Haoxiang Guo, Gongxing Yan, Sultan Saleh Alnahdi, Liang Yin, Belgacem Bouallegue, Abdullah Alnutayfat, Rania M. Ghoniem, Hamid Assilzadeh and José Escorcia-Gutierrez
Abstract
Piezoresistive MEMS pressure sensors are widely deployed across biomedical, automotive, and aerospace sectors, yet their sensitivity is often limited by suboptimal membrane geometry and material selection. While prior research has explored isolated design modifications, there remains a lack of systematic, comparative analysis integrating multiple geometric enhancements with material optimization for maximum performance. This study aims to address this gap by developing and evaluating a four-stage structural optimization framework that systematically enhances sensor sensitivity. The novelty lies in combining targeted geometric modifications, central relocation of transverse resistors, introduction of peripheral grooves, addition of sub-membrane support beams, and membrane thickness optimization, with a comparative assessment of silicon (Si) and germanium (Ge) membranes. This integrated approach enables a unified understanding of how architecture and material mechanics interact to influence piezoresistive output. The methodology employed high-fidelity finite element modeling (FEM) in COMSOL Multiphysics to simulate coupled mechanical–electrical behavior. Input parameters included precise geometric configurations, material properties, and applied pressure (1 psi), while outputs comprised stress distribution, maximum deflection, and Wheatstone bridge output voltage. Mesh convergence analysis ensured numerical accuracy without excessive computational cost. Simulation results show cumulative sensitivity improvements of 256.8% for Si and 140.6% for Ge over baseline designs. After thickness optimization, sensitivities reached 11.99 mV/psi (Si) and 12.51 mV/psi (Ge), closing the performance gap between materials. Si benefited most from thickness reduction due to its higher Young
Key Words
Building Information Modeling (BIM); finite element modelling; germanium; membrane geometry optimization; Piezoresistive MEMS pressure sensor; silicon
Address
(1) Haoxiang Guo, Gongxing Yan:
School of Intelligent Construction, Luzhou Vocational and Technical College, Luzhou 646000, Sichuan, China;
(2) Sultan Saleh Alnahdi:
Civil Engineering Department, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia;
(3) Liang Yin:
Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia;
(4) Belgacem Bouallegue:
Department of Computer Engineering, College of Computer Science, King Khalid University, ABHA, 61421, Saudi Arabia;
(5) Abdullah Alnutayfat:
Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia;
(6) Rania M. Ghoniem:
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.
Box 84428, Riadh 11671, Saudi Arabia;
(7) Hamid Assilzadeh:
Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam;
(8) Hamid Assilzadeh:
School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam;
(9) and José Escorcia-Gutierrez:
Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia.
Abstract
Past earthquake events show that aftershocks may arise within a short interval following a mainshock. Structural deformation caused by a mainshock can affect structural responses during an aftershock. Therefore, this study explores the aftershock fragility analysis of a bridge structure considering mainshock-aftershock sequential ground motions, and the result of sequential ground motion is compared with that of aftershock ground motion. In the numerical example, an in-service prestressed concrete (PSC) bridge that was constructed more than forty years ago is selected as the target structure. To consider structural deterioration of the bridge, a recently proposed approach based on unmanned aerial vehicle (UAV) damage detection is introduced. In the approach, the percent reduction in elastic modulus is computed in accordance with the selected damage states. To derive aftershock fragility curves, nonlinear time-history analyses are conducted using ten sets of mainshockaftershock sequential ground motions, and the analyses are also conducted using the single aftershock ground motions for comparison. The result shows that the failure probability of the target structure increased due to the mainshock effect.
Address
(1) Sangmok Lee:
Dam Safety Management Center, Korea Water Resources Corporation (K-water), 200 Sintanjin-ro, Daedeok-gu, Daejeon 34350, Republic of Korea;
(2) Sungsik Yoon:
Department of Artificial Intelligence, Hannam University, 70 Hannam-ro, Daedeok-gu, Daejeon 34430, Republic of Korea.
Abstract
As an important connection component for prefabricated concrete (PC) structures, grouted splice sleeve (GSS) connectors are widely employed in building construction. However, effectively detecting grouting defects in GSS connectors remains challenging due to their concealed and inaccessible nature. This paper proposes a novel grouting defect detection method based on damping effect-induced ultrasonic wave attenuation. Based on one degree-of-freedom (DOF) free vibration system, an ultrasonic propagation absorption attenuation model considering the damping effect is built. The result indicates that the response of the model is exponentially decaying. Compared to the empty case, 90% of the ultrasonic energy is dissipated when the ultrasonic waves propagate in a compact GSS. To validate the feasibility of the detection principle, five grouting compactness cases (0%, 28%, 50%, 72%, and 100%) were artificially mimicked and tested. To improve the Signal-to-Noise Ratio (SNR), the time reversal algorithm was applied. The normalized amplitude of the focused signal in the time domain is used as an index to quantitatively reveal the compactness of GSS connectors. Experimental results confirmed that grout material significantly enhances damping effects. In addition, the damping ratios of the GSS connector and the grouting stuffing were experimentally investigated based on the logarithmic decrement of ultrasonic wave. A 2D numerical model verified that groutinduced damping causes exponential ultrasonic attenuation, aligning with theoretical predictions and experimental data. Therefore, the proposed damping effect-induced ultrasonic wave attenuation is viable for grouting compactness detection.
Key Words
damping effect; grouting defect; PZT; time reversal; wave attenuation
Address
(1) Dongdong Chen, Xiaojie Yue:
College of Civil Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, P.R. China;
(2) Juntao Fan:
CITIC Construction Co., Ltd., Beijing, 100027, P.R. China;
(3) Haorun Xu:
School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092, P.R. China.
Abstract
The advancement of novel data mining and optimization algorithms has significantly enhanced traditional engineering structural analysis models, particularly those based on swarm intelligence. This study delves into refining the neural assessment of shaft friction capacity in driven pile systems by exploring the social behavior of four hybridized algorithms: Wind-Driven Optimization (WDO), Spotted Hyena Optimization (SHO), Grasshopper Optimization Algorithm (GOA), and Moth–Flame Optimization (MFO). Four crucial influencing variables — pile length (m), diameter (cm), effective vertical stress (Sv), and undrained shear strength (Su) — are considered in constructing the requisite dataset. After applying optimized structures, each ensemble undergoes a sensitivity analysis based on its individual swarm size. The predictive precision of the models is compared using the results of two sensitivity analyses. Neural network simulations exhibit improved results with an increased number of neurons in a single hidden layer. The root mean square errors (RMSEs) for the training and test datasets, employing Multilayer Perceptron (MLP)-based solutions, are (0.05241, 0.32861, 0.06155, and 0.03874) and (0.04334, 0.18155, 0.05382, and 0.03626), respectively. In the training and testing datasets for proposed predictive models using WDO, SHO, GOA, and MFO, R2 values of (0.996, 0.853, 0.992, and 0.997) and (0.985, 0.732, 0.997, and 0.997) were found, respectively. Notably, MFO outperforms its counterparts when integrated with MLP for predicting engineering solutions.
Address
(1) Huanyang Xiao:
Zhejiang Geology and Mineral Technology Co., China LTD., China;
(2) Mesut Gör:
Department of Civil Engineering, Faculty of Engineering, Firat University, Elaz
