Abstract
This research examines a model of a Tuned Inerter Damper (TID) developed by the authors in minimizing vibrations in a five-story structure when exposed to seismic activity. The TID model is developed using MATLAB Simulink and tested on a 5-story building model when exposed to an earthquake signal. The TID model configurations were calibrated to match the primary vibration frequency of the building using established tuning approaches. Then, genetic algorithm optimization was employed to enhance the inertance parameter for better performance under earthquake excitation throughout all building levels. Time-based simulations were performed comparing different scenarios with the TID optimized system. The results demonstrated that implementing the developed model of TID lead to substantial reductions in vibration magnitudes over time, which confirmed the potential of the inerter in enhancing structural oscillations control.
Key Words
frequency response; genetic algorithm; passive inerter; tuned inerter damper; vibration mitigation
Address
(1) Ayman Nasir, Alia Mahmoud, Ahmad Manasrah:
Department of Mechanical Engineering, Al-Zaytoonah University of Jordan, Amman, Jordan;
(2) Raed AlSaleh:
Department of Civil and Environmental Engineering, German Jordanian University, Amman, Jordan;
(3) Omar Omar:
Department of Mechanical Engineering, Al Hussein Technical University, Amman, Jordan.
Abstract
In-service monitoring of the stress state of steel strands or cables is essential for estimating the loading capacity of the entire structure. The traditional wave velocity–based method may not be sensitive to changes in cable tension. To address this issue, a principal component analysis (PCA)-based method was presented in this study, using longitudinal guided waves generated by a pair of magnetostrictive transducers. First, semi-analytical finite element analysis was performed considering the effect of the external load to investigate the changes in wave velocity under various guided wave modes in the lower frequency range. Then, experimental studies were conducted on a seven-wire steel strand under various cable tension levels. The directarrival wave packets of the reference signals were used to construct the PCA model. Score values corresponding to the principal components (PCs) of the reference signals were calculated, and the PCs whose scores were closely related to the tension variations were determined. A linear regression curve for the scores and cable tensions was constructed and used to determine the cable tensions for the test signals. The results show that the proposed method can accurately estimate cable tension and performs better than the notch frequency-based method.
Key Words
cable tension estimation; guided wave; magnetostrictive transducers; principal component analysis; Qstatistics; scores; steel strands
Address
(1) Ru Zhang, Xiaodong Sui:
Department of Civil Engineering, Hangzhou City University, China;
(2) Ru Zhang, Xiaodong Sui, Yuanfeng Duan, Yaozhi Luo:
College of Civil Engineering and Architecture, Zhejiang University, China;
(3) Ru Zhang, Xiaodong Sui:
Zhejiang Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels, China.
Abstract
This paper presents a self-powered tunable piezoelectric vibration sensor based on stress distribution. The piezoelectric sensor features split electrodes to adjust the resonance frequency. Turning electrically OFF configurable electrodes changes the sensor stiffness by varying the stress level. Unlike previous works, the proposed tunable sensor does not use a magnet or an auxiliary mass. The effect of the tuning mechanism on the resonance frequency, voltage sensitivity, and power density has been investigated through theoretical analysis, simulation, and experimentation. The paper explores the effect of the stress level on sensor linearity. The paper also examines the impact of thickness and length on the frequency tuning range. The tuning mechanism achieves a tuning range of 8.6 Hz with a thickness ratio of 0.2 and a length of 90 mm, surpassing the capabilities of most fine-tuning mechanisms. The tunable sensor has a good linearity of R2 ≈ 0.98. Within 116.9 Hz to 120.8 Hz, the minimum power density is 100 μW/cm3 for a 1 g input excitation level. In mode 100, the sensor voltage sensitivity and power density increase by about 83% and 11%, respectively, compared to a conventional sensor. The self-powered tunable piezoelectric vibration sensor can compensate for physical uncertainty, such as inherent variability in material properties and manufacturing tolerances.
Address
(1) Mehdi Aslinezhad, Alireza Malekijavan:
Department of Electrical Engineering, Shahid Sattari Aeronautical University of Science and Technology, 1384663113, Tehran, Iran;
(2) Sajad Hadidi:
Faculty of Electrical Engineering, Shahid Beheshti University, 1983969411, Tehran, Iran.
Abstract
Structural health monitoring (SHM) has been widely used in civil infrastructure in recent decades. In SHM, vast amounts of data are collected using diverse sensors to monitor the health of civil structures. During this process, various types of anomalies may occur, which hindering an accurate assessment of the structure's condition. Anomalies mainly occur due to the influence of the harsh environment, sensor faults, or actual damage to the monitored structure. Therefore, early detection of anomalies is essential for monitoring the condition of structures. Conventional anomaly detection algorithms used in SHM systems, such as statistical thresholding, distance-based, rule-based, and clustering methods, have become ineffective today with growing data flow. These traditional algorithms face several limitations, including scalability issues, lack of adaptability to changing conditions, sensitivity to noise, and extensive feature engineering requirements. To address these issues, this paper proposes a modified transformer-based multiclass anomaly detection method for SHM systems. In our approach, we replace the feed-forward layers in the transformer encoder with two 1D-CNN layers and opt not to use positional encoding, as the occurrence of anomalies in SHM systems is not strongly related to specific positions within the sequence. Initially, the statistic and frequency domain features are extracted from the labeled time-series raw data. Then the modified transformer-based anomaly detection model is trained with extracted features and validated with acceleration data measured from a long-span cable-stayed bridge. The results confirm that the modified transformer encoders with 1D-CNN layers, without positional encoding, provide improved performance in detecting and classifying multiple types of anomalies with high accuracy. This demonstrates the potential of our method for enhancing the effectiveness of SHM systems.
Key Words
anomaly detection; deep learning; structural health monitoring; time-series classification; vibrational signal
Address
(1) Sirojiddin Nuriev, Ji-Hye Kwon, Youngsu Kim:
Research and Development Center, SISTech, 209, Neungdong-ro , Gwangjin-gu, Seoul, Republic of Korea;
(2) Min-Joon Kong:
THESOLT INC., AF002-0007, 202 Dasanjigeum-ro, Namyangju, Gyeonggi-do, Republic of Korea;
(3) Jong-Jae Lee:
Department of Civil and Environmental Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, Republic of Korea.
