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Volume 31, Number 3, March 2023

A passive prismatic-shaped isolation platform (PIP) is proposed to realize enhanced quasi-zero stiffness (QZS) effect. The design concept uses a horizontal spring to produce a tunable negative stiffness and installs oblique springs inside the cells of the prismatic structure to provide a tunable positive stiffness. Therefore, the QZS effect can be achieved by combining the negative stiffness and the positive stiffness. To this aim, firstly, the mathematical modeling and the static analysis are conducted to demonstrate this idea and provide the design basis. Further, with the parametric study and the optimal design of the PIP, the enhanced QZS effect is achieved with widened QZS range and stable property. Moreover, the dynamic analysis is conducted to investigate the vibration isolation performance of the proposed PIP. The analysis results show that the widened QZS property can be achieved with the optimal designed structural parameters, and the proposed PIP has an excellent vibration isolation performance in the ultra-low frequency due to the enlarged QZS range. Compared with the traditional QZS isolator, the PIP shows better performance with a broader isolation frequency range and stable property under the large excitation amplitude.

Key Words
negative stiffness; quasi-zero stiffness; ultra-low frequency; vibration isolation

(1) Jing Bian, Xuhong Zhou, Ke Ke, Yuhang Wang:
School of Civil Engineering, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing, China;
(2) Michael C.H. Yam:
Department of Building and Real Estate, Chinese National Engineering Research Centre for Steel Construction, The Hong Kong Polytechnic University, Hong Kong, China;
(3) Zi Gu:
Department of Civil Engineering, Hunan University, Hunan, China;
(4) Miaojun Sun:
Powerchina Huadong Engineering Corporation Limited, Hangzhou 310014, China;
(5) Miaojun Sun:
Zhejiang Engineering Research Center of Marine Geotechnical Investigation Technology and Equipment, Hangzhou 310014, China.

The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified Jaya algorithm (M-Jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard Jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-Jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and Jaya algorithm.

Key Words
B-spline interpolation function; damage identification; force identification; Jaya algorithm; k-means clustering; Tikhonov regularization

(1) Guangcai Zhang, Chunfeng Wan:
Key Laboratory of concrete and prestressed concrete structure of Ministry of Education, Southeast University, Nanjing, China;
(2) Liyu Xie, Songtao Xue:
Research Institute of Structural Engineering and Disaster Reduction, College of Civil Engineering, Tongji University, Shanghai, China;
(3) Songtao Xue:
Department of Architecture, Tohoku Institute of Technology, Sendai, Japan.

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRLbased optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

Key Words
deep reinforcement learning; discrete combinatorial optimization; modal assurance criterion; sensor placement; structural health monitoring

(1) Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, School of Civil Engineering, Harbin Institute of Technology, Harbin, China;
(2) Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China.

Real-time hybrid simulation (RTHS) is an effective experimental technique for structural dynamic assessment. However, time delay causes displacement de-synchronization at the interface between the numerical and physical substructures, negatively affecting the accuracy and stability of RTHS. To this end, the authors have proposed a model-based adaptive control strategy with a Kalman filter (MAC-KF). In the proposed method, the time delay is mainly mitigated by a parameterized feedforward controller, which is designed using the discrete inverse model of the control plant and adjusted using the KF based on the displacement command and measurement. A feedback controller is employed to improve the robustness of the controller. The objective of this study is to further validate the power of dealing with a nonlinear control plant and to investigate the potential challenges of the proposed method through actual experiments. In particular, the effect of the order of the feedforward controller on tracking performance was numerically investigated using a nonlinear control plant; a series of actual RTHS of a frame structure equipped with a magnetorheological damper was performed using the proposed method. The findings reveal significant improvement in tracking accuracy, demonstrating that the proposed method effectively suppresses the time delay in RTHS. In addition, the parameters of the control plant are timely updated, indicating that it is feasible to estimate the control plant parameter by KF. The order of the feedforward controller has a limited effect on the control performance of the MAC-KF method, and the feedback controller is beneficial to promote the accuracy of RTHS.

Key Words
feedforward and feedback; Kalman filter; model-based adaptive control; real time hybrid simulation

(1) Xizhan Ning, Wei Huang:
College of Civil Engineering, Huaqiao University, Xiamen 361021, China;
(2) Xizhan Ning:
Key Laboratory for Intelligent Infrastructure and Monitoring of Fujian Province, Huaqiao University, Xiamen 361021, China;
(3) Guoshan Xu, Lichang Zheng:
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China;
(4) Zhen Wang:
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China.

In recent years, technological developments have rapidly increased the number of complex structures and equipment in the industrial. Accordingly, the prognostics and health monitoring (PHM) technology has become significant. The safety assessment of industrial sites requires data obtained by installing a number of sensors in the structure. Therefore, digital twin technology, which forms the core of the Fourth Industrial Revolution, is attracting attention in the safety field. The research on digital twin technology of structures subjected to seismic loads has been conducted recently. Hence, this study proposes a digital twin system that estimates the responses and arbitrary load in real time by utilizing the minimum sensor to a pipe that receives a seismic and arbitrary load. To construct the digital twin system, a finite-element model was created considering the dynamic characteristics of the pipe system, and then updating the finite-element model. In addition, the calculation speed was improved using a finite-element model that applied the reduced-order modeling (ROM) technology to achieve real-time performance. The constructed digital twin system successfully and rapidly estimated the load and the point where the sensor was not attached. The accuracy of the constructed digital twin system was verified by comparing the response of the digital twin model with that derived by using the load estimated from the digital twin model as input in the finite-element model.

Key Words
digital twin; finite element model; real-time; reduced-order modeling (ROM); topography prognostics and health monitoring (PHM)

(1) Dongchang Kim, Gungyu Kim, Seunghyun Eem:
School of Convergence & Fusion System Engineering, Major in Plant System Engineering, Kyungpook National University, 2559 Gyeongsang-daero, Sangju, 37224, Republic of Korea;
(2) Shinyong Kwag:
Department of Civil and Environmental Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-gu, Daejeon, 34158, Republic of Korea.

Structural stability of floating platforms has long since been a crucial issue in the field of marine engineering. Excessive motions would not only deteriorate the operating conditions but also seriously impact the safety, service life, and production efficiency. In recent decades, several control devices have been proposed to reduce unwanted motions, and an attractive one is the tuned heave plate (THP). However, the THP system may reduce or even lose its effectiveness when it is mistuned due to the shift of dominant wave frequency. In the present study, a novel adaptive tuned heave plate (ATHP) is proposed based on inerter by adjusting its inertance, which allows to overcome the limitation of the conventional THP and realize adaptations to the dominant wave frequencies in real time. Specifically, the analytical model of a representative semisubmersible platform (SSP) equipped with an ATHP is created, and the equations of motion are formulated accordingly. Two optimization strategies (i.e., J1 and J2 optimizations) are developed to determine the optimum design parameters of ATHP. The control effectiveness of the optimized ATHP is then examined in the frequency domain by comparing to those without control and controlled by the conventional THP. Moreover, parametric analyses are systematically performed to evaluate the influences of the pre-specified frequency ratio, damping ratio, heave plate sizes, peak periods and wave heights on the performance of ATHP. Furthermore, a Simulink model is also developed to examine the control performance of ATHP in the time domain. It is demonstrated that the proposed ATHP could adaptively adjust the optimum inertance-to-mass ratio by tracking the dominant wave frequencies in real time, and the proposed system shows better control performance than the conventional THP.

Key Words
adaptive tuned heave plate; heave motion reduction; inerter; offshore platforms

(1) Ruisheng Ma:
Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing, 100124, China;
(2) Kaiming Bi:
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China;
(3) Haoran Zuo:
Centre for Infrastructure Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Kent Street, Bentley, WA, 6102, Australia.

Tilt is a key indicator of structural safety. Real-time monitoring of tilt responses helps to evaluate structural condition, enable cost-effective maintenance, and enhance lifetime resilience. This paper presents a prototype wireless sensing system for structural tilt measurement. Long range (LoRa) technology is adopted by the sensing system to offer long-range wireless communication with low power consumption. The sensor integrates a gyroscope and an accelerometer as the sensing module. Although tilt can be estimated from the gyroscope or the accelerometer measurements, these estimates suffer from either drift issue or high noise. To address this challenging issue and obtain more reliable tilt results, two sensor fusion algorithms, the complementary filter and the Kalman filter, are investigated to fully exploit the advantages of both gyroscope and accelerometer measurements. Numerical simulation is carried out to validate and compare the sensor fusion algorithms. Laboratory experiment is conducted on a simply supported beam under moving vehicle load to further investigate the performance of the proposed wireless tilt sensing system.

Key Words
complementary filter; data fusion; Kalman filter; structural health monitoring; tilt measurement; wireless sensor

(1) Dan Li:
China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, Jiangsu 211189, China;
(2) Dan Li, Guangwei Zhang, Ziyang Su, Jian Zhang:
School of Civil Engineering, Southeast University, Nanjing, Jiangsu 211189, China;
(3) Jian Zhang:
Jiangsu Key Laboratory of Engineering Mechanics, Southeast University, Southeast University, Nanjing, Jiangsu 211189, China.

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