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Cross-domain cooperative task allocation is a complex and challenging issue in the field of multi-agent task allocation that requires urgent attention. This paper proposes a task allocation method based on the multi-strategy improved dung beetle optimization (MSIDBO) algorithm, aiming to solve the problem of fully distributed multi-agent cross-domain cooperative task allocation. This method integrates two key objective functions: target allocation and control allocation. We propose a target allocation model based on the optimal comprehensive efficiency, cluster load balancing, and economic benefit maximization, and a control allocation model leveraging the radar detection ability and control data link connectivity. To address the limitations of the original dung beetle optimization algorithm in solving such problems, four revolutionary strategies are introduced to improve its performance. The simulation results demonstrate that our proposed task allocation algorithm significantly improves the cross-domain collaboration efficiency and meets the real-time requirements for multi-agent task allocation on various scales. Specifically, our optimization performance was, on average, 32.5% higher compared to classical algorithms like the particle swarm optimization algorithm and the dung beetle optimization algorithm and its improved forms. Overall, our proposed scheme enhances system effectiveness and robustness while providing an innovative and practical solution for complex task allocation problems.

During the construction of deep foundation pits in subways, it is crucial to closely monitor the horizontal displacement of the pit enclosure to ensure stability and safety, and to reduce the risk of structural damage caused by pit deformations. With advancements in machine-learning (ML) techniques and correlation analysis in engineering, data-driven methods that combine ML with engineering monitoring data have become increasingly popular. These methods offer benefits such as high prediction accuracy, efficiency, and cost effectiveness. The main goal of this study was to develop a machine-learning method for predicting the enclosure deformation of deep foundation pits. This was achieved by analyzing the factors influencing deep foundation-pit enclosure deformation and incorporating historical cases and monitoring reports. The performance of each machine-learning prediction model was systematically analyzed and evaluated using K-Fold cross validation. The results revealed that the random forest model outperformed the other models. The result of the test data showed that the random forest model achieved an R2 of 0.9905, an MAE of 0.8572 mm, and an RMSE of 1.9119 mm. Feature importance analysis identified the depth of enclosure structure, water level, surface settlement, axial force, and exposure time as the most critical factors for accurate prediction. The depth of the enclosure structure had an especially significant impact on the prediction of enclosure deformation.

Damage types are important for structural condition assessment, however, for conventionally guided wave-based inspections, the characteristics extracted from the guided wave packets are usually used to detect, locate and quantify the damages, but not classify them. In this work, the data-driven method is proposed to classify the common damages in the pipe utilizing the guided wave signals obtained from numerous damage detection tests. The fundamental torsional mode T(0,1) is selected to conduct the guided wave-based damage detection to reduce the complexity of signal processing for its almost non-dispersive property. A total of 520 groups of experimental data under different degrees of damage were obtained to verify the proposed method. Finally, with help of a deep neural network (DNN) algorithm, all response data from the damages in the pipes were all clearly classified with quite high probability.

In the detection of small size mass loading, such as a single cell, a micro droplet or an aerosol particle, the sensors with longitudinally coupled surface acoustic wave resonator (LC-SAWR) structure can hardly avoid waveform distortions. The relative size of mass loading to the sensitive surface of the detector is the main factor affecting the response of LC-SAWR. The smaller the relative size, the worse the waveform distortion. In order to avoid influences from the mass loading’s size, in this paper, a transversely coupled SAW resonator (TC-SAWR) was proposed in order to achieve high performance in sensing small size mass loadings. For the design and simulation of TC-SAWR, the two-dimensional coupling of model (2D-COM) theory and finite element method (FEM) were used in this work. In the experiment, SiO2 was deposited on the sensor’s surface as a small size mass loading. The results from simulation and experiment mutually demonstrated the advantage of TC-SAWR to conquer waveform distortion in the detection of small size mass loading.

Engineers, architects, planners and designers must carefully consider the effects of wind in their work. Due to their slender and flexible nature, long-span bridges can often experience vibrations due to the wind, and so the careful analysis of wind effects is paramount. Traditionally, wind tunnel tests have been the preferred method of conducting bridge wind analysis. In recent times, owing to improved computational power, computational fluid dynamics simulations are coming to the fore as viable means of analysing wind effects on bridges. The focus of this paper is on long-span cable-supported bridges. Wind issues in long-span cable-supported bridges can include flutter, vortex-induced vibrations and rain–wind-induced vibrations. This paper presents a state-of-the-art review of research on the use of wind tunnel tests and computational fluid dynamics modelling of these wind issues on long-span bridges.

In this work, a new buffer layer material, a bismuth-indium (Bi-In) alloy, was utilized to improve the quality of large-area, laser-induced periodic ripple structures on silicon. Better-defined ripple structures and larger modification areas were obtained at different scanning speeds by pre-depositing a Bi-In film. The single-spot investigations indicated that ripple structures were much easier to form on silicon coated with the Bi-In film under laser fluences of 2.04 and 2.55 J/cm2 at a fixed pulse number of 200 in comparison with on bare silicon. A physical model in terms of the excellent thermal conductivity contributed by the free electrons in the Bi-In film homogenizing the thermal distribution caused by the laser irradiation in the early stage of the formation of laser-induced periodic surface structures was proposed to explain the above phenomena. The results show that the Bi-In film enabled a wider range of laser fluences to generate periodic structures and helped to form regular ripple structures on the silicon. In addition, the modulation effects of the laser fluence and pulse number on surface structures were studied experimentally and are discussed in detail.

Unmanned combat air vehicle (UCAV) path planning aims to calculate the optimal or suboptimal flight path considering the different threats and constraints under the complex battlefield environment. This flight path can help the UCAV avoid enemy threats and improve the efficiency of the investigation. This paper presents a new quantum wind driven optimization (QWDO) for the path planning of UCAV. QWDO algorithm uses quantum rotation gate strategy on population evolution and the quantum non-gate strategy to realize the individual variation of population. These operations improve the diversity of population and avoid premature convergence. This paper tests this optimization in two instances. The experimental results show that the proposed algorithm is feasible in these two cases. Compared to quantum bat algorithm (QBA), quantum particle swarm optimization (QPSO), wind driven optimization (WDO), bat algorithm (BA), particle swarm optimization (PSO), and differential evolution (DE), the QWDO algorithm exhibited better performance. The simulation results demonstrate that the QWDO algorithm is an effective and feasible method for solving UCAV path planning.