The rubber-tyred gantry (RTG) crane is employed as an essential piece of equipment for container handling in port operations. The RTG crane owns time-varying characteristics and parametric uncertainties. Meanwhile, the twisting of the container during its operation has a detrimental effect on the operation efficiency. In view of this, an improved adaptive control method based on integral reinforcement learning (IRL) is proposed in this paper. The mechanism model of the RTG system is developed, and the dynamic characteristics are analysed. Then, an IRL-based adaptive controller is designed and the involved positive definite Lyapunov matrix P is optimised to improve the control performance. In contrast to classical adaptive control methods, the proposed method calculates P based on real-time state variables, thereby eliminating model reliance and guaranteeing adaptive capacity. Finally, the effectiveness of the proposed method in enhancing anti-twisting performance is verified by digital and physical experiments. In the digital experiments, compared with the classical adaptive method, the load twisting settling time is reduced by 1 s, and the maximum twisting angle is decreased by approximately 0.7 degrees. In the physical experiments, despite the influence of practical friction and disturbances, the settling time is still reduced by about 1 s. These results show that the proposed scheme possesses both theoretical effectiveness and engineering practicality.

Long-term pavement performance is a key topic in highway engineering. By diving deep into research on pavement systems, we can bring together past, fragmented knowledge and experiences into a solid, comprehensive engineering theory. This essentially helps guide practical work like pavement design, construction, maintenance, and management. In this research, we look at using a mentoring system for automatic monitoring of pavement performance. By placing various sensors in different positions like the road surface, base, and slopes, a sensor network powered by Internet of Things technology is created. This setup allows for accurate and ongoing observation of factors like weather, physical condition, mechanical responses, and structural changes. Given the large volume of data and the need for real-time analysis, a data from sensors measuring temperature, humidity, pressure, asphalt strain, and displacement are used to train a deep learning model based on a Convolutional Neural Network (CNN) algorithm. This model helps predict multi-point displacement in the pavement, which allows us to detect issues like pavement damage. Impressively, the CNN model achieved accuracy, regression rates, and F-score of 93.51%, 91.63%, and 90.64% respectively. To improve the experimental section of a deep learning study, we compared the performance of the proposed model against several established or simpler algorithms (baselines) in the literature such as K-Nearest Neighbors (K-NN), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM). This contextualizes the model's efficacy and demonstrates its advantage over existing methods. This study showcases how different sensors can support deep learning algorithms in the assessment of pavement performance over the long term.

In long-distance heavy-load transmission systems, the unique high load-bearing capacity and trajectory constraint characteristics of the double-idler rack-and-pinion mechanism can significantly improve the reliability of the transmission system. However, engineering practice has revealed that factors such as tooth profile distortion caused by residual stress from rack rolling, flatness errors of mounting surfaces, and linear expansion due to environmental temperature changes can markedly alter the dynamic meshing characteristics of the rack and pinion. During the meshing process of the double-idler rack-and-pinion, the deformation of the rack and pinion and installation errors can lead to meshing impacts, thereby generating significant impact noise. This paper analyzes abnormal meshing states influenced by changes in center distance and pitch, constructs a defective meshing model for the rack and pinion, and further identifies factors more sensitive to defective meshing impacts through dynamic simulations. Finally, the paper proposes a flexible floating idler shaft structure that effectively reduces meshing impacts. The results demonstrate that the proposed structure yields significant improvements, particularly in the direction of pitch variation, which is more sensitive to vibrations. Specifically, the effective vibration values for random pitch micro-variations and sudden rack pitch changes are reduced by 60.5% and 23.4%, respectively, while those for sudden changes in rack center distance are reduced by 57.01%. This research provides new methodological support for optimizing the dynamic characteristics of precision rack-and-pinion transmission systems.

This paper presents a hybrid fuzzy logic–grey relational analysis (Fuzzy–GRA) framework for multi-objective optimization of active vibration control (AVC) in smart composite beams. A fuzzy-adaptive Linear Quadratic Regulator (LQR) is developed, in which the LQR weighting matrices are adjusted online based on a grey relational grade that synthesizes vibration attenuation, control energy, and robustness metrics. A finite-element model incorporating piezoelectric actuator–sensor coupling is used to generate a hypothetical modal-test dataset, and both numerical simulations and laboratory experiments on an aluminium cantilever beam validate the method. Simulation results show up to 25 % and 13.6 % improvements in vibration attenuation over deterministic and genetic-algorithm-tuned LQR, respectively, while reducing control energy by 12.5 %. Experimental trials confirm 29.7 % and 14.3 % attenuation gains, 15.3 % energy savings, and a 41.7 % enhancement in robustness under ± 10 % parameter variations. Environmental robustness tests demonstrate only a 2.1 % performance drop under a 20 °C temperature increase, compared to an 8.1 % drop for conventional tuning. One-way ANOVA confirms that the observed improvements are highly significant (F ≫ F₍₂,₁₂,₀.₀₅₎). The proposed Fuzzy–GRA approach thus offers a mathematically rigorous yet practical strategy for tuning AVC gains under uncertainty, with promising applications in structural health monitoring and precision engineering.

his paper reports the design, fabrication, and experimental validation of smart tilted fiber Bragg grating (TFBG) sensors embedded within the internal structure of wooden beams for real-time strain and deflection monitoring. Pine and oak specimens were instrumented with five multiplexed FBGs distributed along the span and tested under a constant end load to capture the strain field during bending. To mitigate temperature-induced bias in the deflection analysis, a dedicated reference FBG was placed in a non-deflecting zone and used to compensate the individual temperature response of each sensing element. The optical transducers were encapsulated in epoxy-impregnated carbon fabric to ensure reliable strain transfer, mechanical protection, and long-term stability without compromising the host material. Wavelength-shift demodulation and in-situ calibration provided separate estimates of strain and temperature, enabling accurate reconstruction of the beam shape. The temperature-compensated measurements showed close agreement with Euler–Bernoulli beam predictions for both wood species, with correlation coefficients exceeding 0.87 across all trials. The sensing system exhibited repeatable behavior and low hysteresis, while the embedded configuration preserved the structural integrity and allowed unobtrusive installation. Overall, the results demonstrate that smart TFBG sensors are a practical and effective solution for embedded structural health monitoring of timber members, offering high accuracy, electromagnetic immunity, and seamless integration into load-bearing components. The approach is readily scalable to multi-sensor arrays and mixed materials, paving the way for continuous monitoring of civil and architectural wooden structures.

In complex environments such as industrial sites and rail transit, communication equipment often faces multi-source interference from mechanical vibration and structural noise, which seriously affects its signal quality and transmission stability. Although previous studies have explored the influence mechanism of a single interference source, there is still a lack of in-depth understanding and quantitative modeling of the coupled interference effect of vibration and noise. To this end, this paper builds an experimental platform based on the ESP32 Wi-Fi communication module, which includes controllable electromagnetic vibration and sound pressure loading, and collects communication performance indicators (RSSI, BER, throughput and delay) and synchronous physical disturbance data under different interference conditions. Through multivariate statistics and variance analysis methods, the interaction law between vibration frequency, amplitude and noise sound pressure level is revealed for the first time. It is found that the combination of the two under medium and high intensity conditions will cause significant nonlinear amplification effects, which will have a synergistic degradation effect on communication performance. The long short-term memory neural network (LSTM) is further introduced to construct a time series prediction model under multi-disturbance environment. The results show that the model has excellent fitting accuracy (R2 > 0.97) in RSSI and throughput prediction tasks, which is better than the comparison models such as SVM and polynomial regression, and has good feedforward control potential. The study also proposed communication anti-interference optimization suggestions and equipment structure improvement strategies suitable for industrial and rail scenarios, providing a theoretical basis and experimental support for the intelligent adaptive design of wireless communication systems in high-interference environments.

The inhomogeneity of the cross-sectional grain of Sitka spruce wood was analyzed using image processing techniques, and the influencing mechanism of this inhomogeneity on the acoustic vibration properties of the wood was investigated. The inhomogeneity of grain width was characterized by the average deviation of the growth ring width and latewood rate of the test specimens. The inhomogeneity of grain position distribution was characterized by the average deviation of the variation in growth ring width. The influence of the inhomogeneity of these grains on the acoustic vibration properties of the wood was investigated respectively. The results showed that as the inhomogeneity of the growth ring width and the growth ring width variation increase, the values of the specific modulus of elasticity (E/ρ), acoustic radiation quality constant (R), and acoustic impedance (ω) of the wood decrease, while the value of the loss angle tangent (tan δ) increases. As the inhomogeneity of the latewood rate increases, the values of E/ρ and R of the wood decrease, and the value of tan δ increases. The inhomogeneity of the latewood rate has no significant effect on ω. When the width of growth rings was between 0.14 cm and 0.17 cm while the deviation of the growth ring width ranged from 0.024 to 0.036 and the deviation of the width variation ranged from 0.016 to 0.020, the wood had relatively large values of E/ρ and R and a relatively small tan δ value, leading to better acoustic vibration performance. Similarly, when the latewood rate was between 15% and 20% and the deviation of the latewood rate ranged from 0.030 to 0.045, the wood had relatively large values of E/ρ and R and a relatively small tan δ value, leading to better acoustic vibration performance. This result provides more reference data for the rational selection of materials for musical instruments.

It is of great significance to study the deep integration of manufacturing technology and new-generation information communication technology under vibration interference of machine tools to improve the intelligence level of CNC machine tools. In this paper, a numerical control manufacturing workshop affected by vibration in discrete manufacturing is taken as the research background, and a solution for a digital workshop operation simulation platform based on the industrial internet is proposed. By constructing the simulation environment of the operation process of the digital factory, the generation and transmission of manufacturing information in the digital factory are simulated. The application architecture of the machining workshop based on a numerical control simulation platform is proposed, and the business process of the numerical control machining workshop is analyzed. Then, the key technologies of NC machine tool modeling, synchronous mapping of data and model, data integration, and fusion are studied. Through the integration and implementation of the NC machine tool simulation platform in the machining workshop, the top-down data instructions can be issued accurately, and the bottom-up feedback information can be confirmed in time. Finally, the system is applied to the electronic information and ship machining workshop to verify the effectiveness of the system framework and method proposed in this paper.

A Ms5.2 earthquake occurred in Longyang District, Baoshan City, Yunnan Province on 2 May 2023. The earthquake caused some degree of damage to power facilities and roads. Since then, small and medium sized earthquakes have occurred frequently in this region. In order to better analyze the characteristics of ground motion in this area and the coping strategies of related bridges, the response spectra, omnidirectional response spectra and omnidirectional representations of other indexes of intensity measure from records of strong ground motion were analyzed using the seismic records recorded by stations near the epicenter. Four typical bridges near the epicenter were selected for modeling and seismic response analysis to derive their damages under the action of ground motion, and structural displacements of the bridges with the strong ground motion were compared to analyze directional linkages. The Incremental Dynamic Analysis (IDA) method was used to analyze the fragility of the four bridges, and the seismic capacity of different bridges under the ground motion was compared. The omnidirectional displacement response spectra of the two stations and the omnidirectional representations of other indexes of intensity measure have obvious directionality, and the predominant direction is perpendicular to the direction of the fault, reflecting the rupture directionality of the earthquake. Comparing the corresponding period of the structural displacement response with seismic action with the omnidirectional displacement response spectra, it was found that the bridge structural displacement is correlated with the predominant cycle and predominant direction of the omnidirectional displacement response spectra. Under the same seismic action, the maximum moment of the deck arch bridge in the transverse direction is larger than that in the bridge direction; the larger the span of the deck arch bridge, the larger the maximum moment at the arch footing. According to the analysis of fragility, it can be seen that the seismic capacity of the continuous rigid frame bridge is much lower than that of the deck steel truss arch bridge under this ground motion. Compared to the deck-type concrete arch bridge, the deck steel truss arch bridge has greater seismic capacity. In this paper, based on the structural analysis and fragility analysis of these four bridges close to the fault, the structural damage and damage levels in the future earthquake are speculated, which provide suggestions for the follow-up maintenance and reinforcement work, and are also conducive to the resilience evaluation and rapid repair work after the real earthquake damage.

This paper analyzes the active vibration control of sandwich beams using Active Constrained Layer Damping (ACLD). The finite element model of the viscoelastic sandwich beam combines finite element method with the Golla Hughes McTavish (GHM) model, using a 2-node 8 degrees freedom element. The finite element model is validated by the first four natural frequencies of the model in the literature, and the governing equations of sandwich beams are generated based on the Hamiltonian principle. The physical space dynamic condensation technique and state space complex mode decoupling method are employed to reduce the order of the structural model. This is necessary because free degree of the finite element model is too high to directly control the structure’s vibration. It shows that the fundamental physical characteristics of the structure may remain largely unchanged while the physical and state spaces are jointly reduced. We investigated how the positions and coverages of ACLD patches impact on the active control, vibration damping of viscoelastic sandwich beams.