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CONTENTS
Volume 36, Number 2, August 2025
 

Abstract
River transportation is a significant component of the overall transportation system. Typically, there are surveillance cameras implemented on river bank to avoid collisions between ships and bridges across rivers. However, some of the routes may only contain limited or malfunctioned cameras, making the monitoring of ships occluded. In this study, we propose a deep learning-based framework that identifies the trajectory of a ship in the real world by using the surveillance videos. The proposed framework consists of three modules: object detection, object tracking, and coordinate projection. We implement the Mask RCNN model for object detection to determine the ship position in each video frame and compute the ship centroid as the image coordinates of the ship. We then employ DeepSort as the object tracker, which matches and tracks the detected object in each frame and combines all instances of object detection in the video to output the ship trajectory. For coordinate projection, we incorporate the P3P method and Zhang's algorithm to determine the intrinsic matrix and extrinsic matrix, respectively. The image coordinates of the ships are therefore converted into world coordinates. In addition, we develop an approach to calibrate the ship trajectory out of the coverage using the results from multi-camera triangulation. Meanwhile, the continuity in ship trajectory is enhanced as well. Results demonstrate that the ship trajectory becomes smoother in the evaluation using acceleration variability and directional change. The proposed approach reduces the acceleration variability score from 2.75 to 1.54 and improves the directional hange score from 0.85 to 0.09.

Key Words
coordinate projection; deep learning; instance segmentation; object tracking; ship trajectory identification; triangulation

Address
Department of Civil Engineering, National Taiwan University, Taipei, Taiwan.


Abstract
This study introduces a novel approach for vibration control in smart hybrid nanocomposite-reinforced sport structures, utilizing the Time-Delay Feedback Controller with Derivative Action (TDF controller with DA). The Carrera unified formulation (CUF) is employed to create a mathematical model that captures the dynamic behavior of these structures under high-impact and oscillatory forces. The TDF controller with DA incorporates time-delay feedback combined with derivative action to improve system stability and mitigate vibrations under dynamic loading. The hybrid nanocomposite material, comprising ZnO (zinc oxide), and GO (graphene oxide) enhances the mechanical and electromechanical properties of the sport structures, making them ideal for high-performance applications. Functionally graded nanocomposite face sheets, integrated with piezoelectric sensor-actuator layers, allow real-time adaptive control, dynamically adjusting damping characteristics in response to external disturbances. Rigorous simulations demonstrate that the TDF controller with DA outperforms traditional control methods, significantly reducing vibrations and enhancing dynamic stability. This research establishes a foundation for developing next-generation sport structures that offer superior resilience and longevity. The proposed approach shows great promise for improving the design and performance of sports equipment and infrastructure, particularly in environments with frequent high-impact forces.

Key Words
adaptive structures; carrera unified formulation; deep learning; hybrid nanocomposite; intelligent vibration control; sport structures; swarm intelligence

Address
(1) Guochen Zhang:
School of Physical Education, Anyang Normal University, Anyang 45000, Henan, China;
(2) Bing Lin:
School of Physical Education, Chongqing Preschool Education College, Chongqing 404047, China.

Abstract
This paper presents a new technique for detecting defects in piezoelectric materials located inside smart structures, mainly the troubleshooting of functionally graded piezoelectric (FGP) porous plates excited by an electric field. This research accounts for the Von-Karman nonlinearity to study the influence of mechanical and electrical loadings on the dynamic behavior of world material. Maxwell's equations are used to describe the coupling of electric fields with the piezoelectric properties of the plate, and porosity is closely examined for its impact on the defect detection process. Hamilton's principle is used to obtain the equations of motion, which can replicate the system's non-linear dynamics. The harmonic differential quadrature method (HDQM) is used to achieve numerical results, while equations governing the response of the system under different boundary conditions can be discretized appropriately. Deep learning models known as deep neural networks (DNN) are used to validate the mathematical model and extract more information from this complex, large dataset, which is obtained from these simulations. The DNN model provides a robust framework for detecting defects based on learning the complex relationships between different related features of the system, and its efficient classification aids in the detection and classification of defects. The study also explores the parameter selection and optimization in the DNN algorithm, so as to balance the model accuracy and computational efficiency. The results are important as they provide a cost-effective, accurate technique to detect defects in piezoelectric materials and help in smart structure health monitoring, which is a rapidly growing field. The experimental setup detailed in this research can form the basis for future developments of structural diagnostics and the implementation of smart materials in engineering applications.

Key Words
deep neural networks; dynamic simulation; functionally graded plates; nonlinear stress-strain equations; piezoelectric material defects

Address
(1) Suleiman Ibrahim Mohammad:
Electronic Marketing and Social Media, Economic and Administrative Sciences, Zarqa University, Jordan;
(2) Suleiman Ibrahim Mohammad:
Research follower, INTI International University, 71800 Negeri Sembilan, Malaysia;
(3) Asokan Vasudevan:
Faculty of Business and Communications, INTI International University, 71800 Negeri Sembilan, Malaysia;
(4) Asokan Vasudevan:
Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160, Thailand;
(5) Abdelmajeed Alkasassbeh:
Civil Engineering Department, Faculty of Engineering, Al al-Bayt University, Mafraq 25113, Jordan;
(6) Omar Asad Ahmad:
Department of Civil Engineering, Faculty of Engineering, Amman Arab University, 11953 Amman, Jordan;
(7) A'kif Alfugara:
Independent Researcher;
(8) Nabil Ben Kahla:
Department of Civil Engineering, College of Engineering, King Khalid University, PO Box 394, Abha 61411, Kingdom of Saudi Arabia;
(9) Nabil Ben Kahla:
Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia;
(10) Nejib Ghazouani:
Mining Research Center, Northern Border University, Arar 73213, Saudi Arabia;
(11) Murat Yaylacı:
Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Turkey;
(12) Murat Yaylacı
Turgut Kıran Maritime Faculty, Recep Tayyip Erdogan University, 53900, Rize, Turkey.

Abstract
This paper discusses a new multimodal shunt circuit architecture for simultaneous subsonic flutter control and energy scavenging for aerospace, mechanical, etc. The system includes a finite element electromechanical modeling approach to investigate a multi-layer composite plate with an embedded piezoelectric. We construct a resonant shunt circuit in parallel, utilizing a parallel shunt circuit with two modes operating in parallel to dampen unwanted vibrations. This architecture allows us to extract energy from the plate and damp unwanted vibrations, which ideally relates to subsonic flutter. The finite element model accounts for the structural dynamics of the plate and the piezoelectric dynamics, but also allows us to tune the energy dissipation and scavenging process. The resonant shunt circuit is tuned to specific frequencies and therefore aids the dissipation of energy from the vibration modes and dampens any unwanted oscillation. The study provides a comprehensive description of tuning the shunt circuit and describes the performance of the system under varying flow conditions. To describe how the piezoelectric plate will be subjected to airflow, a diagram was included that provides a good opportunity to visualize part of this system's mechanics. As shown, the presented results indicate that this implementable structure represents innovative advancements in flutter control and energy harvesting and is an exciting opportunity for further work on adaptive structures within aerospace, automotive, and energy-efficient industries.

Key Words
composite plate; energy scavenging; finite element modelling; multimodal shunt circuit; piezoelectric component; subsonic flutter control

Address
(1) Suleiman Ibrahim Mohammad:
Electronic Marketing and Social Media, Economic and Administrative Sciences, Zarqa University, Jordan;
(2) Suleiman Ibrahim Mohammad:
Research follower, INTI International University, 71800 Negeri Sembilan, Malaysia
(3) Asokan Vasudevan:
Faculty of Business and Communications, INTI International University, 71800 Negeri Sembilan, Malaysia;
(4) Asokan Vasudevan:
Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160 Thailand;
(5) A'kif Alfugara:
Independent Researcher;
(6) Mohammed Rauf Abdullah:
Department of Construction and Project Management, College of Engineering, Alnoor University, Mosul, Iraq;
(7) Habib Kraiem:
Center for Scientific Research and Entrepreneurship, Northern Border University, Arar 73213, Saudi Arabia;
(8) Yasser Alashker:
Department of Civil Engineering, College of Engineering, King Khalid University, PO Box 394, Abha 61411, Kingdom of Saudi Arabia;
(9) Yasser Alashker:
Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia;
(10) Murat Yaylacı:
Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Turkey;
(10) Murat Yaylacı:
Turgut Kıran Maritime Faculty, Recep Tayyip Erdogan University, 53900, Rize, Turkey.

Abstract
This study investigates the use of a hybrid artificial intelligence (AI) model combining Recurrent Neural Networks (RNN), Fuzzy Inference (FI), and Particle Swarm Optimization (PSO) for predicting the position and severity of multiple transverse cracks in glass-fiber-reinforced-laminated-composite (GFLCB) beams. To assess the model's accuracy, a verification was conducted using a GFLCB intact and a double cracked beam. Finite Element Analysis (FEA) was employed to determine the first three relative natural frequencies (RNFs) under double crack conditions. Then the obtained RNFs are used to train the programs to locate the crack location and depth. The supremacy of the model over mPSO, RNN, and RNN-mPSO is verified. The maximum error percentage in calculation of first crack location by RNN-FUZZY-PSO, mPSO, RNN, and RNN-mPSO is found to be 3.08%, 5%, 7.2%, and 8.11% respectively. While in detection of crack locations, the error percentage are 1.1%, 5%, 7.2%, and 8.11%, respectively. Further RNN-FUZZY-PSO is used to diagnose the crack severity and locations of multiple cracks (nine crack) in hybrid GFLCB. Results indicated that the RNFs are significantly influenced by the number and severity of the cracks. The predicted crack positions and severities by the method are with a marginal error of 1.53% and 1.3%, respectively. The model shows improved accuracy as the number of cracks increased, especially for the ninth crack, where the mean square error is 0.01154, with maximum error percentage of 0.8%. The findings demonstrate the proposed AI model's effectiveness for precise identification of crack positions and severities in GFLCB structures with multiple cracks.

Key Words
finite element analysis; laminated composite; RNN-Fuzzy-PSO model; severity; transverse-multi-cracks

Address
(1) Sarada Prasad Parida:
Konark Institute of Science & Technology, Mechanical Engineering, Bhubaneswar, Odisha, India;
(2) Saritprava Sahoo, Pankaj Charan Jena:
VSS University of Technology, Production Engineering, Burla, Odisha, India.

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