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CONTENTS
Volume 33, Number 1, January 2024
 

Abstract
The investigation on vehicle-bridge coupling system (VBCS) is crucial in bridge design, bridge condition evaluation, and vehicle overload control. A real-time hybrid testing (RTHT) method for VBCS (RTHT-VBCS) is proposed in this paper for accurately and economically disclosing the dynamic performance of VBCSs. In the proposed method, one of the carriages is chosen as the experimental substructure loaded by servo-hydraulic actuator loading system in the laboratory, and the remaining carriages as well as the bridge structure are chosen as the numerical substructure numerically simulated in one computer. The numerical substructure and the experimental substructure are synchronized at their coupling points in terms of force equilibrium and deformation compatibility. Compared to the traditional iteration experimental method and the numerical simulation method, the proposed RTHT-VBCS method could not only obtain the dynamic response of VBCS, but also economically analyze various working conditions. Firstly, the theory of RTHT-VBCS is proposed. Secondly, numerical models of VBCS for RTHT method are presented. Finally, the feasibility and accuracy of the RTHT-VBCS are preliminarily validated by real-time hybrid simulations (RTHSs). It is shown that, the proposed RTHT-VBCS is feasible and shows great advantages over the traditional methods, and the proposed models can effectively represent the VBCS for RTHT method in terms of the force equilibrium and deformation compatibility at the coupling point. It is shown that the results of the single-degree-offreedom model and the train vehicle model are match well with the referenced results. The RTHS results preliminarily prove the effectiveness and accuracy of the proposed RTHT-VBCS.

Key Words
hybrid simulation; numerical model; real-time hybrid simulation; real-time hybrid testing method; vehiclebridge coupling system

Address
(1) Guoshan Xu, Yutong Jiang, Zhipeng Liu:
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China;
(2) Guoshan Xu, Yutong Jiang:
Key Lab of Structures Dynamic Behavior and Control, Ministry of Education, Harbin Institute of Technology, Harbin 150090, China;
(3) Guoshan Xu, Yutong Jiang:
Key Lab of Intelligent Disaster Mitigation, Ministry of Industry and Information Technology, Harbin 150090, China;
(4) Xizhan Ning:
College of Civil Engineering, Huaqiao University, Xiamen 361021, China.

Abstract
The new metro crossing the existing metro will cause the settlement or floating of the existing structures, which will have safety problems for the operation of the existing metro and the construction of the new metro. Therefore, it is necessary to monitor and predict the settlement of the existing metro caused by the construction of the new metro in real time. Considering the complexity and uncertainty of metro settlement, a Gaussian Prior Bayesian Emulator (GPBE) probability prediction model based on Bayesian optimization (BO) is proposed, that is, BO-GPBE. Firstly, the settlement monitoring data are analyzed to get the influence of the new metro on the settlement of the existing metro. Then, five different acquisition functions, that is, expected improvement (EI), expected improvement per second (EIPS), expected improvement per second plus (EIPSP), lower confidence bound (LCB), probability of improvement (PI) are selected to construct BO model, and then BO-GPBE model is established. Finally, three years settlement monitoring data were collected by structural health monitoring (SHM) system installed on Nanjing Metro Line 10 are employed to demonstrate the effectiveness of BO-GPBE for forecasting the settlement.

Key Words
Bayesian emulator; Bayesian optimization; Gaussian prior; settlement probability prediction; structural health monitoring

Address
(1) Yang Ding:
Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China;
(2) Yang Ding:
Zhejiang Engineering Research Center of Intelligent Urban Infrastructure, Hangzhou City University, 310015, China;
(3) Yang Ding:
Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province, Hangzhou City University, Hangzhou, 310015, China;
(4) Yu-Jun Wei:
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China;
(5) Pei-Sen Xi, Peng-Peng Ang:
Zhejiang Engineering Research Center of Smart Rail Transportation, Power China Huadong Engineering Corporation Limited, Hangzhou 311100, China;
(6) Zhen Han:
Nanjing Metro Operation Co., Ltd., Nanjing, Jiangsu, 210012, China.

Abstract
The effectiveness of conventional tuned liquid dampers (TLDs) in controlling the wind-induced response of tall flexible structures has been indicated. However, the impaired control effect in the detuning condition or a considerably high mass cost of liquid may be incurred in ensuring the high-level serviceability. To provide an efficient TLD-based solution for wind-induced vibration control, this study proposes a serviceability-oriented optimal design method for isolated TLDs (ILDs) and derives analytical design formulae. The ILD is implemented by mounting the TLD on the linear isolators. Stochastic response analysis is performed for the ILD-equipped structure subjected to stochastic wind and white noise, and the results are considered to derive the closed-form responses. Correspondingly, an extensive parametric analysis is conducted to clarify a serviceability-oriented optimal design framework by incorporating the comfort demand. The obtained results show that the highlevel serviceability demand can be satisfied by the ILD based on the proposed optimal design framework. Analytical design formulae can be preliminarily adopted to ensure the target serviceability demand while enhancing the structural displacement performance to increase the safety level. Compared with conventional TLD systems, the ILD exhibits higher effectiveness and a larger frequency bandwidth for wind-induced vibration control at a small mass ratio.

Key Words
analytical design; isolation; serviceability; tuned liquid damper; wind loads

Address
(1) Xiuyan Hu:
School of Urban Construction and Safety Engineering, Shanghai Institute of Technology, Shanghai 201418, China;
(2) Zhipeng Zhao, Cong Liao, Yuanchen Tang:
Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China;
(3) Na Hong:
Institute of Earthquake Protection and Disaster Mitigation, Lanzhou University of Technology, Lanzhou 730050, China.

Abstract
Grouting sleeves are an essential connecting component of prefabricated components, and the quality of grouting has a significant influence on structural integrity and seismic performance. The embedded grouting sleeve (EGS)'s grouting defects are highly undetectable and random, and no effective monitoring method exists. This paper proposes an ultrasonic guided wave method and provides a set of guidelines for selecting the optimal frequency and suitable period for the EGS. The optimal frequency was determined by considering the group velocity, wave structure, and wave attenuation of the selected mode. Guided waves are prone to multi-modality, modal conversion, energy leakage, and dispersion in the EGS, which is a multi-layer structure. Therefore, a time-reversal (TR)-based multi-mode focusing and dispersion automatic compensation technology is introduced to eliminate the multi-mode phase difference in the EGS. First, the influence of defects on guided waves is analyzed according to the TR coefficient. Second, two major types of damage indicators, namely, the time domain and the wavelet packet energy, are constructed according to the influence method. The constructed wavelet packet energy indicator is more sensitive to the changes of defecting than the conventional time-domain similarity indicator. Both numerical and experimental results show that the proposed method is feasible and beneficial for the detection and quantitative estimation of the grouting defects of the EGS.

Key Words
grouted sleeve; guided wave; multi-layer structure; wavelet packet; time-reversal

Address
(1) Jiahe Liu:
School of Civil Engineering, Dalian University of technology, Dalian, 116024, China;
(2) Li Tang:
College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China;
(3) Dongsheng Li:
China State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China;
(4) Wei Shen:
School of Electrical Engineering, University of South China, Hengyang, 421001, China.

Abstract
This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zeroshot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a chargecoupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

Key Words
active thermography; defect inspection; non-destructive test; steel structure; zero-shot learning

Address
Department of Civil Engineering, Korean Advanced Institute for Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Abstract
The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMAMLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R2) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

Key Words
fly ash; high strength concrete; neural-evolutionary; optimization

Address
(1) Yinghao Zhao:
School of Civil Engineering and Engineering Management, Guangzhou Maritime University, Guangzhou, 510725, China;
(2) Hossein Moayedi, Loke Kok Foong, Quynh T. Thi:
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam;
(3) Hossein Moayedi, Loke Kok Foong, Quynh T. Thi:
School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam.

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