Ultrasonics最新文献_第3页

Design, fabrication, and characterization of a novel cantilever-based PMUT incorporating a central spring-like folded beam with enhanced transmission performance for air applications 一种新型悬臂式PMUT的设计、制造和表征，该PMUT采用中央弹簧状折叠梁，具有增强的空气传输性能。

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-30 DOI: 10.1016/j.ultras.2026.107978

Yanyuan Ba , Yiming Li , Yicheng Wang

Piezoelectric micromachined ultrasonic transducer (PMUT), owing to the miniaturization, low power consumption, and ease of driving, have become a viable alternative to traditional piezoelectric ceramic transducers in air-coupled ultrasonic ranging applications. However, PMUT suffer from significantly degraded transmission performance due to residual stresses inherent in microelectromechanical systems (MEMS) fabrication processes, which substantially limits their detection range and accuracy in airborne applications. To address this issue, this work presents a novel cantilever-based PMUT design, which incorporates micro-slits along the diagonal of the diaphragm to form four triangular cantilever beams. Additionally, inspired by springs, a flexible spring-folded beam structure is designed at the tail end of the cantilever beams to achieve cooperative vibration of the cantilevers through low stiffness mechanical coupling. This design significantly reduces the diaphragm stiffness, fully releases the residual stress, and enhances the mechanical response of the PMUT. Experimental results confirm that, under a low drive voltage of 1 V_PP (−5 V offset), the novel PMUT achieves a high resonant displacement of 16,752 nm at its resonant frequency of 73.67 kHz, representing an increase of 10,951 nm compared to the 4,823 nm displacement of the conventional PMUT. At a 10 cm air distance, the device generates a high sound pressure of 4.8 Pa, equivalent to 107.6 dB (Ref. 2 × 10⁻⁵ Pa), which is approximately 6.86 dB higher than conventional PMUT. The new PMUT exhibits a receiving sensitivity of 0.85 mV/Pa, which is an improvement of 0.63 mV/Pa over conventional PMUT. This design significantly enhances the transmission performance of PMUT, showing great potential in high-precision air ranging applications.

压电微机械超声换能器（PMUT）由于其小型化、低功耗和易于驾驶等优点，已成为传统压电陶瓷换能器在空气耦合超声测距应用中的可行替代方案。然而，由于微机电系统（MEMS）制造过程中固有的残余应力，PMUT的传输性能明显下降，这极大地限制了它们在机载应用中的探测范围和精度。为了解决这个问题，这项工作提出了一种新颖的基于悬臂梁的PMUT设计，它结合了沿隔膜对角线的微缝，形成四个三角形悬臂梁。此外，受弹簧的启发，在悬臂梁的尾端设计了柔性弹簧折叠梁结构，通过低刚度机械耦合实现悬臂梁的协同振动。这种设计显著降低了膜片刚度，充分释放了残余应力，提高了PMUT的机械响应。实验结果证实，在1 VPP的低驱动电压（-5 V偏移）下，新型PMUT在73.67 kHz的谐振频率下实现了16752 nm的高谐振位移，比传统PMUT的4823 nm的位移增加了10951 nm。在10 cm的空气距离上，该器件产生4.8 Pa的高声压，相当于107.6 dB (Ref. 2 × 10-5 Pa)，比传统的PMUT高约6.86 dB。新型PMUT的接收灵敏度为0.85 mV/Pa，比传统PMUT提高了0.63 mV/Pa。该设计大大提高了PMUT的传输性能，在高精度空中测距应用中显示出巨大的潜力。

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引用次数: 0

Predicting transcranial ultrasound insertion loss using skull CT: A deep learning approach 利用颅骨CT预测经颅超声插入损失：一种深度学习方法。

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-29 DOI: 10.1016/j.ultras.2026.107976

Ning Wang , Han Li , Jinpeng Liao , Tyler Halliwell , Zhihong Huang

Transcranial ultrasound (tUS) is a non-invasive neuromodulation technique with applications in brain disorders. However, ultrasound attenuation induced by the skull significantly affects focal energy transmission. The loss of ultrasound intensity can be quantified by insertion loss (IL). Accurate IL prediction is crucial for optimizing ultrasound delivery. Conventional grid-based numerical methods for IL prediction are computationally expensive and highly sensitive to parameter variations. To address these challenges, we hypothesized that skull structural features are inherently correlated with IL and can be effectively captured through deep learning method. In this study, we conducted transmission experiments on 20 human skull specimens to measure IL at three frequencies of 220 kHz, 650 kHz, and 1000 kHz. We proposed a modified dual-path Inception-based neural network (mDPI-Net) for IL prediction based on skull computed tomography (CT) scan. Comparison results showed that mDPI-Net outperformed homogeneous pseudo-spectral methods (Peak Pressure Error: 26.6% vs. 34.3%, IL Deviation: 2.47 dB vs. 4.64 dB), and is comparable to the inhomogeneous simulations (Peak Pressure Error: 26.6% vs. 21.0%, IL Deviation: 2.47 dB vs. 1.69 dB), while achieving higher computational efficiency, increasing from 15 min/sample to 0.5 s/sample. The proposed approach demonstrated that the skull CT scan could inherently encode structural information relevant to IL. Under a well-fixed experimental setup, deep learning has the potential to enable real-time or rapid pre-operative IL predictions, and achieve more precise dose control in tUS applications such as neuromodulation, transcranial drug delivery, and non-invasive brain stimulation.

经颅超声（tUS）是一种非侵入性的神经调节技术，在脑部疾病中具有广泛的应用。然而，颅骨引起的超声衰减明显影响焦点能量的传输。超声强度的损失可以通过插入损失（IL）来量化。准确的IL预测是优化超声输送的关键。传统的基于网格的IL预测数值方法计算成本高，对参数变化高度敏感。为了解决这些挑战，我们假设颅骨结构特征与IL具有内在相关性，并且可以通过深度学习方法有效地捕获。在本研究中，我们对20个人类头骨标本进行了透射实验，测量了220 kHz、650 kHz和1000 kHz三个频率下的IL。我们提出了一种改进的基于双路径起始的神经网络（mDPI-Net），用于基于颅骨计算机断层扫描（CT）的IL预测。对比结果表明，mDPI-Net优于均匀伪谱方法（峰值压力误差：26.6% vs. 34.3%， IL偏差：2.47 dB vs. 4.64 dB），与非均匀模拟方法（峰值压力误差：26.6% vs. 21.0%， IL偏差：2.47 dB vs. 1.69 dB）相当，同时实现更高的计算效率，从15分钟/样本增加到0.5秒/样本。所提出的方法表明，颅骨CT扫描可以固有地编码与IL相关的结构信息。在固定良好的实验设置下，深度学习有可能实现实时或快速的术前IL预测，并在tUS应用中实现更精确的剂量控制，如神经调节、经颅给药和非侵入性脑刺激。

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引用次数: 0

An analytical two-step method for precise evaluation of local resonance frequencies for internal planar defects 平面内缺陷局部共振频率精确计算的两步解析法。

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-27 DOI: 10.1016/j.ultras.2026.107980

Honglin Yan , Shuang Xu , Jiarui Deng , Qingping Kang , Paixin Chen , Ruiqi Guan , Hua Zhang , Kai Wang

Despite the effectiveness of methods based on local defect resonance (LDR) for the nondestructive evaluation of planar defects, the physical mechanism underlying the generation of LDR remains an ongoing topic of research interest. Existing methods for interpreting the generation of LDR are based on the vibration theory and simplified boundary conditions, but they demonstrate effectiveness for LDR frequency prediction only in defects within specific parameter ranges and lack universal applicability for both near surface and internal defects. A two-step approach is proposed in this investigation to understand the generation of LDR from the perspective of wave reflection and standing wave formation. In this approach, the interaction of guided waves with defect boundaries are analyzed using the normal mode expansion method, and thereby the phase shift of reflected wave modes is obtained. On this basis, the formation of standing waves is analyzed, and a quantitative relation between the defect parameters and LDR frequency can be obtained explicitly. The shape effect on the LDR frequency is then investigated via a Rayleigh method. The proposed approach provides an insight into the generation of LDR for both near surface and internal defects, and enables the quantitative evaluation of defects with circular and elliptical shapes using the LDR frequencies.

尽管基于局部缺陷共振（LDR）的方法对平面缺陷的无损评估是有效的，但LDR产生的物理机制仍然是一个研究热点。现有的解释LDR产生的方法是基于振动理论和简化的边界条件，但它们仅对特定参数范围内缺陷的LDR频率预测有效，对近表面和内部缺陷缺乏普遍适用性。本文提出了从波反射和驻波形成两个角度来理解LDR产生的两步方法。该方法采用法模展开法分析导波与缺陷边界的相互作用，从而得到反射波模的相移。在此基础上，分析了驻波的形成，明确了缺陷参数与LDR频率之间的定量关系。然后通过瑞利方法研究了形状对LDR频率的影响。该方法为近表面和内部缺陷的LDR生成提供了深入的见解，并能够使用LDR频率对圆形和椭圆形状的缺陷进行定量评估。

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引用次数: 0

LSWNet: A physics-informed neural network for ultrasonic wavefield prediction and elastic constant inversion in unidirectional CFRP LSWNet：用于单向碳纤维复合材料超声波场预测和弹性常数反演的物理信息神经网络。

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-25 DOI: 10.1016/j.ultras.2026.107977

Hongjuan Yang , Jitong Ma , Zhengyan Yang , Tong Tian , Deshuang Deng , Dongyue Gao , Shuyi Ma , Lei Yang , Zhanjun Wu

Accurate determination of elastic constants is crucial for reliable ultrasonic defect detection in carbon fiber reinforced plastic (CFRP). However, non-destructive in-situ characterization of these constants, particularly via full-waveform inversion techniques, is hindered by the computational cost of wavefield simulations. Based on physics-informed neural networks (PINNs), a novel longitudinal and shear wavefield net (LSWNet) method is proposed for the forward wavefield prediction and inversion of ultrasonic waves in a unidirectional CFRP. The longitudinal and shear wave component fields at two moments, ultrasonic measurement data, and the 2D elastic wave equations of isotropic and anisotropic planes for unidirectional CFRP are embedded as physical constraint conditions to predict wavefields and elastic constants. For the inversion of elastic constants, ultrasonic data recorded by a linear phased array on the CFRP surface serve as input, while the LSWNet outputs C₆₆, C₁₃ and C₄₄. To accelerate convergence in large-scale models, weights and biases learned from training on small-scale structures are transferred. The proposed method has been verified through both finite element simulation and experiments. The mean squared errors between the predicted wavefields by PINNs and those obtained from finite element simulation do not exceed 3.2 × 10^-3, and the obtained elastic constants are close to the actual values. Furthermore, the elastic constants obtained via LSWNet are successfully applied to total focusing method, thereby enabling high-resolution detection of delamination damage. Consequently, the proposed method is capable of resolving forward and inverse issues associated with unidirectional CFRP ultrasonic wavefields, as well as in-situ characterization of elastic constants and damage imaging.

准确确定碳纤维增强塑料（CFRP）的弹性常数是可靠的超声缺陷检测的关键。然而，这些常数的无损原位表征，特别是通过全波形反演技术，受到波场模拟计算成本的阻碍。基于物理信息神经网络（PINNs），提出了一种新的纵向和剪切波场网络（LSWNet）方法，用于单向碳纤维复合材料中超声波的正向波场预测和反演。将两个时刻的纵波分量场、横波分量场、超声测量数据、各向同性和各向异性平面的二维弹性波方程作为物理约束条件，对单向CFRP的波场和弹性常数进行了预测。对于弹性常数的反演，采用CFRP表面线性相控阵记录的超声数据作为输入，LSWNet输出C66、C13和C44。为了加速大尺度模型的收敛，从小尺度结构训练中学习到的权重和偏差被转移。通过有限元仿真和实验验证了该方法的有效性。PINNs预测波场与有限元模拟结果的均方误差不超过3.2 × 10-3，得到的弹性常数与实际值接近。此外，将LSWNet获得的弹性常数成功应用于全聚焦方法，实现了分层损伤的高分辨率检测。因此，所提出的方法能够解决与单向CFRP超声波场相关的正演和逆问题，以及弹性常数的原位表征和损伤成像。

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引用次数: 0

Directional beam multiplexing using cylindrical holographic acoustic metasurfaces integrated with surface wave reflectors 结合面波反射器的圆柱全息声学超表面定向波束复用

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-25 DOI: 10.1016/j.ultras.2026.107974

Md Tausif Akram , Pinaki Mazumder , Kyungjun Song

Recent advancements in acoustic metasurfaces have significantly improved beamforming and steering capabilities, with beam multiplexing emerging as a key enabler of multidirectional sound projection. This paper proposes a cylindrical holographic acoustic metasurface integrated with surface wave reflectors (SWRs) to realize efficient acoustic beam multiplexing. By transitioning from conventional planar designs to a cylindrical geometry, the proposed metasurface supports the simultaneous generation of multiple highly directional beams at distinct combinations of elevation and azimuthal angles. The integration with SWRs enhances beam collimation and suppresses side lobes, thereby ensuring high directivity and acoustic field confinement. Both simulations and experimental validations confirmed that the metasurface could steer multiple beams generated by a single monopole source in specific directions in 3D space; this capability can help ensure reliable performance across various applications such as sonar, medical imaging, and acoustic communication. The proposed approach represents a versatile and scalable conformal platform for spatially multiplexed acoustic beam steering, marking a significant advancement in the development of multifunctional acoustic metasurfaces.

声学超表面的最新进展显著改善了波束形成和转向能力，波束多路复用成为多向声音投射的关键因素。提出了一种结合表面波反射器的圆柱全息声学超表面，以实现声波束的高效复用。通过从传统的平面设计过渡到圆柱形几何结构，提议的超表面支持同时产生多个高度定向的光束，以不同的仰角和方位角组合。与swr的集成增强了光束准直，抑制了侧瓣，从而确保了高指向性和声场约束。仿真和实验验证均证实，该超表面可以将单个单极子源产生的多束光束引导到三维空间的特定方向；这种能力有助于确保在声纳、医学成像和声学通信等各种应用中具有可靠的性能。所提出的方法代表了一个通用的、可扩展的保形平台，用于空间复用声波束转向，标志着多功能声学元表面发展的重大进步。

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引用次数: 0

Dirac cones and topological torsional modes in phononic nanowires using Su–Schrieffer–Heeger Model 利用Su-Schrieffer-Heeger模型研究声子纳米线中的狄拉克锥和拓扑扭转模

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-24 DOI: 10.1016/j.ultras.2026.107975

Mohammed Elaouni , Noura Ezzahni , Soufyane Khattou , Madiha Amrani , Afaf Bouzidi , Abdellatif Gueddida , El Houssaine El Boudouti , Bahram Djafari-Rouhani

Topological materials have attracted significant attention due to their distinct edge states, known for their robustness to local perturbations. In the field of phononic crystals, these states manifest as topological surface or interface modes, offering promising applications in waveguiding and energy harvesting. This study explores the emergence and control of azimuthal symmetric torsional interface states in phononic nanowires (PNWs) composed of alternating cylindrical layers. In the framework of the Su–Schrieffer–Heeger (SSH) model, we use the Green’s function approach to derive analytical expressions of the dispersion relations to predict all Dirac-point positions and interface modes. The analytical results are confirmed by finite element method simulations performed using COMSOL Multiphysics. In PNWs with symmetric unit cells, band-structures and scattering calculations reveal tunable interface modes whose frequencies and propagation characteristics can be adjusted via geometrical parameters. We also demonstrate through Zak-phase, local density of states (LDOS), and transmission-spectrum analyses that these interface states remain fixed and topologically protected under variations of the dimerization parameter. These findings pave the way for exploiting topological interface states in PNWs, thus opening to innovative phononic devices and contributing to the advancement of the field of topological physics.

拓扑材料由于其独特的边缘状态和对局部扰动的鲁棒性而引起了人们的极大关注。在声子晶体领域，这些状态表现为拓扑表面或界面模式，在波导和能量收集方面提供了有前途的应用。本研究探讨了由交替圆柱层组成的声子纳米线（PNWs）中方位对称扭转界面态的产生和控制。在Su-Schrieffer-Heeger （SSH）模型的框架中，我们使用Green函数方法推导色散关系的解析表达式，以预测所有的dirac点位置和界面模式。利用COMSOL Multiphysics进行的有限元模拟验证了分析结果。在具有对称晶胞的PNWs中，波段结构和散射计算揭示了可调谐的界面模式，其频率和传播特性可以通过几何参数来调节。我们还通过zak相位，局域态密度（LDOS）和透射谱分析证明，在二聚化参数的变化下，这些界面状态保持固定和拓扑保护。这些发现为开发PNWs中的拓扑界面态铺平了道路，从而为创新声子器件打开了大门，并为拓扑物理领域的发展做出了贡献。

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引用次数: 0

LambNet-T: A lightweight path-conditional transformer autoencoder for temperature-aware baseline learning in Lamb-wave SHM lamnet - t：用于lamwave SHM中温度感知基线学习的轻量级路径条件变压器自编码器。

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-23 DOI: 10.1016/j.ultras.2026.107973

Jan Horňas, Ondřej Vích, Lenka Šedková, Ivan Mlch, Bohuslav Cabrnoch, Michal Král

Reliable Lamb-wave–based Structural Health Monitoring (SHM) depends on accurate baseline selection under varying temperatures. This study presents LambNet-T, a lightweight path-conditional Transformer-based autoencoder for temperature-aware baseline learning across multiple transducer paths. LambNet-T employs Attention Pooling (AP) to generate contextual embeddings and enables robust baseline selection using Cosine Similarity (CS) with a Median-based evaluation strategy, improving diagnostic accuracy and temperature robustness in multi-path Lamb-wave SHM. Experiments on a composite panel over −10 to +50 °C used only four baseline temperatures to reflect practical constraints, with quadratic interpolation for data augmentation. LambNet-T demonstrated significantly higher training efficiency than a convolutional autoencoder (CAE-GAP). During inference, the Median of the highest path-specific CS values identified the optimal temperature-compensated baseline. The method achieved high precision (R² = 0.994 ± 0.001), outperforming both CAE-GAP and conventional Optimal Baseline Selection (OBS). Integration with an existing damage localization framework reduced impact location errors to as low as 4.12 mm. A conservative statistical filter, based on baseline selection variability, was applied to manage uncertainty. All experimental datasets are openly available for reproducibility.

可靠的基于兰姆波的结构健康监测（SHM）依赖于在不同温度下准确的基线选择。本研究提出了LambNet-T，一种轻量级的基于路径条件变压器的自编码器，用于跨多个传感器路径的温度感知基线学习。LambNet-T采用注意力池（AP）生成上下文嵌入，并使用余弦相似度（CS）和基于中值的评估策略实现稳健的基线选择，提高了多路径Lamb-wave SHM的诊断准确性和温度鲁棒性。在-10至+50°C的复合面板上进行的实验仅使用四个基线温度来反映实际约束，并使用二次插值来增强数据。LambNet-T的训练效率明显高于卷积自编码器（CAE-GAP）。在推理过程中，最高路径特异性CS值的中位数确定了最佳温度补偿基线。该方法具有较高的精密度（R2 = 0.994±0.001），优于CAE-GAP和传统的最优基线选择（OBS）。与现有的损伤定位框架集成，将冲击定位误差降低到4.12 mm。采用基于基线选择可变性的保守统计过滤器来管理不确定性。所有的实验数据集都是公开的。

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引用次数: 0

Transcranial steering of focused ultrasound vortex with binary acoustic metasurfaces 双声超表面聚焦超声涡的经颅转向

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-23 DOI: 10.1016/j.ultras.2026.107967

Zhongtao Hu , Xudong Chen

Ultrasound vortices have rapidly expanded their applications to areas like particle trapping, contactless manipulation, acoustic communications. In ultrasonic imaging and therapy involving bone tissues, these vortex beams offer intriguing possibilities but transmitting them through bone (especially the skull) poses challenges. Traditional acoustic lenses were engineered to rectify skull-induced beam aberration, and their capacity was limited to generating only static ultrasound fields within the brain. To overcome this constraint, our study presents a novel method for transcranially steering focused ultrasound vortex using 3D printed binary acoustic metasurfaces (BAMs) with a thickness of 0.8 λ. We tackled the challenge of skull-induced phase aberration by computing the phase distribution via a time reversal technique, which concurrently enabled the generation of a steerable focused vortex inside an ex vivo human skull by adjusting the operating frequency. Both numerical and simulations experiments were conducted to validate the capabilities of BAMs. We further conducted numerical demonstrations of higher-order vortices (

l = 2 - 4

) inside the skull using the BAM, confirming that the approach is extensible beyond the fundamental case. This development paves the way for designing cost-effective particle-trapping systems, facilitating clot manipulation, and applying acoustic-radiation forces and torques within or across bone structures, thus presenting a new frontier for potential biomedical applications.

超声涡旋的应用已迅速扩展到粒子捕获、非接触式操作、声学通信等领域。在涉及骨组织的超声成像和治疗中，这些涡旋光束提供了有趣的可能性，但通过骨骼（特别是头骨）传输它们带来了挑战。传统的声学透镜被设计用来纠正头骨引起的光束畸变，而且它们的能力仅限于在大脑内产生静态超声场。为了克服这一限制，我们的研究提出了一种使用厚度为0.8 λ的3D打印二元声学超表面（BAMs）的新方法。我们通过时间反转技术计算相位分布，同时通过调整工作频率在离体人类头骨内产生可操纵的聚焦涡流，解决了头骨引起的相位畸变问题。通过数值和模拟实验验证了BAMs的性能。我们进一步使用BAM对颅骨内的高阶涡（l=2-4）进行了数值演示，证实了该方法可扩展到基本情况之外。这一发展为设计具有成本效益的粒子捕获系统，促进凝块操作，以及在骨结构内或骨结构间应用声辐射力和扭矩铺平了道路，从而为潜在的生物医学应用提供了新的前沿。

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引用次数: 0

Adaptive sampling for efficient Lamb wavefield reconstruction in composite laminates with Spatial-Temporal Masked AutoEncoder 基于时空掩膜自编码器的复合材料层合板Lamb波场自适应重构。

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-21 DOI: 10.1016/j.ultras.2026.107972

Dingcheng Ji , Wenhao Li , Fei Gao , Jiadong Hua , Jing Lin

The increasing demand for high-accuracy damage quantification in carbon fiber reinforced plastics (CFRP) has led to the widespread adoption of ultrasonic Lamb wave testing (ULWT) for non-destructive testing (NDT) in various engineering applications. The non-contact Scanning Laser Doppler Vibrometer (SLDV) has emerged as a valuable tool for damage evaluation. However, despite significant research on Lamb wavefield analysis methods, the rapid and reliable acquisition of full wavefield data remains a substantial challenge, limiting SLDV’s applicability in real-world engineering scenarios. This study presents a novel deep learning-based approach to reconstructing full wavefield data from highly under-sampled wavefield data using the Spatial-Temporal Masked AutoEncoder (STMAE). By leveraging time-series high-sparsity Lamb wavefield data, our method achieves remarkable reconstruction performance with a sampling ratio as low as 5%. Furthermore, we propose a novel scanning path optimization method based on Bayesian Optimization, which generates adaptive sparse spatial sampling patterns for wavefield reconstruction. The integration of this adaptive sampling pattern with the STMAE, termed as AdaSTMAE, yields lower precision wavefield prediction error around the damage areas. A comprehensive parametric study on the sampling ratio was conducted and validated through comparative experiments in both single-damage, multi-damage scenarios. The implementation of the adaptive sampling strategy resulted in a 2–16% reduction in reconstruction error for single-damage scenarios and a 0.7–5% reduction for multi-damage scenarios around damage areas, compared to scenarios without the adaptive strategy. Our experimental results demonstrate the outstanding performance of the proposed technique in wavefield reconstruction, achieving an 87–88% reduction in reconstruction error relative to the original masked autoencoder (MAE) across different sampling ratios (5%-25%). Additionally, cross-structural validation using composite blades with variable thickness confirmed the model’s strong generalization capability, effectively reconstructing wavefront distortion and velocity variation without fine-tuning.

碳纤维增强塑料（CFRP）对高精度损伤量化的需求日益增长，导致超声波兰姆波检测（ULWT）在各种工程应用中广泛用于无损检测（NDT）。非接触式扫描激光多普勒测振仪（SLDV）已成为一种有价值的损伤评估工具。然而，尽管对Lamb波场分析方法进行了大量研究，但快速可靠地获取全波场数据仍然是一个重大挑战，这限制了SLDV在实际工程场景中的适用性。本研究提出了一种新的基于深度学习的方法，利用时空掩膜自动编码器（STMAE）从高度欠采样的波场数据中重建全波场数据。通过利用时间序列高稀疏度Lamb波场数据，我们的方法在采样率低至5%的情况下获得了显著的重建性能。此外，我们提出了一种基于贝叶斯优化的扫描路径优化方法，该方法产生自适应的稀疏空间采样模式，用于波场重建。这种自适应采样模式与STMAE（称为AdaSTMAE）的集成，在损伤区域周围产生较低精度的波场预测误差。对采样比进行了综合参数化研究，并在单损伤和多损伤两种情况下进行了对比试验验证。与没有自适应采样策略的情况相比，自适应采样策略的实施使单损伤情况下的重建误差降低了2-16%，在损伤区域周围的多损伤情况下的重建误差降低了0.7-5%。我们的实验结果表明，该技术在波场重建方面表现出色，在不同采样比（5%-25%）下，相对于原始掩蔽自编码器（MAE），重建误差降低了87-88%。此外，采用变厚度复合材料叶片进行的跨结构验证证实了该模型具有较强的泛化能力，无需微调即可有效地重建波前畸变和速度变化。

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引用次数: 0

Lightweight frameworks for real-time crack monitoring in civil infrastructure 用于民用基础设施裂缝实时监测的轻量级框架

IF 4.1 2区物理与天体物理 Q1 ACOUSTICS

Ultrasonics

Pub Date : 2026-01-20 DOI: 10.1016/j.ultras.2026.107970

Vindhyesh Pandey, S.S. Mishra

Cracks in concrete buildings, pavements and bridges are important signs of structural deterioration and present serious concerns to integrity and safety. Tradionally, manual inspection has been in use to detect the cracks which is labour-intensive, subjective and prone to errors. Recently, researchers have evolved an advanced automated techniques such as YOLO (You Only Look Once), to overcome the manual errors. Continuous refinements have led to the developments of sophisticated versions YOLOv4 to YOLOv11 in the YOLO series. This study suggests a customisation of YOLOv11, for the purpose of its quickness, high accuracy and recall. Using data augmentation, hyperparameter optimisation and transfer learning on a composite dataset of concrete crack images, this model is specifically customized for crack detection. Based on experimental and publicly accessible data like SDNET2018 (Structural Defects Network), this customized version outperforms baseline versions YOLOv5, YOLOv8, YOLOv9, YOLOv10 and YOLOv11. An mAP50 (mean Average Precision) value of 68.6% is achieved which is 3.47% higher as compared to YOLOv11. Similarly, a precision of 80.8% and recall of 63.6% are achieved. The study provides 50, 100, 200, 300 and 400 epochs for training and validation. The 100 layers and 6.3 GFLOPs (Giga Floating Point Operations Per Second) of this model are also very less compared to other given models which is an indicator of less complex model. This model has proved computationally efficient and suitable for real-time applications and robust to challenging conditions such as low contrast and complex backgrounds, making it a valuable tool for structural health monitoring.

混凝土建筑物、路面和桥梁的裂缝是结构恶化的重要标志，对完整性和安全构成严重关切。传统上，人工检查一直用于检测裂缝，这是劳动密集型的，主观的，容易出错。最近，研究人员开发了一种先进的自动化技术，如YOLO (You Only Look Once)，以克服人工错误。不断的改进导致了YOLO系列中复杂版本YOLOv4到YOLOv11的发展。本研究建议对YOLOv11进行定制，以提高其速度、准确性和召回率。在混凝土裂缝图像的复合数据集上使用数据增强、超参数优化和迁移学习，该模型是专门为裂缝检测定制的。基于SDNET2018（结构缺陷网络）等实验和公开可访问的数据，该定制版本优于基准版本YOLOv5， YOLOv8, YOLOv9， YOLOv10和YOLOv11。mAP50 （mean Average Precision）值达到68.6%，比YOLOv11高3.47%。同样，准确率为80.8%，召回率为63.6%。本研究提供了50、100、200、300和400个epoch用于训练和验证。与其他给定模型相比，该模型的100层和6.3 GFLOPs（每秒千兆浮点运算）也非常少，这是一个不太复杂的模型的指标。该模型已被证明具有计算效率，适合实时应用，并且对低对比度和复杂背景等具有挑战性的条件具有鲁棒性，使其成为结构健康监测的宝贵工具。

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