03) , 130-136 . |
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