Article
Engineering, Mechanical
Chengyin Liu, Yipeng Zhu, Hui Ye
Summary: The study proposes a two-stage framework to extract the bridge frequency (BF) using the tire pressure of a vehicle crossing it. The first step involves establishing and calibrating a tire pressure model, while the second step utilizes the model to identify BFs based on field measurements of the rear tire pressure change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Cheng Zhang, Wenju Zhao, Weiguo Wang, Jian Zhang
Summary: This paper introduces a cost-effective method for measuring vertical tire forces using computer vision and a neural network, showing potential for rapid vehicle-induced bridge impact testing.
Article
Engineering, Civil
Jinsong Zhu, Teng Shi
Summary: In this study, a split-type two-axle vehicle is proposed for bridge indirect measurement. The impacts of vehicle frequency and speed on extracting bridge frequencies are investigated through theoretical and numerical analysis, providing a theoretical basis for bridge health monitoring.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS
(2023)
Article
Environmental Sciences
Bin Zhang, Hua Zhao, Chengjun Tan, Eugene J. OBrien, Paul C. Fitzgerald, Chul-Woo Kim
Summary: This study proposes a vehicle-response-based method for scour detection, which utilizes wavelet transform of vehicle accelerations to obtain wavelet energy to determine the level and location of scour damage. Through numerical simulations and lab experiments, the feasibility of this method is demonstrated, indicating its potential for scour detection.
Article
Engineering, Civil
Nan Xu, Ehsan Hashemi, Zepeng Tang, Amir Khajepour
Summary: This paper presents a generic framework for tire capacity identification that can handle different conditions and accurately estimate tire performance using a combination of model description and learning methods. The proposed method has been validated through indoor testing, road experimenting, and real-world scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Environmental Sciences
David Mennekes, Bernd Nowack
Summary: Research has shown that most of the 14 country-based TWP emission studies are based on other research rather than their own measurements, with only a few directly referencing measurement studies. There is an urgent need to reduce the uncertainties in TWP emission estimates to better understand their contribution to overall microplastic pollution.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Automation & Control Systems
Woongsun Jeon, Ankush Chakrabarty, Ali Zemouche, Rajesh Rajamani
Summary: This article introduces a neural network approach for simultaneous learning of the vehicle's tire model and state estimation, achieving estimation of the state vector and neural network weights through a neuro-adaptive observer. Through MATLAB simulations and testing with CarSim software, the technology demonstrates good performance on both low-order vehicle models and unknown high-order models, effectively estimating tire characteristics.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2021)
Article
Engineering, Multidisciplinary
Jie Zhang, Xuan Kong, Eugene J. OBrien, Jiaqiang Peng, Lu Deng
Summary: This study proposes a noncontact measurement method of tire deformation based on computer vision and deep learning techniques. A diverse dataset of tire images is established and a semantic segmentation Tire-Net is developed to segment the tire images. The proposed quantification algorithm calculates the physical value of tire deformation using subpixel-level edge detection, key point positioning, and scale factor determination. Field tests on various vehicles verify the effectiveness of the proposed method.
Article
Construction & Building Technology
Mingyang Gong, Jingyun Chen
Summary: A 3D tire-bridge interaction FE model was developed to investigate load-induced fatigue damage behavior in curved ramp bridge deck pavement. The results showed that considering tire-bridge interaction and structural design parameters significantly influenced the accurate prediction of fatigue crack evolution law. The developed model, which considers multiple factors, has the potential to enhance fatigue damage analysis of curved ramp bridge deck pavement.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Engineering, Civil
Nan Xu, Hassan Askari, Yanjun Huang, Jianfeng Zhou, Amir Khajepour
Summary: This paper focuses on intelligent tires and the application of machine learning techniques for tire force estimation. By installing a tri-axial acceleration sensor and utilizing neural network techniques for real-time processing, accurate prediction of tire forces can be achieved.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Construction & Building Technology
Dexin Liu, Bo Liu, Xingui Li, Kang Shi
Summary: This study presents a novel and highly efficient technique to identify moving forces using the acceleration response of an instrumented moving vehicle. The task is simplified by solving linear equations through the Newmark-beta method and improving accuracy with Tikhonov regularization. By placing sensors solely on the vehicle, the method quickly identifies moving forces based on the responses of the vehicle-bridge system. Numerical verification and examination of external factors demonstrate the method's high recognition accuracy, robustness, and reliability, offering a new perspective for identifying moving forces in small to medium-span bridges.
Article
Engineering, Electrical & Electronic
Xiaolin Ding, Zhenpo Wang, Lei Zhang, Jizheng Liu
Summary: This paper presents a comprehensive vehicle stability assessment system that accurately estimates tire forces and evaluates and predicts vehicle stability, which is of great significance for improving vehicle safety under critical driving conditions.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Review
Engineering, Multidisciplinary
Debojyoti Paul, Koushik Roy
Summary: Bridge health monitoring (BHM) is essential for maintaining transportation networks worldwide. Traditional visual inspection methods have limitations such as bias, inaccessibility, and the need for physical presence. Vibration-based methods require artificial excitation. Therefore, researchers have explored the potential of bridge weigh-in-motion (B-WIM) systems as an alternative. B-WIM systems have advantages such as durability, portability, easy installation, and provide weight estimation and structural information, making them cost-effective compared to standalone BHM systems.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Mechanical
Min Wu, Liu Jin, Xiuli Du
Summary: The study investigated the dynamic responses and failure modes of bridge precast segment piers under vehicle collision, showing that precast segment piers sustain less damage than cast-in-place piers under collision, with the prestressing level and number of segments having no significant impact on impact force. Design considerations should include the relative displacements between precast segments under vehicle collision.
ENGINEERING FAILURE ANALYSIS
(2021)
Article
Mathematics
Haniyeh Fathi, Mehran Khosravi, Zeinab El-Sayegh, Moustafa El-Gindy
Summary: This paper investigates the cornering characteristics of a truck tire using finite element analysis (FEA) on a dry, hard surface. A finite element model is developed to simulate the tire terrain cornering condition, and various operating conditions including slip angles, inflation pressures, and vertical loads are considered. The simulation results show that the tire lateral force is highly sensitive to slip angle variations and that tire inflation pressure is a significant parameter affecting the tire-cornering properties.
Article
Construction & Building Technology
Hao Sun, Jamal Al-Qazweeni, Jafarali Parol, Hasan Kamal, Zhao Chen, Oral Buyukozturk
STRUCTURAL CONTROL & HEALTH MONITORING
(2019)
Article
Computer Science, Interdisciplinary Applications
Ruiyang Zhang, Zhao Chen, Su Chen, Jingwei Zheng, Oral Buyukozturk, Hao Sun
COMPUTERS & STRUCTURES
(2019)
Article
Engineering, Mechanical
Zhao Chen, Ruiyang Zhang, Jingwei Zheng, Hao Sun
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2020)
Article
Engineering, Multidisciplinary
Zhao Chen, Hao Sun
Summary: The study introduces a novel two-stage sensitivity analysis-based framework for model updating and sparse damage identification. By utilizing sparse representation for inverse analysis, together with Bayesian learning method and Bayesian optimization technique, it can accurately localize and quantify structural damage, improving the reliability and accuracy of damage identification.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Multidisciplinary Sciences
Zhao Chen, Yang Liu, Hao Sun
Summary: The authors propose a learning approach to discover governing partial differential equations from scarce and noisy data, which can advance modeling and understanding of complex systems in various fields including science and engineering. This work combines the strengths of deep neural networks and sparse regression to approximate system variables, compute essential derivatives, and identify key derivative terms and parameters that form the structure and explicit expression of the equations.
NATURE COMMUNICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhao Chen, Yang Liu, Hao Sun
Summary: This study proposes a forecasting model based on symbolic invariance, which develops an invariant symbolic structure by training and pruning a symbolic neural network and leverages non-parametric Bayesian inference for probabilistic forecasting. It also utilizes the delay coordinate embedding to solve the univariate forecasting problem of partially observed multivariate systems. The experimental results demonstrate that the proposed framework achieves better generalization in nonlinear dynamics prediction and exhibits higher performance and more efficient optimization when the function search space is enormous.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Engineering, Civil
Nan Wang, Qin Chen, Zhao Chen
Summary: This paper focuses on utilizing physics-informed neural networks (PINNs) to model nearshore wave transformation. The performance of the developed nearshore wave nets (NWnets) is examined by comparing the results with numerical solutions and experimental data. The study shows that the physics-guided deep learning method is a promising tool for studying nearshore processes.
COASTAL ENGINEERING
(2022)
Article
Construction & Building Technology
Zhao Chen, Yang Liu, Hao Sun
Summary: A symbolic deep learning framework is proposed to flexibly determine model types and discover symbolic invariance of structural systems. A two-stage model selection strategy is introduced to balance model sparsity and goodness of fit. Experimental results demonstrate the potential of the proposed method in interpreting hidden mechanisms for real-world applications.
JOURNAL OF STRUCTURAL ENGINEERING
(2022)
Article
Mathematics, Applied
J. Koch, Z. Chen, A. Tuor, J. Drgona, D. Vrabie
Summary: This work aims to infer the intrinsic physics of a base unit, the underlying graphical structure between units, and the coupling physics of a networked dynamical system based on observed nodal states. These tasks are formulated using the Universal Differential Equation, approximating unknown systems with neural networks, known mathematical terms, or combinations of both. The value of these inference tasks is demonstrated through future state predictions and inference of system behavior on varied network topologies.
Article
Engineering, Mechanical
Zhao Chen, Nan Wang
Summary: Distilling equations from data can provide insights into physics systems, helping validate theoretical modeling, infer unknown system properties, and explore indeterminate processes. In this paper, a novel physics-informed equation learning method is proposed to address the limitations of noisy or downsampled data in symbolic regression. The method uses a neural network to learn dynamics with various frequency components and enables physics-consistent data augmentation to improve equation reliability.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Civil
Qin Chen, Nan Wang, Zhao Chen
Summary: This paper introduces two methods that utilize physics-informed neural networks (PINNs) to determine nearshore water depths and wave height fields based on remote sensing of the ocean surface with limited or sparse measurements. The first method utilizes observed wave celerity fields and scarce measurements of wave height as training data, while the second method uses scarce wave height and water depth measurements as training points. The study demonstrates the potential of the inverse PINN model as a promising tool for estimating nearshore bathymetry and reconstructing wave fields.
COASTAL ENGINEERING
(2023)
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
