This paper investigates energy dissipation and operating time of a vibro-impact capsule robot powered by a compact LIR1040 lithium battery under pulse-width modulation (PWM) excitation. An improved mathematical model incorporating coil inductance and a nonlinear excitation force is developed and validated against experiments. Numerical simulations and laboratory tests quantify losses from viscous damping, Coulomb friction, and impact events, and show their dependence on excitation frequency and duty cycle. Analysis of hysteresis loops of impact force versus relative displacement and relative velocity clarifies how collision dynamics convert mechanical energy into irreversible loss. Higher frequencies generally favor forward locomotion with reduced impact losses, while lower frequencies produce larger instantaneous displacements and faster energy dissipation. Comparison of theoretical operating time based on battery capacity with operating time accounting for voltage decay reveals that voltage drop substantially shortens effective operation. Practical PWM tuning guidelines are provided to balance locomotion efficiency and battery endurance for biomedical and industrial applications.
This study presents a BIM-integrated automated shape quality control framework for precast concrete elements, designed to enhance accuracy, efficiency, and traceability in industrial inspection processes. The proposed five-stage workflow combines 3D laser scanning, point-cloud preprocessing, two-stage registration (edge-point alignment and Iterative Closest Point refinement), deviation analysis, and BIM-linked reporting. Using a dataset of 820 precast arch segments, the method achieved a mean registration RMSE of 1.58 mm, representing a 43% improvement in dimensional accuracy compared to manual inspection. The fully automated process reduced inspection time by 40% and achieved 100% defect-detection sensitivity while maintaining consistency under variable environmental conditions. Color-coded deviation heatmaps and digital audit reports were automatically linked to BIM object identifiers, ensuring full compliance with ISO 19650 and ISO 9001 standards for information and quality management. The results demonstrate that the proposed method provides a practical foundation for digital transformation in precast production and aligns with the objectives of Smart Structural Systems by establishing a data-driven, ISO-compliant, and scalable framework for automated geometric quality assurance.
With the increase of bridge service life, various loads, environmental corrosion and material aging may lead to the accumulation of structural damage, it is significant to identify structural damage to provide maintenance strategies and prevent collapse accidents. Abridge damage identification method based on fused multi-point deflection influence line area is proposed in this study. Initially, the variational mode decomposition (VIVID) technique is introduced to decompose the structural dynamic deflection under moving load, which will separate the dynamic fluctuation disturbance component from the structural low-frequency deflection response. In order to reduce the number of unknown variables to be solved and smooth the local fluctuations in the solution caused by noise, the B-spline basis functions are used to expand the influence line, which will transform the identification of influence line into solving the weight coefficients of base functions. Subsequently, the influence lines of the simply supported bridge before and after damage are derived, and a damage index based on the area difference of influence lines of fusing multi-point is proposed. Finally, a numerical simulating and experimental testing under various damage cases are studied to validate the feasibility of the proposed method. The dynamic fluctuation interference can effectively eliminate and the influence line can be accurately identified by proposed method. The fused damage index can effectively identify and localize structural damage under various damage cases, and the identification result is better and more stable than the damage index of single measurement point.
To improve the frequency sensitivity of a single tuned mass damper (TMD) and solve the complexity for multiple tuned mass dampers (MTMD) to determine the control mode and the effective number of TMDs, a new type of MTMD, namely rocking wall tuned mass dampers (RW-TMDs), is proposed in this paper based on the structural characteristics of rocking wall and the damping principle of TMD. To prove the effectiveness of RW-TMDs, theoretical analysis and numerical simulations were used to compare the damping effects of uncontrolled structures and controlled structures with TMD and RW-TMDs, respectively, under Gaussian white noise and different earthquake excitations. The results show that the RW-TMDs arranged along the height of the structure can effectively alleviate the frequency sensitivity of TMD, significantly reducing structural peak and root mean square value of acceleration and displacement. Besides, RW-TMDs with advantages of rocking wall structure can control inter-layer deformation mode and prevent the occurrence of layer collapse mechanism, efficiently. Furthermore, the RW-TMDs can mitigate structural damage degree by increasing the damping energy dissipation and reducing the plastic energy dissipation. Therefore, the RW-TMDs have a high potential in practical applications for vibration control of building.
To evaluate the load carrying capacity of a bridge, it is necessary to set up an appropriate analytical model for the bridge. According to the current guideline or manual, the load rating is calculated with the response correction factor obtained from the difference between the response measured through the field load test and the response by the analytical model. According to the criteria for evaluating the load capacity, for conservative evaluation, it is suggested to use the response correction factor at the location where the axle weight of the test vehicle is directly applied, and to select the location with the largest response correction factor. The purpose of this study is to propose and examine a method for estimating the load rating of PSC-I girder bridges by updating the model that minimizes the difference between the measured and analyzed responses with a real-coded genetic algorithm (RCGA). In addition, the reliability of the method for estimating the load rating through the available response correction factor was investigated. The results were compared with those of the genetic algorithm (GA) model updating method. In the study, a pseudo-static load test method was proposed and applied as a field load test for PSC-I girder bridges, and its advantages were analyzed.
Identifying damage at an early stage is crucial for preserving the integrity of structures. Damage in structures often manifests as adverse changes within the system, which can propagate over time and ultimately lead to structural failure. This research introduces a novel damage index, referred to as the P-Index, for detecting damage in beam-type structures. The P-Index is developed using the Reduced Interference Distribution-a bilinear time-frequency function-along with matrix expansion techniques. To validate the effectiveness of the proposed method, laboratory tests were conducted on a steel beam. The results demonstrate the capability of the P-Index to accurately identify damage. Key advantages of this method include high accuracy, ease of implementation, no requirement for input force measurement, reduced sensor usage, and the ability to work with recorded signals without noise removal.
Wind tower structures are continuously exposed to dynamic loads and harsh environmental conditions, leading to the gradual degradation of critical tower joints, which compromises structural integrity and operational safety. This study proposes a combined piezoelectric impedance sensing and deep learning approach for low-cost condition monitoring of bolted joints in wind turbine support structures. A hybrid 1D CNN (convolutional neural network) and LSTM (long short-term memory) model is developed to enhance damage detection under operational challenges such as blade rotation, vibrations, and environmental noise. Experimental validation on awind turbine joint model, where different levels of fastener looseness are systematically introduced, demonstrates the model's effectiveness in detecting subtle changes in joint rigidity under various wind and noise conditions. Comparative analysis with conventional 1D CNN models confirms that the proposed approach significantly improves both accuracy and robustness, enabling reliable real-time monitoring and early damage prediction.
This study proposes a multitask learning (MTL)-based framework for the integrated prediction of key structural performance parameters in steel-concrete composite deck slabs, including mid-span deflection and residual fatigue life. Traditional single-task approaches focus on isolated performance indicators and fail to capture the complex nonlinear interactions among design variables and the inherent coupling between structural responses. To address these limitations, a comprehensive hybrid dataset of 152 validated records was compiled by integrating experimental data, finite element analysis (FEA) simulations, and design-code-based augmented data. Input variables were standardized and optimized through feature importance analysis, and a multitask artificial neural network (MT-ANN) was developed to simultaneously predict both target outputs. The model performance was benchmarked against established machine learning algorithms including Random Forest, XGBoost, Support Vector Regression, and Long Short-Term Memory networks. The proposed MT-ANN consistently outperformed single-task baselines in accuracy and generalization. SHAP-based interpretability analysis further revealed that geometric parameters and material properties contribute differently across prediction tasks, confirming the value of the multitask architecture. The findings demonstrate that multitask learning provides a robust and scalable solution for holistic assessment of composite deck structural performance. The developed model supports structural design optimization, service life evaluation, and predictive maintenance planning. Its compatibility with digital twin and BIM-integrated environments further enhances its potential for real-time structural health monitoring in modern infrastructure systems.
The identification of dynamic parameters and vibration control are fundamental to understanding the dynamic behaviour of civil engineering structures and constructing earthquake-resistant systems. Despite previous research, a critical need remains for a deeper understanding of comprehensively determining these parameters experimentally using Frequency Response Functions (FRFs). Furthermore, there is a lack of studies investigating vibration control in multi-degree-of-freedom (MDOF) structures through the modification of their physical properties, specifically mass. This study aims to experimentally identify dynamic parameters using FRFs and investigate the vibration control performance of a three-story reduced-scale steel portal frame by adding masses to its different stories. The model is excited using a white-noise signal from a shaking table, while responses are recorded at each floor using piezoelectric accelerometers. FRFs are computed from input-output signals using spectral analysis techniques, allowing natural frequency identification from resonance peaks. Damping ratios are estimated via the half-power bandwidth method, and mode shapes are extracted using Singular Value Decomposition (SVD) of the FRF matrix. In the second part, the influence of added masses on the model's dynamic parameters, specifically the natural frequencies and steady-state acceleration responses under harmonic excitation, was analysed. These results are compared with a finite element model (FEM) to evaluate experimental-numerical correspondence. Results show good agreement, confirming the FEM accurately reproduces the model's real dynamic behaviour. It is also observed that both the location and magnitude of added masses affect natural frequencies differently, particularly the first frequency. Moreover, optimal added mass placement significantly reduces response amplitudes under harmonic excitation. This study offers valuable insights into tuning structural frequencies to prevent resonance, especially under intense excitation, and highlights effective locations for Tuned Mass Damper (TMD) systems to improve vibration mitigation and the overall performance of MDOF structures.
Many of the structures and mechanical systems used have elements in contact. Determining the contact pressures and contact areas that occur after the applied load in the systems in contact is extremely important for the structure to continue its function. On the other hand, it is also known that one of the most important parameters affecting the contact pressures and contact areas of the elements in contact is the material of these elements. Although many studies have been carried out in the literature to determine the contact pressures and areas of the structures in contact with various analytical and numerical methods, it seems that no study has been conducted on how the contact pressures and contact areas will be affected by the changes in the mechanical properties of the materials in these structures. In this context, in this study, friction stir process was applied to structural steel with different parameters and the effects of this process on the strength and elongation values of the steel were examined. Afterwards, the changes in strength and elongation values were reflected in the models created with finite elements (FEM) and artificial neural networks (ANN), and the effects of material properties changing at changing loads on contact pressures and distances during contact were investigated. As a result of the investigations, it was determined that after the friction stir process, the strength value of the steel increased and the elongation values decreased compared to the base material in all parameters. Also, it has been determined that the increase in the strength of the material is extremely effective on the increase of contact pressures, and the contact areas generally decrease with the decrease in the elongation values of the material.
For a truss structure, the most common damage manifestation is the reduction of elastic modulus due to the degradation of material properties and the reduction of effective cross-sectional area caused by corrosion, etc. Based on the single parameter identification gradient regularization method (GRM), this paper proposes an elastic modulus and cross-sectional area dual-parameter joint identification method. Firstly, a mathematical and physical model for dual-parameter joint identification is constructed, followed by the derivation of the solution formula for dual-parameter identification based on GRM and the development of a general finite element solution program using MATLAB. A series of numerical simulation experiments are conducted. The results of which show that the identification results under different working conditions are in good agreement with the actual design parameters, and the solution precision can be as low as 10-7 with less than 10 iterations, and the error is below 4% under 12% measurement noise. The method is further applied to a steel truss experimental model, the results of which furtherly demonstrate the high accuracy and robustness of the method.
To go beyond vision-based surface defect inspection techniques for concrete structures, immersive 3D collaborative environments that enable remote inspections are being actively studied. This paper proposes a real-time remote collaboration system for the automated assessment of concrete infrastructure by combining augmented reality (AR) and virtual reality (VR) technologies with impact acoustic inspection. By connecting to a human-in-the-loop framework, a field inspector (onsite AR operator) and an office manager (remote VR expert) can collaborate in real time during the inspection process. The system objectively detects internal defects in concrete by analyzing acoustic signals, thereby reducing human errors inherent in conventional hammer-sounding methods. The automated 3D-annotation feature of the proposed system enables a real-time visualization of defects within a digital model, minimizing discrepancies between actual inspection points and recorded data. Enhanced remote collaboration using AR and VR facilitates intuitive interactions between on-site inspectors and remote experts within a shared virtual environment, effectively reducing the reliance on large expert teams. An experimental validation conducted in a tunnel demonstrated the effectiveness of the system, in which an on-site inspector equipped with an AR headset and a remote expert with a VR device collaboratively performed the inspections. Our integrated system represents a viable framework for the automated inspection of concrete infrastructure.
The structural health monitoring (SHM) of utility tunnels is critical for urban safety, where automated crack detection plays a vital role in early damage identification. Crack detection in utility tunnels remains challenging due to the subtle appearance of cracks and complex background interference, which often degrades the performance of general-purpose algorithms. To address this, DG-YOLOv8, a lightweight instance segmentation model enhanced with attention mechanisms, is proposed. Specifically, a Ghost module is integrated into the backbone to reduce redundancy and computational cost, while a Dynamic Head introduces a feature selection mechanism to improve adaptability. Experimental results demonstrate that DG-YOLOv8 outperforms the baseline YOLOv8, achieving a 1.6% increase in mAP50 while reducing the number of parameters, GFLOPs, and inference time by 37%, 28%, and 49.5%, respectively. The proposed DG-YOLOv8 model offers a robust and efficient intelligent solution for automated visual inspection, contributing to the development of smart SHM systems for utility tunnels.
Smart structures have now been revolutionized with the introduction of nanomaterials to enhance the responsiveness, efficiency and sustainability of systems across a broad range of applications including education. In this paper, the application of nanomaterials in smart structures will be discussed with particular attention given to the ways of making the systems more responsive, efficient in the learning environment, and sustainable. With the unique mechanical, electrical and thermal properties of nanomaterials, it is possible to develop adaptive and intelligent systems that will be capable of detecting and reacting to environmental stimuli instantly. This paper will explain the importance of nanomaterials in the advancement of smart structural technologies focusing particularly on its advantageous energy efficiency, durability and multifunctionality. Further, the paper also highlights the potentials of these innovations in learning environments where smart infrastructure has the potential to improve the learning experience, streamline the utilisation of resources and support interactive and technology enabled learning activities. There are also issues of scalability, cost and long term performance. The results indicate that smart structures made of nanomaterials are an opportunity on the way to sustainable development and educational systems of the next generation.
The use of smart materials provides a compact and efficient solution for vibration attenuation in structures. This research investigates a flexible beam equipped with a surface-mounted piezoelectric patch connected to a tuned resistive-inductive shunt circuit, considering both series and parallel configurations under different boundary conditions. The electromechanically coupled equations are derived using Hamilton's principle and Euler-Bernoulli beam theory, and solved via the finite element method with modal reduction. In order to ensure methodological rigor and facilitate experimental reproducibility, the analyses are deliberately performed at relatively higher frequency ranges than those typically observed in practical structural applications. The results show that passive shunt damping efficiency depends strongly on the interaction between mechanical and electrical parameters, with boundary conditions, patch position, and resistance tuning each playing a decisive role. Series and parallel shunt topologies exhibit complementary advantages depending on the control objective. This study lies in the comparative assessment of metaheuristic optimization methods for determining optimal resistance values, revealing faster and more stable convergence with Particle Swarm Optimization, while Genetic Algorithm achieves comparable performance despite stochastic fluctuations. The findings provide practical design guidelines for optimizing piezoelectric shunt systems, contributing to the advancement of passive vibration control strategies in lightweight structures.
Accurate control of hanger rod forces is crucial for ensuring structural safety and effective force distribution in long-span bridge construction. Traditional contact-based force measurement methods, such as strain gauges and accelerometers, face challenges including high costs, susceptibility to environmental interference, and intensive equipment demands, limiting their practical efficiency in complex environments. To address these limitations, this work proposes an innovative multi-stage construction method that integrates the influence matrix method with ground-based microwave radar interferometry to achieve rapid, high-precision, and non-contact force detection during hanger rod tensioning. Specifically, multi-stage construction employs a stepwise, iterative approach, dynamically adjusting rod forces in multiple stages based on real-time radar monitoring. This significantly reduces the risk of construction errors, minimizes equipment requirements, and enhances operational feasibility and safety. Based on this, a series of field tests on an actual long-span bridge validated the proposed approach, showing that after two tensioning stages, the rod forces approached target values closely, maintaining errors within 10%. The results confirm that this integrated multi-stage approach provides a practical, safe, and efficient solution for hanger rod tensioning in long-span bridges, offering substantial improvements in construction accuracy, efficiency, and applicability.
This study explores the application of nonlinear wave propagation in smart plates for structural health monitoring (SHM), specifically focusing on the future directions enabled by computer vision technologies. The system under consideration involves a smart rectangular plate comprising two distinct piezoelectric materials: PZT-4 on the top surface and PZT-5H on the bottom. The plate is subjected to an electric potential, which activates the piezoelectric materials, inducing voltage-based stress distributions that influence wave propagation dynamics. To capture the plate's behavior, high-order shear deformation theory (HSDT) is employed, incorporating von-Karman nonlinearity for a more accurate representation of large deformations. The Hamiltonian formulation is used to derive the governing equations for the system, which account for both linear and nonlinear wave behaviors. The nonlinear wave dynamics of the structure, including group and phase velocities, are analyzed to provide insights into the SHM capabilities. Nonlinear group and phase velocity information, particularly as it relates to the material heterogeneity and varying thicknesses of the plates, is presented as a key feature for future SHM methodologies. This study highlights how computer vision techniques can be leveraged to monitor these nonlinear phenomena in real-time, enabling precise damage detection and health assessment of complex, piezoelectric-enabled structures. Future directions aim to enhance the accuracy and efficiency of SHM systems by integrating advanced computational techniques, further extending the application of nonlinear wave propagation analysis in smart materials and structures.
Damage detection of steel tube in in-service steel grid structures (SGSs) is essential for maintaining structural safety. Ultrasonic guided waves (UGWs) enable non-destructive detection of micro-damage using single-point excitation. UGWs hold significant potential for inspecting tube members. However, tube members in SGSs are typically connected by spherical joints, and echoes reflected from the joint can potentially obscure damage identification using UGW signals. To address this issue, an enhanced UGW-based damage detection method for steel tube with spherical joints (STSJs) is proposed. First, the low-frequency torsional T(0,1) mode UGW is selected as the excitation signal. Next, a bidirectional control strategy for UGW excitation and reception is proposed and incorporated into the UGW-based damage detection method. This strategy employs the pulse-echo method, enabling the same set of transducers to both excite and receive signals. Two transducer rings are positioned a quarter wavelength apart, with a phase shift of pi/2 between their signals. This configuration not only suppresses excitation signals in non-detection direction, but also attenuates reception signals from that direction, effectively mitigating interference caused by spherical joints and significantly enhancing the signal-to-noise ratio for damage detection. Subsequently, the reflection coefficient method is used to locate damage in STSJs. This strategy facilitates UGW-based damage detection in STSJs without the need for reference signals. The effectiveness of the proposed method is validated through an experiment on a notched STSJ specimen. Results indicate that by eliminating the interference signals, the proposed method achieves a damage localization error of 0.08%, demonstrating potential applicability of the method for practical damage detection in STSJs.
Seismic isolation systems are widely used in structural engineering to mitigate earthquake-induced damage by decoupling structure displacement from ground motion. Building on this principle, this study investigates the effectiveness of a novel eddy current damping (ECD) mechanism integrated into a roller seismic isolation bearing (RSIB) system to enhance seismic performance. The proposed non-contact energy dissipation device controls seismic-induced displacements while minimizing wear-related issues in traditional friction-based damping systems. A scaled RSIB model coupled with the ECD device was tested under free vibration and seismic excitation. Besides, a numerical model was developed to simulate the response to both far-and near-fault earthquakes and evaluate RSIB performance. The results indicate that increasing braking force reduces isolator displacements but leads to higher absolute accelerations. Displacement reductions of up to 72% were achieved, ensuring that movements remained within the physical limits. However, higher braking forces also increased peak acceleration. The equivalent damping ratio improved by up to 61.58%, confirming the ability of the ECD to enhance energy dissipation. The numerical model accurately reproduced the experimental results, and the key dynamic parameters of the RSIB and ECD systems were identified. The findings demonstrate that eddy current damping can be effectively integrated into roller seismic isolation systems to enhance energy dissipation and displacement control in RSIB systems. However, the optimal braking force must be carefully selected based on the expected seismic demands to achieve a trade-off between displacement control and acceleration performance. Future research should focus on full-scale implementation and an ECD device optimized for target-breaking force.
This study applies the digital Real-Time Hybrid Testing with a Shake Table (RTHT-ST) method to verify the control performance of the Polynomial Sliding Isolators with Variable Curvature-Floor Isolation System (PSIVC-FIS). The Hardware-in-the-loop (HIL) simulation is also used in this research, where both primary structures and subsystems are numerical models embedded in the single-task processor environment, with real-time communication through the shared common RAM network hardware (SCRAMNet) with optical fiber interface (digital) or the traditional Analog-to-Digital/Digital-to-Ananlog (AD/DA) converters with BNC connectors (analog). The dynamic characteristics and operational limitations of the shake table are analyzed through the HIL simulation before and after the digital RTHT-ST to ensure system stability, equipment safety, and to investigate the key issue related to the discrepancy between the Shaking Table Test (STT) and the digital RTHT-ST. The adopted control device, the PSIVC-FIS, is tested as a subsystem through the digital RTHT-ST using the Multi-Axial Seismic Test (MAST) system at the southern branch of the National Center for Research on Earthquake Engineering (NCREE). The passive control performance is compared with its STT results conducted earlier using the shaking table at the northern branch of the NCREE.
