Pendulum tuned mass damper with eddy current damping (EC-PTMD) is a promising TMD device for vibration control of structures. Previous study focused primarily on the plate-like configuration of EC-PTMD, which motion of inertial mass is approximately horizontal. However, uneven distribution of damping force, non-constant damping and low energy efficiency will be resulted. This study developed a newly assembled pendulum tuned mass damper with constant eddy current damping (AEC-PTMD) in the form of arc. The proposed AEC-PTMD consists of a rigid suspension with sufficient lateral stiffness to keep inertial mass motion in a plane, the conductor plate fixed on the baseplate, and arc-shaped back iron acted as inertial mass placed on two sides of conductor plate. Meanwhile, the arc-shaped permanent magnets (PMs) are embedded into both sides of back iron to overcome the normal attraction and enhance greater magnetic density. Based on the Biot-Savart Law, the analytical expressions of magnetic flux distribution for arc-shaped PM are derived and assessed. Meanwhile, the effect of ferromagnetic media on magnetic flux distribution of arc-shaped PM is analyzed, which utilized a parameterization formula for the distance from the surface of the PM to a point outside. Further, the 3D finite element model (FEM) of an AEC-PTMD unit is established to evaluate the accuracy of the analytical results. A prototype of the proposed AEC-PTMD unit has been fabricated and laboratory experiments are conducted for the purpose of validating analytical and FEM results. All of these results have a good agreement.

Accidents involving inland waterway vessels have raised concerns regarding monitoring their navigation tracks. The economical and convenient deployment of video surveillance equipment and computer vision techniques offer an effective solution for tracking vessel trajectories in narrow inland waterways. However, field applications of video surveillance systems face challenges of small object detection and the limited field of view of cameras. This paper investigates the feasibility of using multiple monocular cameras to monitor long-distance inland vessel trajectories. The one-stage CNN model, YOLOv5, is enhanced for small object detection by incorporating generalized intersection over union loss and a multi-scale fusion attention mechanism. The Bytetrack algorithm is employed to track each detected vessel, ensuring clear distinction in multiple-vessel scenarios. An inverse projection formula is derived and applied to the tracking results from monocular camera videos to estimate vessel world coordinates under potential water level changes in long-term monitoring. Experimental results demonstrate the effectiveness of the improved detection and tracking methods, with consistent trajectory matching for the same vessel across multiple cameras. Utilizing the Savitzky-Golay filter mitigates jitter in the entire final trajectory after timing-alignment merging, leading to a better fit of the dispersed trajectory points.

This paper proposes a composite form of fuzzy modal control plan based on a piecewise Lyapunov criterion in ambient intelligence (AI). In some cases, these goals are of equal importance and cannot be easily prioritized. Environmental intelligence systems are being developed to handle multi-objective problems related to daily activities. This paper proposes a context-aware structure to provide strategies in an AI control system. Based on context data from sensors distributed throughout the environment, the modelled system recognizes the individual state, makes supporting decisions with no designation for control targets, and executes operations that is based on the environment feedbacks. To validate the developed model, an example using the system to deal with a practical engineering structural stability of analysis and control is described. The objectives of this paper are access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable planning and management of human settlement. Therefore, the goal is believed to be achieved in the near future through the continuous development of AI and control theory for a better life from the environment and built systems.

This study presents a comparative analysis of three nature-inspired algorithms-Black Hole Algorithm (BHA), Earthworm Optimization Algorithm (EWA), and Future Search Algorithm (FSA)-for predicting the compressive strength of masonry structures. Each algorithm was integrated with a Multilayer Perceptron (MLP) model, using a structural dimension, rebound number, ultrasonic pulse velocity, and failure load dataset. The dataset was divided into training (70%) and testing (30%) subsets to evaluate model performance. Root Mean Square Error (RMSE) and the coefficient of determination (R²) were employed as statistical indices to measure accuracy. The BHA-MLP model achieved the best performance, with an RMSE of 0.04731 and an R² of 0.9995 for the training dataset and an RMSE of 0.06537 and an R² of 0.99877 for the testing dataset, securing the highest overall score. FSA-MLP ranked second, demonstrating strong predictive performance, followed by EWA-MLP, which performed with lower accuracy but still showed valuable results. The study highlights the potential of using these nature-inspired optimization algorithms to enhance the predictive accuracy of compressive strength in masonry structures, offering insights for engineering and policymaking to improve structural safety and performance.

A cracked bridge with reduced stiffness is susceptible to vehicle-induced vibrations above the warning threshold. This study proposes a pounding tuned mass damper (PTMD) with an adjustable mass and double pounding boundaries covered with a viscoelastic material. The PTMD is intended to reduce bridge vibrations caused by vehicle loads. A vehicle-bridge-PTMD coupled equation of motion is established against the engineering background of a continuous steel-concrete composite girder bridge. The bridge performance degradation is evaluated in terms of crack density and stiffness reduction coefficient, which are determined through field crack investigations. The vehicle-induced vibrations of a cracked continuous steel-concrete bridge are then studied while changing the parameters of the designed PTMD. The PTMD effectively reduced the vehicle-induced vibrations of the bridge. The vibration reduction ratio reached 38.9% after applying three PTMDs with a total mass ratio of 2%. On a simply supported steel-concrete composite beam, three PTMDs with a total mass ratio of 2% reduced the vibration amplitudes by 31.4%.