Abstract
Experimental evaluation is crucial for ensuring the accuracy of real-time hybrid simulation (RTHS) results. While existing methods can calculate time delay and amplitude error, time-varying delays can destabilize RTHS, requiring a method to account for them. This paper proposes using empirical mode decomposition (EMD) to calculate instantaneous control parameters, such as instantaneous amplitude and time delay. Intrinsic mode functions (IMF) from EMD capture the signal's local characteristics at different time scales, and the Hilbert transform is applied to compute these parameters. After EMD, a different number of IMFs may be obtained for calculated displacements than for measured displacements, and this paper gives advice on the IMFs needed to calculate instantaneous control parameters (ICP), and how they should be matched. The signals obtained from the numerical simulation of the benchmark model without and with compensation are firstly subjected to the calculation of ICP, and the results prove the effectiveness of ICP. Subsequently, the predefined displacement test with a multidegree-of-freedom structure and the RTHS with a single-degree-of-freedom structure and self-centering viscous damper were subjected to the traditional ICP method and the EMD-based ICP calculation method for ICP calculation, respectively, and the comparative results show that the effectiveness of the EMD-based ICP calculation method is increased, and that EMD effectively solves the negative-frequency issue caused by signals with multiple poles between two crossed zeros. These calculations show great potential in improving experimental evaluations.
Key Words
empirical mode decomposition; instantaneous control parameters; instantaneous frequency; real-time hybrid simulation
Address
(1) Weijie Xu, Xiangjin Meng, Tong Guo, Changle Peng:
Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, Southeast University, P.R. China;
(2) Cheng Chen:
School of Engineering, San Francisco State University, USA.
Abstract
Global monitoring of structures is vital for assessing their structural integrity, especially with the impact of moving vehicles on railroad bridges. This necessitates simultaneous monitoring of both systems to understand interaction dynamics comprehensively. In vibration-based Structural Health Monitoring fields, demands for directly obtaining displacement responses increase, leading to non-contact sensing adoption. Computer Vision (CV)-based methods, using feature tracking techniques for displacement measurements, have become practical alternatives. The proposed approach utilizes Poor Feature Points, offering a global view and overcoming spatial resolution limitations. Addressing challenges related to camera ego-motion in large-scale monitoring, strategies for re-assigning regions of interest based on feature quality are introduced, and camera ego-motion is compensated by calibrating feature points. The You Only Look Once algorithm is used for vehicle wheel detection, localizing contact points to examine Vehicle-Bridge Interaction dynamics. A laboratory-scale experiment validation confirms the feasibility of global monitoring with vision sensors, especially in interpreting VBI dynamics.
Key Words
KLT (Kanade Lucas Tomasi) algorithm; MST (Modified S-Transform); poor-feature points; vehicle track bridge interaction dynamics; yolo
Address
(1) Jae Hun Lee:
Department of Civil and Environmental Engineering, Hanyang University, Seoul, Republic of Korea;
(2) Sang Bin Lee, Robin Eunju Kim:
Department of Architecture and Architectural Engineering, Seoul National University, 1, Gwanak-gu, Seoul 08826, Republic of Korea;
(3) Jae Hun Lee:
Infrastructure Bridge Engineering Division, Hyundai Engineering & Construction, 75, Yulgok-ro, Jongno-gu, Seoul 03058, Republic of Korea.
Abstract
This study introduces a novel carbon nanotube (CNT) cementitious composite sensor developed using pore conductivity theory to address durability and structural compatibility requirements for monitoring ship-bridge collisions in marine environments. The sensor employs a dual-channel sensing mechanism by integrating CNT networks with conductive pathways formed by electrolyte solutions within cement pores. Experimental results demonstrate high sensing accuracy across sensors with varying slenderness ratios, achieving axial and lateral errors under 8%. Notably, sensors with a 1:4 slenderness ratio exhibit significantly enhanced resistance change rates under axial loading, up to 281% within a 10 kN lateral load range. Impact tests further confirm strong correlation between electrical signals and strain gauge measurements when collision speeds range between 1-2 m/s, validating real-time collision damage monitoring capabilities. This research establishes design principles for pore conductivity-based CNT cement sensors while providing theoretical foundations for smart concrete applications in shipbridge collision monitoring.
Key Words
carbon nanotubes; cementitious composite; impact load; pore conductivity; sensing model
Address
(1) Jian Guo:
State Key Laboratory of Bridge Intelligent and Green Construction, Southwest Jiaotong University, Chengdu, Sichuan 610000, China;
(2) Yuhao Cui:
School of Civil Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610000, China;
(3) Shan Hu:
Institute of Bridge Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310012, China.
Abstract
A predictive model to determine shear strength and mechanical properties of soil-mix material (soil reinforcement) is required in many geotechnical projects especially when the weight of geomaterial is important for stability or drainage purposes. In this paper, several matching learning (ML) techniques namely Chi-squared Automatic Interaction Detection (CHAID), Classification and Regression Trees (CART), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Generalized Linear Mixed Model (GLMM) were used to predict the effects of reinforcement on cohesion (C) parameter in sandy soil. To establish an appreciate database for prediction purposes, several laboratory tests were planned and conducted on sandy soil mixed with fiber and subsequently, soil properties together with their shear strength parameters were measured. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and then, the mentioned parameters were set as inputs. According to the most effective parameters of predictive ML techniques, many models were constructed to predict C of the soil. The modelling results showed that the CHAID model provides the best prediction performance of cohesion in the short term and long term. Coefficient of determination of one and system error of zero for both train and test stages of CHAID have confirmed that this model is a perfect, powerful and applicable ML technique. The design process and model development presented in this study can be considered and used by the other researchers or engineers in resolving their complicated issues.
Address
(1) Jun Song:
Chongqing Chemical Industry Vocational College, Chongqing 401228, China;
(2) Gongxing Yan:
School of Intelligent Construction Luzhou Vocational and Technical College, Luzhou 646000, Sichuan, China;
(3) Gongxing Yan:
Luzhou Key Laboratory of Intelligent Construction and Low-Carbon Technology Luzhou 646000, Sichuan, China;
(4) F. Mirza Aslzadh:
Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam;
(5) F. Mirza Aslzadh:
School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam;
(6) F. Mirza Aslzadh:
Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600077, India;
(7) Rania M. Ghoniem:
Department of Information Technology, College of Computer and Information Sciences, Princes Nourah bint Abdulrahman University, P.O. Box 84428, Riadh 11671, Saudi Arabia;
(8) Abdullah Alnutayfat:
Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia;
(9) B. Bouallegue:
Department of Computer Engineering, College of Computer Science, King Khalid University, ABHA, 61421, Saudi Arabia;
(10) J. Escorcia-Gutierrez:
Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia.
