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CONTENTS
Volume 34, Number 6, December 2024
 

Abstract
Structural damage identification based on wavelet packet energy has been applied due to its powerful timefrequency analysis capability. Current methods are mainly based on the wavelet packet total energy of structural acceleration or displacement responses, and these responses primarily reflect structural overall characteristics. Moreover, it is essential to investigate the influence of different excitations on identifying structural damage under random excitations. Therefore, three studies on structural damage identification based on wavelet packet total energy of structural responses under random excitations are discussed in this paper. First, structural damage identifications based on wavelet packet total energy of structural acceleration, displacement, and macro-strain/strain responses are discussed, respectively. Second, the effects of different excitations to intact and damaged structures on structural damage identification based on wavelet packet total energy of structural macro-strain/strain responses are also discussed. Finally, to eliminate the influence of random excitations, structural damage identifications based on transmissibility functions of wavelet packet total energy of structural macro-strain/strain responses are proposed. Through numerical studies of structural damage identification of both beam-type and truss structures, it is illustrated that the wavelet packet total energy change rate based on macro-strain/strain responses is more sensitive to local damage, different excitations significantly affect structural damage identification, and the influence of random excitations can be eliminated by the transmissibility functions of wavelet packet total energy of structural macro-strain/strain responses.

Key Words
random excitations; structural damage identification; structural strain responses; transmissibility functions; wavelet packet energy

Address
School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China.


Abstract
Multi-supported beam structures are widely used in engineering applications, particularly as sensitive components in resonators. The intrinsic properties of these sensitive components significantly influence the performance of resonators. In this study, a dynamic theoretical model of a multi-point supported beam was established, considering the impact of the supported beams on the overall structure. The governing equations of motion of the multi-point supported beam structure were derived using the Hamiltonian principle. A theoretical method was proposed to calculate frequencies and global modal shapes of the multi-point supported beam structures. The theoretical results were validated through numerical simulations with specific parameters. The natural frequencies of a multi-point supported beam were also experimentally measured and compared with the simulation results. Our investigation into the effects of structural parameters on the frequency and global modal shapes demonstrated the effectiveness of the proposed method. The findings show that frequencies and modal shapes of the complex beam undergo regular changes as structural parameters vary. This study provides a theoretical foundation for improving the performance of beam resonators and serves as a guide for the parametric design of multi-point supported beam structures.

Key Words
experiment; frequency; global modal shape; multi-point supported beam; resonator sensitive component

Address
(1) Wenhua Hu, Guofeng Xia:
Chongqing Three Gorges University, Chongqing 404120, P.R. China;
(2) Wenhua Hu, Yibao Cai, Ruiqin Wu, Jianen Chen, Jingjing Feng:
Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, P.R. China;
(3) Wenhua Hu, Yibao Cai, Ruiqin Wu, Jianen Chen, Jingjing Feng:
National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, P.R. China.

Abstract
Compressive Strength (CS) and Tensile Strength (TS) are vital in designing a reinforced concrete structure. In fact, ensuring structural strength and safety requires both properties. Establishing predictive models for CS and TS yields high advantages, ensuring considerable cost savings by reducing labor-intensive and time-consuming lab experiments. This becomes more critical where high performance is necessary, such as in testing advanced HPC, renowned for its remarkable durability and strength in critical infrastructure and construction undertakings. The use of machine learning has become one of the innovative approaches to predicting these concrete characteristics. Through data-driven analyses on ingredient ratios, curing conditions, and environmental exposure, ML returns quite accurate CS and TS predictions. Applying ML methodologies results in gains in efficiency, cost economy, design refinement, higher-quality control, and increased safety. The contribution of this paper is the realization of an extended comparison between many algorithms. In particular, this work investigates two ML-based models: Histogram Gradient Boosting (HGB) and Light Gradient Boosting (LGB). These models are combined in a structured fashion with three newer optimization algorithms: Snake Optimization Algorithm (SO), Fox Optimization Algorithm (FO), and the Prairie Dog Optimization Algorithm (PDO), along with an ensemble of all three optimizers, namely SO-FO-PDO. From the results represented by the R2 values, it is obvious that the HGPD model demonstrated far better forecasting performance for the CS, with a resultant R2 value of 0.9961 during training. Similar to TS, the HGPD model developed as the best estimator in the case of TS with an R2 value of 0.9947 for the training phase. Besides, from the statistical measures of accuracy w.r.t MAE and RMSE, it has been quite evident that the proposed PDO-based hybrid and ensemble models outperformed the rest by a long margin in estimating concrete mechanical properties.

Key Words
compressive and tensile strength; high-performance concrete; light gradient and histogram gradient boosting; meta-heuristic algorithms; sensitivity analyses

Address
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, Hubei, China.


Abstract
The electromechanical impedance (EMI) method is utilized for monitoring corrosion damage in pipelines. However, the variable thickness of the adhesive layer at the planar and cylindrical interfaces presents a challenge. To investigate the impact of uneven adhesive layer thickness on the EMI method, a theoretical model has been developed. This model analyzes the distribution of shear stress in the adhesive layer and the root-mean-square deviation (RMSD) of the impedance of the PZT. It was observed that the energy conversion efficiency decreased with a thicker adhesive layer. In monitoring cylindrical surface structures using the EMI method, the thicker adhesive layer locations experienced concentrated shear stresses, exacerbating energy attenuation and reducing monitoring sensitivity. In an experimental corrosion monitoring study of an aluminum alloy pipeline, two groups of PZTs with different forms of pasting were compared. The analysis of the RMSD of the PZT impedance before and after corrosion revealed that the group with poorer homogeneity exhibited lower sensitivity. Additionally, the theory provides a permissible error range for the pasting process.

Key Words
corrosion monitoring; EMI method; uneven thickness of adhesive layer

Address
(1) Rui Hao, Yuanfan Wang, Yu Bao:
Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, 46 Nanchang Road, Guanghan, Sichuan 618307, China;
(2) Rui Hao:
Sichuan Province Engineering Technology Research Center of General Aircraft Maintenance, Civil Aviation Flight, University of China, Guanghan, Sichuan 618307, China;
(3) Guoquan Shi:
Guangdong Excellence Shengda Anti-Corrosion Engineering Technology Co., Ltd., 1 Chongmin Road, Foshang, Guangdong 528211, China.

Abstract
Dynamic properties extracted from bridge acceleration responses are critical for assessing safety, particularly in the context of long-span cable-supported bridges with main spans exceeding one kilometer. However, the abundance of acceleration sensors in their Structural Health Monitoring (SHM) systems is compromised by frequent failures in harsh operational environments, leading to significant issues of missing or erroneous vibration monitoring data. Recent advancements in deep learning offer promising solutions to diagnose the monitored abnormal bridge vibration data. Existing methods often rely on single-bridge vibration monitoring data, posing challenges in applying models across different bridges. To address these challenges, this study proposes a novel ResNet-based feature extraction method for bridge vibration data anomaly detection, emphasizing time-efficient classification and transfer learning. The timeseries bridge vibration responses are transformed into images to enhance computation efficiency. The proposed methodology leverages a pre-trained ResNet50 network for feature extraction, feeding extracted feature vectors into a k-means clustering algorithm for classification. Transfer learning with labelled training datasets ensures detection performance across different bridges, minimizing the required training data. Validation utilizes long-term vibration monitoring data from the SHM system of Sutong Bridge. The results aim to provide reliable technical support for data-driven condition assessment and maintenance of long-span bridges, addressing challenges in SHM systems and contributing to the safety and sustainability of critical infrastructure.

Key Words
anomaly detection; long-span bridges; ResNet transfer learning; structural health monitoring; vibration data

Address
(1) Jianxiao Mao, Xun Su, Gui Gui, Hao Wang, Dan Li:
Key laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 211189, China;
(2) Yuguang Fu:
School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore.

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