Search Results (491)

The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation. Full article

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

Gear transmission error (GTE) is a critical factor influencing the performance and service life of gear systems, as it directly contributes to vibration, noise generation, and premature wear. The present study introduces a combined theoretical and experimental approach to characterizing GTE in a single-stage spur gear system. A six-degree-of-freedom nonlinear dynamic model was formulated to capture coupled lateral–torsional vibrations, accounting for gear mesh stiffness, bearing and coupling characteristics, and a harmonic transmission error component representing manufacturing and assembly imperfections. Simulations and experiments were conducted under healthy and eccentricity-faulted conditions, where a controlled 890 g eccentric mass induced misalignment. Frequency domain inspection of faulty gear data showed pronounced sidebands flanking the gear mesh frequency near 200 Hz, as well as harmonics extending from 500 Hz up to 1200 Hz, in contrast with the healthy case dominated by peaks confined to 50–100 Hz. STFT analysis revealed dispersed spectral energy and localized high-intensity regions, reinforcing its role as an effective fault diagnostic tool. Experimental findings aligned with theoretical predictions, demonstrating that the integrated modelling and time–frequency framework is effective for early fault detection and performance evaluation of spur gear systems. Full article

Bolted connections are extensively utilized in aerospace, civil, and mechanical systems for structural assembly. However, inevitable structural vibrations can induce bolt loosening, leading to preload reduction and potential structural failure. Early-stage preload degradation, particularly during initial loosening, is often undetectable by conventional monitoring methods due to limited sensitivity and poor noise resilience. To address these limitations, this study proposes an intelligent bolt preload monitoring framework that combines electromechanical impedance (EMI) signal analysis with a parallel deep learning architecture. A multiphysics-coupled model of flange joint connections is developed to reveal the nonlinear relationships between preload degradation and changes in EMI conductance spectra, specifically resonance peak shifts and amplitude attenuation. Based on this insight, a parallel convolutional neural network (P-CNN) is designed, employing dual branches with 1 × 3 and 1 × 7 convolutional kernels to extract local and global spectral features, respectively. The architecture integrates dilated convolution to expand frequency–domain receptive fields and an enhanced SENet-based channel attention mechanism to adaptively highlight informative frequency bands. Experimental validation on a flange-bolt platform demonstrates that the proposed P-CNN achieves 99.86% classification accuracy, outperforming traditional CNNs by 20.65%. Moreover, the model maintains over 95% accuracy with only 25% of the original training samples, confirming its robustness and data efficiency. The results demonstrate the feasibility and scalability of the proposed approach for real-time, small-sample, and noise-resilient structural health monitoring of bolted connections. Full article

In response to China’s dual-carbon strategy, this study proposes a comprehensive analytical framework to identify the evolutionary pathways of key policy tasks in developing a new-type power system. A dual-channel data acquisition process was designed to extract, standardize, and segment policy documents and online texts into a unified corpus. A multi-label BERT classification model was then developed, incorporating domain-specific terminology injection, label-wise attention, dynamic threshold scanning, and imbalance-aware weighting. The model was trained and validated on 200 energy news articles, 100 official policy releases, and 10 strategic planning documents. By the 10th epoch, it achieved convergence with a Macro-F1 of 0.831, Micro-F1 of 0.849, and Samples-F1 of 0.855. Ablation studies confirmed the significant performance gain over simplified configurations. Structural label analysis showed “Build system-friendly new energy power stations” was the most frequent label (107 in plans, 80 in news, 24 in policies) and had the highest co-occurrence (81 times) with “Optimize and strengthen the main grid framework.” The label co-occurrence network revealed multi-layered couplings across generation, transmission, and storage. The Priority Evaluation Index (PEI) further identified “Build shared energy storage power stations” as a structurally central task (centrality = 0.71) despite its lower frequency, highlighting its latent strategic importance. Within the domain of national-level public policy and planning documents, the proposed framework shows reliable and reusable performance. Generalization to sub-national and project-level corpora is left for future work, where we will extend the corpus and reassess robustness without altering the core methodology. Full article

In an intelligent driving system, the rationality of driving decisions and the trajectory planning scheme directly determines the safety and stability of the system. Existing research mostly relies on high-definition maps and empirical parameters to estimate road adhesion conditions, ignoring the direct impact of real-time road status changes on the dynamic feasible domain of vehicles. This paper proposes an intelligent driving decision-making and trajectory planning method that comprehensively considers the influence factors of vehicle–road interaction. Firstly, real-time estimation of road adhesion coefficients was achieved based on the recursive least squares method, and a dynamic adhesion perception mechanism was constructed to guide the decision-making module to restrict lateral maneuvering behavior under low-adhesion conditions. A multi-objective lane evaluation function was designed for adaptive lane decision-making. Secondly, a longitudinal and lateral coupled trajectory planning framework was constructed based on the traditional lattice method to achieve smooth switching between lateral trajectory planning and longitudinal speed planning. The planned path is tracked based on a model predictive control algorithm and dual PID algorithm. Finally, the proposed method was verified on a co-simulation platform. The results show that this method has good safety, adaptability, and control stability in complex environments and dynamic adhesion conditions. Full article

Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier development, this study proposes DySC-MDE, an end-to-end co-designed framework for seizure detection. A novel multi-domain entropy (MDE) representation is constructed at the feature level based on amplitude-sensitive permutation entropy (ASPE), which adopts entropy-based quantifiers to characterize the nonlinear dynamics of EEG signals across diverse domains. Specifically, ASPE is extended into three distinct variants, refined composite multiscale ASPE (RCMASPE), discrete wavelet transform-based hierarchical ASPE (HASPE-DWT), and time-shift multiscale ASPE (TSMASPE), to represent various temporal and spectral dynamics of EEG signals. At the classifier level, a dynamic synapse classifier (DySC) is proposed to align with the structure of the MDE features. Particularly, DySC includes three parallel and specialized processing pathways, each tailored to a specific entropy variant. These outputs are then adaptively fused through a dynamic synaptic gating mechanism, which can enhance the model’s ability to integrate heterogeneous information sources. To fully evaluate the effectiveness of the proposed method, extensive experiments are conducted on two public datasets using cross-validation. For the binary classification task, DySC-MDE achieves an accuracy of 97.50% and 98.93% and an F1-score of 97.58% and 98.87% in the Bonn and CHB-MIT datasets, respectively. Moreover, in the three-class task, the proposed method maintains a high F1-score of 96.83%, revealing its strong discriminative performance and generalization ability across different categories. Consequently, these impressive results demonstrate that the joint optimization of nonlinear dynamic feature representations and structure-aware classifiers can further improve the analysis of complex epileptic EEG signals, which opens a novel direction for robust seizure detection. Full article

In deep-sea mining operations, rigid risers operate in a complex and uncertain ocean environment where vessel–riser interactions present significant structural challenges. This study develops a coupled dynamic modeling framework that integrates vessel motions and environmental loads to evaluate the probabilistic risk of riser failure. Using frequency-domain RAOs derived from AQWA and time-domain simulations in OrcaFlex 11.0, we analyze the riser’s effective tension, bending moment, and von Mises stress under a range of wave heights, periods, and directions, as well as varying current and wind speeds. A Monte Carlo simulation framework based on Latin hypercube sampling is used to generate 10,000 sea state scenarios. The response distributions are approximated using probability density functions to assess structural reliability, and global sensitivity is evaluated using a Sobol-based approach. Results show that the wave height and period are the primary drivers of riser dynamic response, both with sensitivity indices exceeding 0.7. Transverse wave directions exert stronger dynamic excitation, and the current speed notably affects the bending moment (sensitivity index = 0.111). The proposed methodology unifies a coupled time-domain simulation, environmental uncertainty analysis, and reliability assessment, enabling clear identification of dominant factors and distribution patterns of extreme riser responses. Additionally, the workflow offers practical guidance on key monitoring targets, alarm thresholds, and safe operation to support design and real-time decision-making. Full article

A methane-air premixed gas explosion is one of the most destructive disasters in the process of coal mining, and the dynamic coupling between the shock wave triggered by the explosion and the surrounding rock of the roadway can lead to the destabilization of the surrounding rock structure, the destruction of equipment, and casualties. The aim of this study is to systematically reveal the propagation characteristics of the blast wave, the spatial and temporal evolution of the wall load, and the damage mechanism of the surrounding rock by establishing a two-way fluid-solid coupling numerical model. Based on the Ansys Fluent fluid solver and Transient Structure module, a framework for the co-simulation of the fluid and solid domains has been constructed by adopting the standard $k-\epsilon$ turbulence model, finite-rate/eddy-dissipation (FR/ED) reaction model, and nonlinear finite-element theory, and by introducing a dynamic damage threshold criterion based on the Drucker–Prager and Mohr–Coulomb criteria. It is shown that methane concentration significantly affects the kinetic behavior of explosive shock wave propagation. Under chemical equivalence ratio conditions (9.5% methane), an ideal Chapman–Jouguet blast wave structure was formed, exhibiting the highest energy release efficiency. In contrast, lean ignition (7%) and rich ignition (12%) conditions resulted in lower efficiencies due to incomplete combustion or complex combustion patterns. In addition, the pressure time-history evolution of the tunnel enclosure wall after ignition triggering exhibits significant nonlinear dynamics, which can be divided into three phases: the initiation and turbulence development phase, the quasi-steady propagation phase, and the expansion and dissipation phase. Further analysis reveals that the closed end produces significant stress aggregation due to the interference of multiple reflected waves, while the open end increases the stress fluctuation due to turbulence effects. The spatial and temporal evolution of the strain field also follows a three-stage dynamic pattern: an initial strain-induced stage, a strain accumulation propagation stage, and a residual strain stabilization stage and the displacement is characterized by an initial phase of concentration followed by gradual expansion. This study not only deepens the understanding of methane-air premixed gas explosion and its interaction with the roadway’s surrounding rock, but also provides an important scientific basis and technical support for coal mine safety production. Full article

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis. Full article

Visual attractiveness perception—an individual’s capacity to recognise and evaluate the visual appeal of urban scene safety—has direct implications for well-being, economic vitality, and social cohesion. However, most empirical studies rely on single-source metrics or algorithm-centric pipelines that under-represent human perception. Addressing this gap, we introduce a fully reproducible, multimodal framework that measures and models this domain-specific facet of human intelligence by coupling Generative Pre-trained Transformer 4o (GPT-4o) with 1000 Street View images. The pipeline first elicits pairwise aesthetic judgements from GPT-4o, converts them into a latent attractiveness scale via Thurstone’s law of comparative judgement, and then validates the scale against 1.17 M crowdsourced ratings from MIT’s Place Pulse 2.0 benchmark (Spearman ρ = 0.76, p < 0.001). Compared with a Siamese CNN baseline (ρ = 0.60), GPT-4o yields both higher criterion validity and an 88% reduction in inference time, underscoring its superior capacity to approximate human evaluative reasoning. In this study, we introduce a standardised and reproducible streetscape evaluation pipeline using GPT-4o. We then combine the resulting attractiveness scores with network-based accessibility modelling to generate a “aesthetic–accessibility map” of urban central districts in Chongqing, China. Cluster analysis reveals four statistically distinct street types—Iconic Core, Liveable Rings, Transit-Rich but Bland, and Peripheral Low-Appeal—providing actionable insights for landscape design, urban governance, and tourism planning. Full article

In ultra-wideband (UWB) radio fuze architectures, the receiver serves as the core component for receiving target-reflected signals, with its performance directly determining system detection accuracy. Manufacturing tolerances and operational environments induce inherent stochastic perturbations in circuit components, causing deviations of actual parameters from nominal values. This consequently degrades the signal-to-noise ratio (SNR) of receiver outputs and compromises ranging precision. To overcome these limitations and identify critical sensitive components in the receiver, this study proposes the following: (1) A dual-channel time-domain correlated UWB fuze detection model; and (2) the integration of an asymmetric tolerance mathematical model for dual-channel correlated receivers with a Morris-LHS-Sobol collaborative strategy to quantify independent effects and coupling interactions across multidimensional parameter spaces. Simulation results demonstrate that integrating capacitors and resistors constitute the dominant sensitivity sources, exhibiting significantly positive synergistic effects. Physical simulation correlation and hardware circuit verification confirms that the proposed model and sensitivity analysis method outperform conventional approaches in tolerance resolution and allocation optimization, thereby advancing the theoretical characterization of nonlinear coupling effects between parameters. Full article

The development of reliable visual inference models is often constrained by the burdensome and time-consuming processes involved in collecting and annotating high-quality datasets. This challenge becomes more acute in domains where key phenomena are time-dependent or event-driven, narrowing the opportunity window to capture representative observations. Yet, accelerating the deployment of deep learning (DL) models is crucial to support timely, data-driven decision-making in operational settings. To tackle such an issue, this paper explores the use of 2D synthetic data grounded in real-world patterns to train initial DL models in contexts where annotated datasets are scarce or can only be acquired within restrictive time windows. Two complementary approaches to synthetic data generation are investigated: rule-based digital image processing and advanced text-to-image generative diffusion models. These methods can operate independently or be combined to enhance flexibility and coverage. A proof-of-concept is presented through a couple case studies in precision viticulture, a domain often constrained by seasonal dependencies and environmental variability. Specifically, the detection of Lobesia botrana in sticky traps and the classification of grapevine foliar symptoms associated with black rot, ESCA, and leaf blight are addressed. The results suggest that the proposed approach potentially accelerates the deployment of preliminary DL models by comprehensively automating the production of context-aware datasets roughly inspired by specific challenge-driven operational settings, thereby mitigating the need for time-consuming and labor-intensive processes, from image acquisition to annotation. Although models trained on such synthetic datasets require further refinement—for example, through active learning—the approach offers a scalable and functional solution that reduces human involvement, even in scenarios of data scarcity, and supports the effective transition of laboratory-developed AI to real-world deployment environments. Full article

A time-domain semi-empirical simulation model based on the wake oscillator approach is developed to investigate the coupled in-line (IL) and cross-flow (CF) vortex-induced vibration (VIV) of a flexible riser in uniform oscillatory flow. A novel nondimensionalization method is introduced by utilizing the dimensionless parameter $StKC$, which effectively replicates the fundamental lift frequency caused by the complex vortex motion around the riser. The structural responses of the riser are described using the Euler–Bernoulli beam theory, and the van der Pol equations are used to calculate the fluid forces acting on the riser, which can replicate the nonlinear vortex dynamics. The coupled equations are discretized in both time and space with a finite difference method (FDM), enabling iterative computations of the VIV responses of the riser. A total of six cases are examined with four different Keulegan–Carpenter ($KC$) numbers (i.e., $KC=31$, 56, 121, and 178) to investigate the VIV characteristics of small-scale and large-scale risers in uniform oscillatory flow. Key features such as intermittent VIV, amplitude modulation, and hysteresis, as well as the VIV development process, are analyzed in detail. The simulation results show good agreement with the experimental data, indicating that the proposed numerical model is able to reliably reproduce the riser VIV in uniform oscillatory flow. Overall, the VIV characteristics of the large-scale riser resemble those of the small-scale riser but exhibit higher vibration modes, stronger traveling wave features, and more complex energy transfer mechanisms. Full article

Permittivity measurements of concrete materials benefit from the application of high-frequency electromagnetic waves (HF-EMWs), but they still face the problem of being aleatory and exhibit epistemic uncertainty, originating from multi-phase heterogeneous materials and the limited knowledge of HF-EMW propagation. This limitation restricts the precision of non-destructive testing. This study proposes an evidential regression deep network for conducting permittivity measurements with uncertainty quantification. This method first proposes a finite-difference time-domain (FDTD) model with multi-phase heterogeneous concrete materials to simulate HF-EMW propagation in a concrete sample or structure, obtaining the HF-EMW echo that contains aleatory uncertainties owing to the limited knowledge of wave propagation. A U-net-based model is then proposed to denoise an HF-EMW, where the difference between a couple of observed and denoised HF-EMWs characterizes aleatory uncertainty owing to measurement noise. Finally, a Dempster–Shafer theory-based (DST-based) evidential regression network is proposed to compute permittivity, incorporating the quantification of two types of uncertainty using a Gaussian random fuzzy number (GRFN): a type of fuzzy set that has the characteristics of a Gaussian fuzzy number and a Gaussian random variable. An experiment with 1500 samples indicates that the proposed method measures permittivity with a mean square error of 7.50% and a permittivity uncertainty value of 74.70% in four types of concrete materials. Additionally, the proposed method can quantify the uncertainty in permittivity measurements using a GRFN-based belief measurement interval. Full article