Advances in Engineering Software最新文献_第10页

Optimizing foam padding of the advanced combat helmet to maximize protection of blast-induced brain injury and wearing comfort 优化先进战斗头盔的泡沫填充物，最大限度地保护爆炸引起的脑损伤和佩戴舒适性

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-23 DOI: 10.1016/j.advengsoft.2025.103980

Chaokai Zhang , Feng Zhu , Wenye He , Zhiqing Cheng , Songbai Ji

The Advanced combat helmet (ACH) is critical for mitigating the risk of blast-induced traumatic brain injury (bTBI). Helmet foam pads are in continuous contact with the head to provide mechanical support. They are essential for helmet bTBI mitigation effectiveness and wearing comfort. In this study, we parametrically investigate the significance of foam pad thickness and relative density on reducing the peak intracranial pressure (ICP) from blast. In addition, we study how they influence the perceived comfort, by quantifying the distribution uniformity of ACH-to-scalp pressure resulting from gravity, referred to as the Comfort Index. Three specific pad thicknesses and random relative densities coupled with a range of trinitrotoluene (TNT) masses placed to the front or side of the helmet-head complex were used for simulation. The incidence pressures from the ConWep model were used as input for blast loading. The ratios between peak ICP in the corpus callosum and the peak incident pressure as well as the comfort indices were analyzed using a data-driven approach. A multi-functional design method, Pareto front, was used to identify sets of optimal parameters based on user preferred weighting factors for ICP reduction and head surface pressure distribution. Finally, a decision tree was applied to refine the rules for optimal designs. For an equal weighting on ICP reduction and surface pressure distribution, a pad thickness of 10 mm and relative density of 7.7 % were identified. This study demonstrates the effectiveness of combining Pareto front and decision trees for the identification of optimal design parameters for the ACH.

先进战斗头盔（ACH）对于降低爆炸引起的创伤性脑损伤（bTBI）的风险至关重要。头盔泡沫垫与头部持续接触，提供机械支撑。它们对于头盔bTBI缓解效果和佩戴舒适性至关重要。在本研究中，我们参数化研究了泡沫垫厚度和相对密度对降低爆炸后颅内压峰值的意义。此外，我们通过量化由重力引起的ach -头皮压力分布均匀性（称为舒适度指数）来研究它们如何影响感知舒适度。模拟使用了三种特定的垫层厚度和随机相对密度，以及放置在头盔-头部复合物前部或侧面的三硝基甲苯（TNT）质量范围。来自ConWep模型的入射压力被用作爆炸加载的输入。采用数据驱动的方法分析了胼胝体ICP峰值与入射压力峰值之间的比值以及舒适度指数。基于用户偏好加权因子，采用多功能设计方法Pareto front来确定ICP降低和水头表面压力分布的最优参数集。最后，运用决策树方法对优化设计规则进行细化。对于同等权重的ICP减少和表面压力分布，垫厚度为10毫米，相对密度为7.7%。本研究证明了将Pareto front和决策树相结合用于ACH最优设计参数识别的有效性。

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引用次数: 0

Multi-objective optimisation of complex mechanisms using Moving Spheres: An application to suspension elasto-kinematics 基于运动球体的复杂机构多目标优化：在悬架弹性运动学中的应用

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-20 DOI: 10.1016/j.advengsoft.2025.103974

Lorenzo De Santanna, Massimiliano Gobbi, Riccardo Malacrida, Gianpiero Mastinu

This paper presents a new iterative method, called Moving Spheres (MS), for solving multi-objective design optimisation problems involving three-dimensional mechanisms. The method is suited to problems in which most of the design variables belong to the three-dimensional Euclidean space. MS method is able to explore efficiently the design space and identifies the regions where the optimal solutions are located, resulting in a clear spatial representation of optimal solutions. In this paper, MS method is applied to the elasto-kinematic optimisation of an automotive suspension system. The optimal locations of suspension joints are sought within spherical neighbourhoods of a reference suspension. This preserves the kinematic compatibility of the mechanism and facilitates the exploration of the design space through iterative updates of the reference suspension. The rigorous

k

-optimality metric, which introduces a hierarchical sorting in the Pareto-optimal set, is employed to rank optimal design solutions. In the suspension test case, the Pareto-optimal set of approximated through Moving Spheres method is compared with the Pareto-optimal sets resulting from Parameter Space Investigation and multi-objective optimisation Genetic Algorithm with sorting (KEMOGA) methods, considering similar computational time. Moving Spheres method yields a more accurate approximation of the Pareto-optimal set.

本文提出了一种新的迭代方法，称为移动球体（MS），用于解决涉及三维机构的多目标设计优化问题。该方法适用于大多数设计变量属于三维欧几里德空间的问题。MS方法能够有效地探索设计空间并识别最优解所在的区域，从而使最优解具有清晰的空间表示。本文将质谱法应用于某汽车悬架系统的弹性运动学优化。在参考悬架的球面邻域内寻找悬架节点的最佳位置。这既保留了机构的运动兼容性，又便于通过参考悬架的迭代更新探索设计空间。采用严格的k-最优性度量，在帕累托最优集中引入层次排序，对最优设计方案进行排序。在悬架试验用例中，考虑相似的计算时间，将移动球法逼近的pareto最优集与参数空间调查和多目标优化排序遗传算法（KEMOGA）方法逼近的pareto最优集进行了比较。移动球体法产生了更精确的帕累托最优集近似值。

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引用次数: 0

Machine learning based optimal design of superelastic friction pendulum for controlling underground blast-induced vibration of building 基于机器学习的超弹性摩擦摆控制地下建筑爆震优化设计

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-19 DOI: 10.1016/j.advengsoft.2025.103978

Mohammad Yasir Mohammad Hasan Shaikh, Sourav Gur

This study shows the machine learning (ML) based optimal design and response controllability of superelastic shape memory alloy (SMA) integrated friction pendulum (SMA-FP) isolator under the blast induced ground motion (BIGM), and compared with the FP isolator. An elastic steel shear building isolated with the FP or SMA-FP system is analysed through nonlinear time-history analysis (NLTHA). Design of base isolators (BIs) are obtained through optimizing two conflicting objectives, i.e. top floor peak acceleration (TFPA) and peak isolator displacement (PID). A multi-objective optimization (MOO) algorithm is used to estimate the optimal combination of friction coefficient and SMA wire strength. Comparison of the pareto optimal front clearly reveals a better trade-off between two objective functions for SMA-FP BI than FP BI. Robustness of optimal design and control effectiveness is studied through extensive parametric studies, for various parameters of isolator, building, and BIGM. Study results reveal that, SMA-FP can substantially reduce the TFPA (up to 30 %) in conjunction with the PID and residual isolator displacement (RID), reduction up to 42 % and 60 %, respectively, than FP BI. Finally, employing different ML based regression methods (multilinear, ridge, lasso, elastic-net regression), predictive models have been proposed for optimal design and optimum responses of structure and isolator.

研究了基于机器学习（ML）的超弹性形状记忆合金（SMA）集成摩擦摆（SMA-FP）隔振器在爆炸致地震动（BIGM）作用下的优化设计和响应可控性，并与FP隔振器进行了比较。采用非线性时程分析（NLTHA）对采用FP或SMA-FP体系隔震的弹性钢剪力建筑进行了分析。通过对顶楼峰值加速度（TFPA）和隔离器峰值位移（PID）两个相互冲突的目标进行优化，得到了基础隔离器（BIs）的设计。采用多目标优化算法估计摩擦系数和SMA钢丝强度的最优组合。对pareto最优前沿的比较清楚地揭示了SMA-FP BI比FP BI在两个目标函数之间更好的权衡。通过对隔离器、建筑物和BIGM的各种参数进行广泛的参数研究，研究了优化设计的鲁棒性和控制有效性。研究结果表明，SMA-FP结合PID和剩余隔离器位移（RID）可以显著降低TFPA（高达30%），比FP BI分别降低42%和60%。最后，采用不同的基于机器学习的回归方法（多元线性回归、脊回归、套索回归、弹性网回归），提出了结构和隔振器优化设计和优化响应的预测模型。

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引用次数: 0

An improved YOLOv10-based lightweight multi-scale feature fusion model for road defect detection and its applications 基于yolov10改进的轻型多尺度特征融合道路缺陷检测模型及其应用

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-13 DOI: 10.1016/j.advengsoft.2025.103976

Jianxi Ou , Jianqin Zhang , Haoyu Li , Bin Duan

Intelligent road damage detection is critical for ensuring traffic safety and extending the lifespan of roads. However, existing methods struggle to balance high accuracy and real-time performance in complex detection scenarios and resource-constrained environments. To address this issue, this study proposes a lightweight multi-scale feature fusion model based on an improved YOLOv10—GAS-YOLO. The model utilizes a novel lightweight architecture (GSF-ST) designed through a combination of feature generation, asymmetric convolution, and grouped channel shuffling optimization strategies, significantly reducing computational complexity and parameter count while enhancing both global and local feature representation. To improve multi-scale damage detection performance, GAS-YOLO incorporates an improved bidirectional feature pyramid network (BiFPN) and Swin Transformer module. A resolution halving and channel doubling strategy enhances the detection ability of small targets. Moreover, the WiOU loss function further optimizes bounding box regression accuracy, mitigating errors caused by sample imbalance. Channel pruning techniques are applied to achieve secondary lightweight compression of the model, resulting in significant resource savings. Through comparative experiments and ablation analysis with several advanced damage detection models, this study demonstrates a significant performance improvement of GAS-YOLO. Experimental results show that GAS-YOLO exhibits outstanding performance in multi-scale damage detection tasks, with 5.6 M parameters, 8.4GFLOPs of computational complexity, and a model size of only 5.8 MB. Compared to baseline models, detection accuracy improves by 10.8 %, computational complexity is reduced by 2.57 times, and parameter count is reduced by 1.29 times, with an average detection accuracy of 86.5 % and a single image processing time of 6.1 ms. Validation on both public datasets and self-constructed datasets further proves its real-time processing capability while maintaining high accuracy. The GAS-YOLO model proposed in this study not only provides a practical solution for road damage detection in resource-constrained environments but also offers new insights for intelligent management of intelligent transportation and urban infrastructure, with broad application prospects.

智能道路损伤检测对于确保交通安全和延长道路寿命至关重要。然而，在复杂的检测场景和资源受限的环境中，现有的方法难以平衡高精度和实时性。为了解决这一问题，本研究提出了一种基于改进的YOLOv10-GAS-YOLO的轻量级多尺度特征融合模型。该模型采用了一种新型的轻量级架构（GSF-ST），通过结合特征生成、非对称卷积和分组信道变换优化策略设计，显著降低了计算复杂度和参数数量，同时增强了全局和局部特征表示。为了提高多尺度损伤检测性能，GAS-YOLO结合了改进的双向特征金字塔网络（BiFPN）和Swin Transformer模块。分辨率减半和信道加倍策略提高了小目标的检测能力。此外，WiOU损失函数进一步优化了边界盒回归的精度，减轻了样本不平衡带来的误差。通道修剪技术用于实现模型的二次轻量级压缩，从而显著节省资源。通过与几种先进的损伤检测模型的对比实验和烧蚀分析，本研究证明了GAS-YOLO的性能有显著提高。实验结果表明，GAS-YOLO在多尺度损伤检测任务中表现优异，参数为5.6 M，计算复杂度为4.4 gflops，模型大小仅为5.8 MB，检测精度比基线模型提高10.8%，计算复杂度降低2.57倍，参数数量减少1.29倍，平均检测精度达到86.5%，单幅图像处理时间为6.1 ms。通过对公共数据集和自构建数据集的验证，进一步证明了该方法在保持较高精度的同时具有实时性。本研究提出的GAS-YOLO模型不仅为资源受限环境下的道路损伤检测提供了切实可行的解决方案，也为智能交通和城市基础设施的智能管理提供了新的见解，具有广阔的应用前景。

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引用次数: 0

Advanced numerical modeling for nonlinear responses of sandwich multiphase composite plates with viscoelastic damping core 粘弹性阻尼芯夹层多相复合材料板非线性响应的先进数值模拟

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-13 DOI: 10.1016/j.advengsoft.2025.103958

Ho-Nam Vu , Duy-Khuong Ly , Umut Topal , Tan Nguyen , T. Nguyen-Thoi

This study introduces an advanced numerical model for the nonlinear dynamic analysis of sandwich multiphase composite plates composed of carbon nanotubes (CNT), carbon fibers, and epoxy, featuring a viscoelastic core modeled using the Golla–Hughes–McTavish (GHM) method. The proposed framework uniquely combines the Cell-Based Smoothed Discrete Shear Gap Method (CS-DSG3) with the sinusoidal–zigzag shear deformation theory, pioneering their integration to improve layerwise modeling and viscoelastic damping analysis. The sinusoidal–zigzag theory effectively captures the continuous in-plane displacement distributions, yielding superior predictions of the layered structure’s mechanical response compared to classical theories. By incorporating the von Kármán displacement–strain relationship, the model effectively captures the passive damped dynamic behavior of viscoelastic core plate under large deformations. The Newmark time integration scheme and Picard’s methods are employed to efficiently solve the resulting nonlinear equations of motion at each time step. Validation against benchmark studies demonstrates the model’s accuracy and reliability in capturing the complex dynamic responses of laminated systems. A comprehensive parametric investigation further explores the impact of material properties, including the volume fractions and configurations of CNTs and carbon fibers, on the nonlinear dynamic behavior. These advancements position the model as a computationally efficient and high-fidelity tool for analyzing the nonlinear dynamics of complex laminated composite structures.

本文引入了一种先进的数值模型，用于碳纳米管（CNT）、碳纤维和环氧树脂组成的夹层多相复合材料板的非线性动力学分析，该模型采用了Golla-Hughes-McTavish （GHM）方法建模。提出的框架独特地将基于单元的平滑离散剪切间隙方法（CS-DSG3）与正弦之字形剪切变形理论相结合，开创了它们的集成，以改进分层建模和粘弹性阻尼分析。与经典理论相比，正弦之字形理论有效地捕获了连续的平面内位移分布，对层状结构的力学响应做出了更好的预测。该模型通过引入von Kármán位移-应变关系，有效地捕捉了粘弹性芯板在大变形下的被动阻尼动力行为。采用Newmark时间积分格式和Picard方法有效地求解每个时间步长的非线性运动方程。对基准研究的验证表明，该模型在捕获层压系统的复杂动态响应方面具有准确性和可靠性。全面的参数研究进一步探讨了材料性能，包括碳纳米管和碳纤维的体积分数和结构，对非线性动态行为的影响。这些进步使该模型成为一种计算效率高、保真度高的工具，用于分析复杂层压复合材料结构的非线性动力学。

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引用次数: 0

Probabilistic slope stability analysis based on the Hermite-logistic regression approach 基于Hermite-logistic回归方法的概率边坡稳定性分析

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-11 DOI: 10.1016/j.advengsoft.2025.103973

Yi Liang , Zhengpeng Jia , Qinglong Wu , Kefeng Xiao , Ran Yuan , Haizuo Zhou , Yi He

In slope reliability analysis, conventional surrogate model-based analysis methods, such as response surface method, Kriging method, and neural networks method, often rely on the safety factor of slopes for analysis. However, the calculation of safety factors requires repeated iterations using strength reduction, leading to low efficiency in reliability analysis. Addressing this challenge, this manuscript proposes an improved slope reliability analysis method to improve analysis efficiency. This method, which considers the spatial variability of soil parameters, is based on the principles of binary classification concept. It employs the Karhunen-Loève (K-L) expansion to discretize the soil of the slope and generate a random field. By combining Hermite polynomials with logistic regression approach, a surrogate model is established. Using the intrinsic program in FLAC^3D for convergency determination, the stability classification (stable or unstable) for each slope is carried out without reducing the soil strength parameters (using original soil strength parameters). The classification results serve as response values for the Hermite-logistic regression surrogate model, establishing an implicit relationship between random variables and slope stability. The effectiveness of this Hermite-logistic regression method is verified through examples of undrained saturated clay slopes and c-φ soil slopes. The findings indicate that the Hermite-logistic regression model demonstrates remarkable computational efficiency when compared to conventional random finite element calculations, all while maintaining high computational accuracy. Specifically, the proposed method reduces the computational cost by at least a factor of ten while ensuring the attainment of precise results. In addition, a sensitivity analysis is performed to investigate the influence of slope geometric parameters and spatial variability parameters on slope stability and reliability.

在边坡可靠度分析中，传统的基于代理模型的分析方法，如响应面法、Kriging法、神经网络法等，往往依赖于边坡的安全系数进行分析。然而，安全系数的计算需要使用强度折减法进行反复迭代，导致可靠性分析效率较低。针对这一挑战，本文提出了一种改进的边坡可靠度分析方法，以提高分析效率。该方法基于二元分类概念，考虑了土壤参数的空间变异性。采用karhunen - lo (K-L)展开对边坡土体进行离散化，生成随机场。将Hermite多项式与logistic回归方法相结合，建立了一个代理模型。利用FLAC3D中的固有程序进行收敛判定，在不降低土强度参数（使用原土强度参数）的情况下，对每个边坡进行稳定性分类（稳定或不稳定）。分类结果作为Hermite-logistic回归代理模型的响应值，建立了随机变量与边坡稳定性之间的隐式关系。通过不排水饱和粘土边坡和c-φ土边坡实例验证了该方法的有效性。研究结果表明，与传统的随机有限元计算相比，Hermite-logistic回归模型具有显著的计算效率，同时保持了较高的计算精度。具体而言，该方法在确保获得精确结果的同时，将计算成本降低了至少十倍。此外，还对边坡几何参数和空间变异性参数对边坡稳定性和可靠度的影响进行了敏感性分析。

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引用次数: 0

Metaheuristic algorithms-optimized machine learning models for FRP-concrete interfacial bond strength prediction 基于元启发式算法优化的frp -混凝土界面粘结强度预测机器学习模型

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-07 DOI: 10.1016/j.advengsoft.2025.103971

Peng Ge , Ou Yang , Jia He , Zhiyu Liu , Hao Chen

Globally, the technique of reinforcing concrete structures with bonded fiber-reinforced polymers (FRP) has become widely adopted. The integrity of the interface between concrete and FRP significantly influences the behavior of the reinforced structure. Consequently, precise prediction of the bond strength at the concrete and FRP interface is crucial for the logical design and assessment of structures that are repaired and reinforced using FRP. This paper utilizes two emerging metaheuristic algorithms, the Slime Mould Algorithm (SMA) and the Dung Beetle Optimization Algorithm (DBO), to improve the performance of machine learning (ML) techniques, including KNN, SVR, GBDT, and XGBoost. Optimizing the ML models with metaheuristic algorithms significantly enhanced the prediction accuracy compared to the non-optimized models. The SMA-GBDT performed better than other ML models, achieving an R² of 0.9492, an MAE of 1.5294, an MSE of 6.4159, an RMSE of 2.5329, and a MAPE of 8.6916, based on the testing dataset. Specifically, the SMA-GBDT model exhibited improvements of 5.83%, 39.04%, 50.75%, 29.82%, and 43.84% in R², MAE, MSE, RMSE, and MAPE, respectively, compared to the non-optimized GBDT. The predictions made by the SMA-GBDT model were higher precision than those provided by the current design codes and existing models.

在全球范围内，结合纤维增强聚合物（FRP）加固混凝土结构的技术已被广泛采用。混凝土与FRP之间界面的完整性对加筋结构的性能有重要影响。因此，精确预测混凝土和FRP界面的粘结强度对于使用FRP修复和加固的结构的逻辑设计和评估至关重要。本文利用两种新兴的元启发式算法——黏菌算法（SMA）和屎壳郎优化算法（DBO）来提高机器学习（ML）技术的性能，包括KNN、SVR、GBDT和XGBoost。与未优化的模型相比，用元启发式算法优化ML模型显著提高了预测精度。基于测试数据，SMA-GBDT比其他ML模型表现更好，R²为0.9492，MAE为1.5294，MSE为6.4159，RMSE为2.5329，MAPE为8.6916。其中，与未优化的GBDT模型相比，SMA-GBDT模型在R²、MAE、MSE、RMSE和MAPE上分别提高了5.83%、39.04%、50.75%、29.82%和43.84%。SMA-GBDT模型的预测精度高于现行设计规范和现有模型的预测精度。

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引用次数: 0

Layout optimization design method for thermo-elastic thin-walled structures with lattices and stiffeners 带有加劲肋的热弹性薄壁结构布局优化设计方法

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-07 DOI: 10.1016/j.advengsoft.2025.103962

Yang Li , Tong Gao , Yongbin Huang , Longlong Song , Weihong Zhang

This work proposes a layout optimization design method for thermo-elastic thin-walled structures with lattices and stiffeners in the framework of multi-material topology optimization, in which both the steady-state temperature field and mechanical loads are considered. Firstly, taking into account the design requirements, suitable lattice unit cells are chosen and their equivalent mechanical properties are obtained by the homogenization method. Thus, the candidate lattice unit cells are represented as corresponding virtual homogeneous materials. Meanwhile, the stiffeners are modelled with solid material. Afterwards, a multi-material thermo-elastic structural optimization formulation is established and solved iteratively through gradient-driven optimization algorithms to obtain the optimized layouts of the lattices and stiffeners. In addition, the maximum size constraint and the overall volume constraint with a lower bound are introduced. The former ensures that the solid material takes the form of 'ribs' in the optimization results and the latter could meet the requirement that the design space is filled with lattice or solid material. Finally, numerical tests are conducted to demonstrate the detailed application process and validate the effectiveness of the proposed design method. This work provides an effective design tool for the application of additively manufactured lattice structures in thermo-elastic coupled load-bearing structures.

本文提出了一种在多材料拓扑优化框架下兼顾稳态温度场和机械载荷的格筋热弹性薄壁结构布局优化设计方法。首先，根据设计要求，选择合适的点阵单元，并采用均匀化方法获得其等效力学性能；因此，候选晶格单元被表示为相应的虚拟均质材料。同时，用固体材料对加强筋进行建模。然后，建立了多材料热弹性结构优化公式，并通过梯度驱动优化算法进行迭代求解，得到了优化的格和加强筋布局。此外，还引入了最大尺寸约束和带下界的总体体积约束。前者确保固体材料在优化结果中以“肋”的形式存在，后者可以满足设计空间以晶格或固体材料填充的要求。最后，通过数值试验验证了该方法的具体应用过程，验证了该设计方法的有效性。本工作为增材制造晶格结构在热弹性耦合承重结构中的应用提供了有效的设计工具。

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引用次数: 0

A lightweight convolutional neural network-based model and system for defect detection and navigation on bridge road surface 基于轻量级卷积神经网络的桥梁路面缺陷检测与导航模型与系统

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-07 DOI: 10.1016/j.advengsoft.2025.103972

Ronghua Fu , Yufeng Zhang , Drahomír Novák , Alfred Strauss , Maosen Cao

The Faster Region-based Convolutional Neural Network (Faster R-CNN) is widely used for detecting defects on road surface. However, its effectiveness in this task is limited by its large model size and slow detection speed. To address these challenges, two versions of the Faster R-CNN model—small and large—were developed. First, the models were structurally optimized by integrating inverted residual blocks, depthwise separable convolutions, and attention mechanisms to improve efficiency and performance. The large version also incorporated multi-scale feature extraction for enhanced detection capabilities. Second, model pruning was applied to further compress the networks. Extensive ablation experiments were conducted to investigate the relationship between the model's internal structure and its impact on crack detection accuracy and efficiency. The experimental results demonstrate that the proposed models outperform general CNN-based models in bridge surface defect detection, achieving superior detection speed while maintaining high accuracy. The large version exhibits better performance but at the cost of increased model complexity. Testing was conducted on a real-life bridge in Nanjing, China. Additionally, a software application, integrated with a laptop and a smartphone, was deployed to identify defects and map their locations on the bridge, streamlining the detection process. The source code of this software is freely available at https://github.com/DUYA686686/detection-software.git

基于更快区域的卷积神经网络（Faster R-CNN）被广泛应用于路面缺陷检测。然而，由于模型规模大，检测速度慢，限制了该方法在该任务中的有效性。为了应对这些挑战，我们开发了两个版本的Faster R-CNN模型——小型和大型。首先，通过整合倒立残差块、深度可分卷积和注意机制对模型进行结构优化，以提高效率和性能。大型版本还集成了多尺度特征提取，以增强检测能力。其次，采用模型剪枝进一步压缩网络；通过广泛的烧蚀实验来研究模型内部结构及其对裂纹检测精度和效率的影响。实验结果表明，该模型在桥梁表面缺陷检测方面优于一般基于cnn的模型，在保持较高精度的同时，检测速度更快。大版本表现出更好的性能，但代价是增加了模型的复杂性。测试是在中国南京一座真实的桥梁上进行的。此外，还部署了一个集成了笔记本电脑和智能手机的软件应用程序来识别缺陷，并绘制出它们在桥上的位置，简化了检测过程。该软件的源代码可在https://github.com/DUYA686686/detection-software.git免费获得

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引用次数: 0

An isogeometric approach to supersonic flutter analysis of lightweight-designed plates with graphene reinforcement 石墨烯配筋轻量化板的超音速颤振分析等几何方法

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-06-06 DOI: 10.1016/j.advengsoft.2025.103955

Nam V. Nguyen, Thoai N. Tran

In recent years, there has been an increasing emphasis on implementing high-performance, lightweight designs in a wide range of contemporary interdisciplinary applications. The primary objective of this paper, therefore, is to present a NURBS-based isogeometric approach for a comprehensive investigation into the supersonic flutter characteristics of graphene-reinforced functionally graded (FG) metal foam plates. The lightweight structures are designed using a combination of two porosity distributions and two graphene dispersion patterns, featuring both uniform and non-uniform configurations. The mathematical equations governing the dynamic behavior of the porous plates are derived using a computational approach based on generalized higher-order shear deformation theory (HSDT) within a NURBS-based isogeometric analysis (IGA). A first-order approximation of piston theory is employed to model the fluid–structure interaction by estimating the aerodynamic forces induced by high-speed airflow. The accuracy of the current approach is assessed and validated against the analytical approach and other existing benchmark results. Several extensive parametric investigations are subsequently conducted to fulfill the primary goal of this research: to clarify the influence of internal porosity and graphene nanofiller on the flutter boundaries and associated vibrational modes of lightweight-designed plate structures. The obtained results demonstrate that graphene-reinforced FG cellular plates possess exceptional properties, such as high stiffness and reduced weight, making them well-suited for advanced technological applications. Furthermore, the present findings offer valuable insights that can assist in the design and fabrication, with the goal of improving the robustness and efficacy of future practical engineering structures.

近年来，人们越来越重视在广泛的当代跨学科应用中实现高性能、轻量化设计。因此，本文的主要目标是提出一种基于nurbs的等几何方法来全面研究石墨烯增强功能梯度（FG）金属泡沫板的超音速颤振特性。轻质结构的设计结合了两种孔隙率分布和两种石墨烯分散模式，具有均匀和非均匀的配置。在基于nurbs的等高几何分析（IGA）中，使用基于广义高阶剪切变形理论（HSDT）的计算方法推导了控制多孔板动态行为的数学方程。采用一阶近似活塞理论，通过估计高速气流引起的气动力来模拟流固耦合。根据分析方法和其他现有基准结果评估和验证当前方法的准确性。随后进行了几项广泛的参数研究，以实现本研究的主要目标：阐明内部孔隙率和石墨烯纳米填料对轻量化板结构颤振边界和相关振动模式的影响。结果表明，石墨烯增强FG细胞板具有优异的性能，如高刚度和减轻重量，使其非常适合于先进的技术应用。此外，目前的研究结果提供了有价值的见解，可以帮助设计和制造，以提高未来实际工程结构的稳健性和有效性。

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