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学者姓名：方圣恩
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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 等. "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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由于传统建模方法难以反馈桥梁系统参数和响应的细微变化，为此提出了以数字孪生体作为结构系统的高保真映射模型。首先，定义数字孪生体包含物理孪生层、数字孪生层和信息交互媒介3部分，数字孪生层的孪生模型是对斜拉桥物理实体的虚拟映射，通过信息交互媒介实现不同层间信息的实时传递；其次，针对具体应用提出了结构信息化、信息数据化和数据模型化3条建模准则，实现对斜拉桥物理实体的信息勾勒和可视化过程，建立高保真的斜拉桥数字孪生模型；最后，以一座实桥端锚索的监测数据为感知信息，将变化的索参数实时反馈给孪生模型，实现模型更新和响应预测。研究结果表明，所提出的数字孪生建模方法能及时反馈实桥的参数变化，并预测由此造成的索力、塔顶偏位及主梁跨中挠度的细微改变。
Keyword ：
信息交互媒介 信息交互媒介 孪生模型更新 孪生模型更新 建模框架和准则 建模框架和准则 数字孪生体 数字孪生体 斜拉桥 斜拉桥
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| GB/T 7714 | 方圣恩 , 郭新宇 . 结合数字孪生的斜拉桥系统更新和响应预测 [J]. | 振动.测试与诊断 , 2024 , 44 (01) : 11-17,193 . |
| MLA | 方圣恩 等. "结合数字孪生的斜拉桥系统更新和响应预测" . | 振动.测试与诊断 44 . 01 (2024) : 11-17,193 . |
| APA | 方圣恩 , 郭新宇 . 结合数字孪生的斜拉桥系统更新和响应预测 . | 振动.测试与诊断 , 2024 , 44 (01) , 11-17,193 . |
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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 等. "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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钢拱塔斜拉桥的受力体系与传统斜拉桥有所不同，为研究环境温度变化对这种异形桥塔斜拉桥主要受力部件的影响，以某钢拱塔斜拉桥为工程背景，首先基于在线监测获取的环境和部件温度数据，分析斜拉索索力、拱塔倾角和主梁应变的温度时变效应；然后以斜拉索为研究对象，通过该桥的有限元模型升降温模拟，分析各部件温差引起的温度耦合效应对拉索索力的影响；最后以环境温度、主梁温度、桥塔温度为输入，索力为输出，利用长短期记忆神经网络对实测索力-温度数据进行映射，实现数据压缩和特征提取，建立温度-索力预测模型，再对网络模型输入新的温度监测数据，以预测索力。研究结果表明：主梁和钢拱塔温度变化具有周期性，且滞后于环境温度；主梁应变与环境温度的变化趋势基本一致但具有一定的滞后性，环境温度变化对拱塔倾角的影响很小且没有周期性规律；索力与环境温度呈线性负相关，且需要考虑斜拉桥各部件的温差所引起的温度耦合效应；长短期记忆神经网络对带有时序特性的数据训练效果好，建立的温度-索力关系模型准确度高，可用于该桥索力的实时预测。
Keyword ：
桥梁工程 桥梁工程 温度耦合效应 温度耦合效应 索力预测 索力预测 钢拱塔斜拉桥 钢拱塔斜拉桥 长短期记忆神经网络 长短期记忆神经网络
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| GB/T 7714 | 方圣恩 , 秦劲东 , 张玮 et al. 钢拱塔斜拉桥的温度耦合效应和索力预测 [J]. | 土木与环境工程学报(中英文) , 2024 , 46 (02) : 146-153 . |
| MLA | 方圣恩 et al. "钢拱塔斜拉桥的温度耦合效应和索力预测" . | 土木与环境工程学报(中英文) 46 . 02 (2024) : 146-153 . |
| APA | 方圣恩 , 秦劲东 , 张玮 , 江星 . 钢拱塔斜拉桥的温度耦合效应和索力预测 . | 土木与环境工程学报(中英文) , 2024 , 46 (02) , 146-153 . |
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The nature of damage identification is close to a pattern recognition process that classifies different damage patterns. The na & iuml;ve Bayes classifier (NBC) can effectively handle multiple-classification problems by choosing patterns with high probabilities. Therefore, by absorbing the autoregressive model with exogenous inputs (the ARX model), an ARX-Na & iuml;ve Bayes damage identification strategy has been proposed in which the autoregressive coefficients of the ARX model are taken as the sensitive damage feature. The classification training and test sample datasets are then built on these coefficients corresponding to various damage scenarios. The model order of an AR model is first determined for the subsequent order selection of the ARX model, whose autoregressive coefficients are further used to construct the NBC. This procedure can enhance the pattern recognition robustness to uncertainties such as measurement noises. Different damage patterns are determined by calculating the sum of logarithmic likelihoods of testing samples. The effectiveness of the proposed method has been verified against a bridge benchmark model having different damage scenarios under the noise pollution. In addition, an experimental five-story shear frame structure was adopted for validation, showing that compared with the SVM algorithm suitable for handling binary classification problems, the proposed method excels in multi-classification of damage patterns.
Keyword ：
damage patterns damage patterns na & iuml;ve bayesian classifier na & iuml;ve bayesian classifier Structural damage identification Structural damage identification the ARX model the ARX model uncertainties uncertainties
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| GB/T 7714 | Zheng, Jin-Ling , Fang, Sheng-En , Wang, Si-Rong . ARX-Naïve Bayes based structural damage identification [J]. | NONDESTRUCTIVE TESTING AND EVALUATION , 2024 . |
| MLA | Zheng, Jin-Ling et al. "ARX-Naïve Bayes based structural damage identification" . | NONDESTRUCTIVE TESTING AND EVALUATION (2024) . |
| APA | Zheng, Jin-Ling , Fang, Sheng-En , Wang, Si-Rong . ARX-Naïve Bayes based structural damage identification . | NONDESTRUCTIVE TESTING AND EVALUATION , 2024 . |
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Cable force identification is crucial for ensuring the safety and operational performance of in-service long-span bridge structures. Besides the commonly-used frequency measurements for calculating cable forces using frequency-cable force relationship formulas, more efficient and straightforward identification could be achieved by directly utilizing frequency response functions (FRFs). This study presents a novel approach that employs neural networks to model the relationship between the FRFs and cable forces, resulting in a more streamlined method for identifying cable forces on long-span bridges. Firstly, the working mechanism of an auto-encoder is merged with the unique characteristics of the FRFs, giving the cross signature assurance criterion. This criterion is then integrated into the loss function as a constraint to account for the poor interpretability of pure data-driven methodology in solving engineering problems, leading to a grey-box data-driven paradigm. Following this paradigm, a physics-informed auto-encoder (PIAE) network is employed to reduce the dimensionality of the FRF data during extracting key features. The reduced FRF data are paired with the cable forces to form training samples. The PIAE network is then trained directly on these samples for the purpose of cable force identification. Finally, the validation of the proposed method was conducted on the actual monitoring data from a cable-stayed bridge and a concrete-filled steel tubular arch bridge. Results indicate that the proposed method achieves not only high prediction accuracy, but also a good fit between the predicted and actual developmental trends of cable forces, and is well-suited for the different types of bridges.
Keyword ：
Bridge structures Bridge structures Cable force identification Cable force identification Cross signature assurance criterion Cross signature assurance criterion Grey running mechanism Grey running mechanism Physics -informed auto -encoder Physics -informed auto -encoder
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| GB/T 7714 | Guo, Xin-Yu , Fang, Sheng-En . A physics-informed auto-encoder based cable force identification framework for long-span bridges [J]. | STRUCTURES , 2024 , 60 . |
| MLA | Guo, Xin-Yu et al. "A physics-informed auto-encoder based cable force identification framework for long-span bridges" . | STRUCTURES 60 (2024) . |
| APA | Guo, Xin-Yu , Fang, Sheng-En . A physics-informed auto-encoder based cable force identification framework for long-span bridges . | STRUCTURES , 2024 , 60 . |
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Structural modal identification is generally implemented within a dynamic framework, requiring multidisciplinary knowledge and adequate operational experience. This preliminary study attempts to explore an easy-to-handle quasi-static alternative method for dynamics-based modal identification of beam-type structures. Acceleration time-history data are replaced by quasi-static deflections induced by slow moving loads. A concept of quasi-static deflection influence surface is proposed based on the theory of influence lines and the principle of virtual work. Its analytical expression is simplified into a deflection matrix by choosing specific points on the surface according to deflection measurement coordinates. The matrix form is divided by external loads to obtain a generalised quasi-static flexibility matrix, which is used to replace the inversion of the global stiffness matrix in the modal eigenvalue equation. The lumped-mass method is also employed to establish the mass matrix. Subsequently, the eigenvalue equation is analytically solved seeking for modal frequencies and mode shapeswithout performing modal tests. The feasibility of the proposed method has been successfully verified against three experimental examples including a continuous box girder with variable cross sections. It was observed that the modal frequencies and mode shapes estimated by the proposed method were very close to those given by the dynamically modal tests.
Keyword ：
generalised quasi-static flexibility matrix generalised quasi-static flexibility matrix modal eigenvalue equation modal eigenvalue equation modal parameter estimation modal parameter estimation quasi-static deflection influence surface quasi-static deflection influence surface Structural dynamics Structural dynamics
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| GB/T 7714 | Fang, Sheng-En , Huang, Ji-Yuan , Zhang, Xiao-Hua . Quasi-static testing based structural modal parameter estimation [J]. | NONDESTRUCTIVE TESTING AND EVALUATION , 2024 . |
| MLA | Fang, Sheng-En et al. "Quasi-static testing based structural modal parameter estimation" . | NONDESTRUCTIVE TESTING AND EVALUATION (2024) . |
| APA | Fang, Sheng-En , Huang, Ji-Yuan , Zhang, Xiao-Hua . Quasi-static testing based structural modal parameter estimation . | NONDESTRUCTIVE TESTING AND EVALUATION , 2024 . |
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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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In most real-world cases, an in-service structure doesn't always follow the two-state hypothesis under which the structure stays at an intact or completely failed state. Structural failure is sometimes regarded as a fuzzy event, and the actual failure boundary has a certain level of ambiguity that affects the structural limit state function under a fuzzy failure criterion. Under such circumstance, structural reliability should be solved within a hybrid reliability analysis framework involving the coupled effect of randomness and fuzziness. A fuzzy Bayesian interval estimation strategy has been proposed for this purpose. Structural parameters and external loads having fuzziness are decomposed and extended to fuzzy sets. The interval bounds of the distribution characteristics of the fuzzy parameters and loads are estimated using the fuzzy Bayesian estimation. Then an equivalent performance function is defined considering the fuzziness of the failure criterion. After that, the failure probability is computed under different interval combinations. The structural failure probability is expressed by an interval, instead of a traditional deterministic value. The solution process provides a better estimation of failure boundaries taking into account parameter ambiguities. The proposed method has been successfully verified against a plane steel frame structure and the IASC-ASCE benchmark test frame. It was found that the estimated failure probability intervals embraced the deterministic value predicted by the Monte Carlo simulation.
Keyword ：
Equivalent performance function Equivalent performance function Failure probability interval Failure probability interval Fuzzy Bayesian estimation Fuzzy Bayesian estimation Fuzzy failure criterion Fuzzy failure criterion Hybrid reliability analysis Hybrid reliability analysis
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| GB/T 7714 | Fang, Sheng-En , Zheng, Jin-Ling , Wang, Si-Rong . Hybrid reliability analysis of structures using fuzzy Bayesian interval estimation [J]. | ENGINEERING STRUCTURES , 2024 , 307 . |
| MLA | Fang, Sheng-En et al. "Hybrid reliability analysis of structures using fuzzy Bayesian interval estimation" . | ENGINEERING STRUCTURES 307 (2024) . |
| APA | Fang, Sheng-En , Zheng, Jin-Ling , Wang, Si-Rong . Hybrid reliability analysis of structures using fuzzy Bayesian interval estimation . | ENGINEERING STRUCTURES , 2024 , 307 . |
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In damage identification of civil structures, training deep neural networks (DNNs) often requires a large volume of annotated training data. However, artificial damage to real-world structures is always forbidden, resulting in insufficient data for training. Transfer learning provides a solution that allows structural damage information in a data-rich domain to be transferred and shared as the prior knowledge to a data-scarce domain, thereby indirectly augmenting available training data in the latter domain. To this end, a multi-task transfer learning strategy has been adopted for the purpose of achieving cross-domain damage identification between a plane frame in the source domain and a three-dimensional frame in the target domain. The strategy can share the learning knowledge from the domain with sufficient training data to the target domain with insufficient data. Thereby, the DNN (named DNN#2) in the target domain can be trained on a small amount of training data. Owing to their ability in data feature extraction, stacked auto-encoders (SAEs) are used to construct the desirable DNN#1 and DNN#2 corresponding to different damage identification tasks, named Task#1 and Task#2, in the two domains. The two DNNs share the hidden layers in order to share damage feature information between the source and target domains. The training datasets of the two domains are first used to jointly pre-train the auto-encoders' parameters in an unsupervised learning way. Afterwards, a supervised fine-tuning step is carried out to retraining the entire SAEs for better performance. By these means, Task#2 receives some prior knowledge from Task#1, and thus, it is accomplished on limited training data when Task#1 is synchronously achieved. The analysis results demonstrate that the proposed multi-task learning strategy requires only a single training session to simultaneously realize the cross-domain damage identification of two different steel frames.
Keyword ：
Cross-domain damage identification Cross-domain damage identification Fine-tuning Fine-tuning Frame structure Frame structure Multi-task learning Multi-task learning Transfer learning Transfer learning
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| GB/T 7714 | Liu, Yang , Fang, Sheng-En . Cross-domain structural damage identification using transfer learning strategy [J]. | ENGINEERING STRUCTURES , 2024 , 311 . |
| MLA | Liu, Yang et al. "Cross-domain structural damage identification using transfer learning strategy" . | ENGINEERING STRUCTURES 311 (2024) . |
| APA | Liu, Yang , Fang, Sheng-En . Cross-domain structural damage identification using transfer learning strategy . | ENGINEERING STRUCTURES , 2024 , 311 . |
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