Advances in Engineering Software最新文献_第7页

Optimization design and simplified model of a multi-layered nested tubular structure for train collision protection 列车碰撞防护多层嵌套管状结构优化设计及简化模型

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1016/j.advengsoft.2025.104039

Jun Chen , Biao Wei , Lizhong Jiang , Xianglin Zheng , Shuaijie Yuan , Mingyu Chen

Rising incidence of train derailments and collisions underscores the urgent need for more effective passive energy‐absorbing systems. While conventional aluminum honeycomb devices achieve high specific energy absorption, they suffer from complex fabrication, require full replacement after minor impacts, and generate high rebound velocities that can exacerbate secondary damage. In this study, we propose a Multi-layered Nested Tubular Structure (MNTS)—an arrangement of adjustable square and circular thin-walled tubes—as an alternative absorber. A physics–based finite‐element (FE) model, incorporating material nonlinearity, simulates a lead‐car collision against a rigid wall and is validated against full-scale experiments (velocity: 8.357 m/s; mass: 54 t). The model accurately reproduces peak absorbed energy, average force response, displacement history, and rebound velocity. A parametric study of 144 FE simulations combined with response surface methodology identifies optimal wall‐thickness parameters (λ_s = 7.4 mm, λ_c = 18.6 mm), yielding a maximum energy absorption of 1.728 MJ (R_MSE = 0.0477 MJ, R² = 0.945). Building on these results, we develop a reduced‐order analytical model using logistic regression to relate train speed (5.0–9.0 m/s) to peak force, maximum displacement, and energy absorption, achieving an R² of 0.989 for displacement predictions. Validation against 41 additional FE runs confirms the analytical model’s accuracy while reducing computational cost by orders of magnitude. Compared with honeycomb absorbers, the MNTS matches energy-absorption efficiency yet significantly lowers peak impact forces and rebound velocities, thereby enhancing passenger safety. Together, the validated FE framework and its streamlined analytical counterpart constitute a rapid, practical design and assessment tool for train collision energy-absorption systems.

列车脱轨和碰撞事故的发生率不断上升，迫切需要更有效的被动吸能系统。虽然传统的铝蜂窝装置可以实现高比能吸收，但它们的制造复杂，在轻微撞击后需要完全更换，并且产生高回弹速度，可能会加剧二次损伤。在这项研究中，我们提出了一种多层嵌套管结构(MNTS) -一种可调节的方形和圆形薄壁管的排列-作为替代吸收器。基于物理的有限元（FE）模型，结合材料非线性，模拟了铅车与刚性墙壁的碰撞，并在全尺寸实验中进行了验证（速度：8.357 m/s；质量：54 t）。该模型准确地再现了峰值吸收能量、平均力响应、位移历史和回弹速度。结合响应面法对144个有限元模拟进行参数化研究，确定了最佳壁厚参数（λs = 7.4 mm, λc = 18.6 mm），最大吸能为1.728 MJ （RMSE = 0.0477 MJ, R²= 0.945）。在这些结果的基础上，我们开发了一个使用逻辑回归的降阶分析模型，将列车速度（5.0-9.0 m/s）与峰值力、最大位移和能量吸收联系起来，实现了位移预测的R²为0.989。对41个额外FE运行的验证证实了分析模型的准确性，同时降低了计算成本的数量级。与蜂窝式吸能器相比，MNTS在满足吸能效率的同时，显著降低了峰值冲击力和回弹速度，从而提高了乘客的安全性。经过验证的有限元框架和简化的分析框架共同构成了列车碰撞吸能系统快速实用的设计和评估工具。

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引用次数: 0

Research on coal wall parameter calibration and high precision model construction based on discrete element method 基于离散元法的煤壁参数标定及高精度模型构建研究

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-12-01 Epub Date: 2025-09-10 DOI: 10.1016/j.advengsoft.2025.104032

Xin Jin , Dongpo Han , Guochao Zhao , Lijuan Zhao

The accuracy of discrete element coal wall model significantly influences the characterization of coal-rock breaking mechanisms and equipment dynamic response in virtual prototype simulation. Based on coal-rock samples from Ordos Wenyu Mine of Yanzhou Coal Mining, key Tavares UFRJ parameters affecting particle compressive strength were identified through Plackett-Burman test and steepest ascent experiment. Breakage parameters were calibrated using optimal latin hypercube sampling (OLHS) and gaussian process regression (GPR). Hertz-Mindlin with Bonding parameters were then calibrated via uniaxial compression simulation. Model accuracy was verified through discrete element method-multi flexible body dynamics (DEM-MFBD) coupling simulation. Results indicate that D0, E Infinity, and Phi are the most significant parameters with influence rates of 38.5 %, 30.5 %, and 18.6 % respectively. The relative error between simulated and experimental particle compressive strength is below 4.56 %, while uniaxial compression simulation shows maximum relative error below 9.80 %. Comparing tri-axial load curves during shearer drum cutting, the maximum relative error of mean values between experimental and simulation data is 3.72 %, with maximum root mean square error (RMSE) of 4.60 %, outperforming traditional models and validating the model's accuracy and reliability for dynamic cutting process simulation.

在虚拟样机仿真中，离散单元煤壁模型的准确性对煤岩破碎机理表征和设备动态响应具有重要影响。以兖州煤矿鄂尔多斯文玉矿煤岩样品为研究对象，通过Plackett-Burman试验和最陡爬坡试验，确定了影响颗粒抗压强度的Tavares UFRJ关键参数。采用最优拉丁超立方体抽样（OLHS）和高斯过程回归（GPR）对断裂参数进行了标定。然后通过单轴压缩模拟校准带有键合参数的Hertz-Mindlin。通过离散元法-多柔体动力学（DEM-MFBD）耦合仿真验证了模型的准确性。结果表明，D0、E∞和Phi是最显著的参数，其影响率分别为38.5%、30.5%和18.6%。模拟颗粒抗压强度与实验颗粒抗压强度的相对误差小于4.56%，单轴压缩模拟颗粒抗压强度的最大相对误差小于9.80%。对比采煤机滚筒切削过程的三轴载荷曲线，实验值与仿真值的最大平均值相对误差为3.72%，均方根误差（RMSE）最大为4.60%，优于传统模型，验证了该模型用于动态切削过程仿真的准确性和可靠性。

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引用次数: 0

Mooring tension estimation for multi-connected floating photovoltaic arrays via LSTM networks 基于LSTM网络的多连接浮式光伏阵列系泊张力估计

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-12-01 Epub Date: 2025-09-13 DOI: 10.1016/j.advengsoft.2025.104037

Jihun Song , Chungkuk Jin , Do Kyun Kim , Donghwi Jung , Seungjun Kim

Accurately estimating mooring‑line tension is essential for the safe operation of large, multiconnected floating‑photovoltaic (FPV) arrays, yet installing load cells on every line is impractical. This study develops and evaluates data‑driven tension estimators that use only motion responses generated from time‑domain hydrodynamic simulations. A long short‑term memory (LSTM) network trained on displacements provides the reference performance. When trained instead on raw accelerations, the model performs noticeably worse, reflecting the spectral mismatch between acceleration and tension signals. Adding directional spreading to the training data restores robustness for the displacement‑based model under oblique seas, but offers limited benefit for the acceleration‑based model. In this study, a physics‑guided LSTM is proposed to reduce reliance on displacement sensors, in which a learnable filter transforms accelerations into displacement‑like features. This hybrid model narrows the performance gap, achieving stable and robust prediction performance. The proposed model attains accuracy comparable to displacement‑based estimation, demonstrating its effectiveness with accelerometer input alone and highlighting its potential as a cost‑efficient tool for structural health monitoring of large‑scale FPV systems.

准确估计系泊线张力对于大型多连接浮动光伏（FPV）阵列的安全运行至关重要，然而在每条线上安装称重传感器是不切实际的。本研究开发和评估仅使用时域流体动力学模拟产生的运动响应的数据驱动张力估计器。基于位移训练的长短期记忆（LSTM）网络提供了参考性能。当在原始加速度上进行训练时，模型的表现明显更差，反映了加速度和张力信号之间的频谱不匹配。在训练数据中加入方向扩展可以恢复斜海下基于位移的模型的鲁棒性，但对基于加速度的模型的好处有限。在这项研究中，提出了一个物理引导的LSTM来减少对位移传感器的依赖，其中一个可学习的滤波器将加速度转换为类似位移的特征。该混合模型缩小了性能差距，实现了稳定、稳健的预测性能。所提出的模型达到了与基于位移的估计相当的精度，证明了其单独使用加速度计输入的有效性，并突出了其作为大型FPV系统结构健康监测的成本效益工具的潜力。

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引用次数: 0

Seismic performance comparison of passive and active friction-tuned mass dampers in tall buildings considering soil-structure interaction effects 考虑土-结构相互作用的高层建筑被动与主动摩擦调谐质量阻尼器抗震性能比较

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-12-01 Epub Date: 2025-09-26 DOI: 10.1016/j.advengsoft.2025.104036

Morteza Akbari , Abbas-Ali Zamani , Tomasz Falborski , Sadegh Etedali , Robert Jankowski

This study investigates the enhancement of seismic performance in tall buildings by utilising passive friction-tuned mass dampers (PFTMDs), with a specific emphasis on the impact of soil-structure interaction (SSI). The novelty of this work lies in integrating multi-objective optimization of PFTMDs with consideration of SSI effects, providing a comprehensive comparison with active damping strategies under various soil conditions. A multi-objective marine predator algorithm (MOMPA) is employed to optimize the damper parameters. The analysis incorporates four earthquake ground motion records and three soil types: soft, medium, and dense. The optimized performance of the PFTMD is compared to that of an active friction-tuned mass damper (AFTMD), which is controlled by a proportional-integral-derivative with filter (PIDF) controller, also optimized via MOMPA. Both control strategies are benchmarked against an uncontrolled structure. The results indicate that both systems significantly enhance the seismic performance of the structure by reducing displacement, acceleration, and inter-storey drift. However, the AFTMD consistently outperforms the PFTMD across all soil conditions. To account for the unpredictability of seismic events, the study further assesses damper performance using averaged parameters derived from multiple earthquake-soil combinations. While the PFTMD performs effectively when tuned for specific conditions, its performance declines under averaged settings and can even amplify structural responses. Conversely, the AFTMD shows robust and consistent performance, establishing it as a more reliable solution for seismic mitigation in tall buildings.

本研究探讨了利用被动摩擦调谐质量阻尼器（pftmd）增强高层建筑的抗震性能，特别强调了土-结构相互作用（SSI）的影响。本工作的新颖之处在于将pftmd的多目标优化与SSI效应相结合，在不同土壤条件下与主动阻尼策略进行了全面比较。采用多目标海洋掠食者算法（MOMPA）对阻尼器参数进行优化。该分析结合了四次地震地面运动记录和三种土壤类型：软土、中土和致密土。PFTMD的优化性能与主动摩擦调谐质量阻尼器（AFTMD）进行了比较，后者由带滤波器的比例-积分-导数（PIDF）控制器控制，也通过MOMPA进行了优化。这两种控制策略都是针对非受控结构进行基准测试的。结果表明，两种体系通过减小位移、加速度和层间漂移，显著提高了结构的抗震性能。然而，AFTMD在所有土壤条件下的表现都优于PFTMD。为了考虑地震事件的不可预测性，该研究进一步使用从多个地震-土壤组合中得出的平均参数来评估阻尼器的性能。虽然PFTMD在针对特定条件进行调整时表现有效，但其性能在平均设置下会下降，甚至会放大结构响应。相反，AFTMD表现出稳健和一致的性能，使其成为高层建筑中更可靠的抗震解决方案。

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引用次数: 0

Stochastic vibration mechanisms in irregular coupled plate of supersonic porous functionally graded materials with temperature gradients 具有温度梯度的超音速多孔功能梯度材料不规则耦合板的随机振动机理

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-11-15 Epub Date: 2025-08-06 DOI: 10.1016/j.advengsoft.2025.104002

Hui Dong , Rui Zhong , Qingshan Wang , Tao Liu , Long Yu

The porous irregular functional gradient material (FGM) coupled plates, composed of two arbitrary quadrilateral plates coupled at any angle, are widely used in aerospace applications and equipment such as hypersonic vehicles. This paper investigates the stochastic response mechanisms of the porous irregular FGM coupled plate under aerothermal environments and base acceleration excitations. Three typical geometric models are established to validate the universality of the present method. The equations derived from supersonic piston theory and Mindlin plate theory incorporate temperature-dependent material properties. Subplate displacements are approximated using the first-kind Chebyshev polynomials, with irregular domain integrals resolved through coordinate transformations. Sufficient comparisons with the finite element method (FEM) and published literature confirm the accuracy and computational efficiency of this approach. The resulting systematic framework enables stochastic response analysis in analogous complex structures. Numerical discussions are conducted to analyze the effects of FGM gradient p, porosity ζ, coupling angle θ, boundary conditions, and temperature variations ΔT on the stochastic response, establishing practical tools for optimizing and conducting rapid integrity assessment of such structures.

不规则功能梯度材料（FGM）多孔耦合板是由两个任意四边形板以任意角度耦合而成的，广泛应用于航空航天和高超声速飞行器等设备。研究了不规则多孔FGM耦合板在空气热环境和基础加速度激励下的随机响应机制。建立了三个典型的几何模型，验证了该方法的通用性。由超声速活塞理论和明德林板理论导出的方程包含了与温度相关的材料特性。底板位移近似采用第一类切比雪夫多项式，通过坐标变换求解不规则域积分。与有限元法和已发表的文献进行了充分的比较，证实了该方法的准确性和计算效率。由此产生的系统框架使类似复杂结构的随机响应分析成为可能。通过数值讨论，分析了梯度p、孔隙度ζ、耦合角θ、边界条件和温度变化ΔT对随机响应的影响，建立了优化和快速完整性评估的实用工具。

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引用次数: 0

Optimization design method of C30/C40 fly ash concrete based on machine learning and elite retention genetic algorithm 基于机器学习和精英保留遗传算法的C30/C40粉煤灰混凝土优化设计方法

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-11-15 Epub Date: 2025-09-01 DOI: 10.1016/j.advengsoft.2025.104019

Mingyue Hao , Yue Li , Xiwang Chen , Kun Ni , Wei Li

This paper establishes an intelligent optimization design method for fly ash (FA) concrete considering 28-day compressive strength, slump, and carbon emissions based on machine learning (ML) and elite retention genetic algorithm (EGA). The results demonstrate that the Extreme Gradient Boosting (XGB) model achieves high accuracy in predicting compressive strength, while Gradient Boosting (GB) shows higher accuracy and generalization ability in predicting slump. The water-to-binder ratio and cement content have a significant impact on the compressive strength of FA concrete. Reducing the water-to-binder ratio or increasing cement content helps improve compressive strength. The dosage of superplasticizer and the water content are key factors in controlling the slump. Properly increasing the dosage of superplasticizer and water content can effectively improve the slump of concrete. The FA concrete intelligent design system developed based on the XGB model, GB model, and EGA algorithm can efficiently obtain the optimal preparation parameters and accurately predict the corresponding performance. Furthermore, the carbon emissions of the optimized C30 and C40 FA concrete decrease by 12.72 % and 17.44 % respectively compared to the baseline concrete. Finally, the experimental results verify the prediction accuracy and generalization ability of the XGB and GB models, with the relative prediction error of C30 and C40 FA concrete both being less than 8 %.

本文建立了一种基于机器学习（ML）和精英保留遗传算法（EGA）的考虑28天抗压强度、坍落度和碳排放的粉煤灰混凝土智能优化设计方法。结果表明，极端梯度增强（XGB）模型在预测抗压强度方面具有较高的精度，梯度增强（GB）模型在预测坍落度方面具有较高的精度和泛化能力。水胶比和水泥掺量对FA混凝土抗压强度有显著影响。降低水胶比或增加水泥掺量有助于提高抗压强度。高效减水剂的用量和含水量是控制坍落度的关键因素。适当增加减水剂掺量和水掺量，可有效改善混凝土坍落度。基于XGB模型、GB模型和EGA算法开发的FA混凝土智能设计系统可以高效地获得最优的制备参数并准确预测相应的性能。此外，优化后的C30和C40 FA混凝土的碳排放量分别比基准混凝土减少12.72%和17.44%。最后，实验结果验证了XGB和GB模型的预测精度和泛化能力，C30和C40 FA混凝土的相对预测误差均小于8%。

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引用次数: 0

Less than 500 lines self-contained Python finite element implementation of the phase-field method for fracture mechanics 500行以下自包含Python有限元实现的相场法断裂力学

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-11-15 Epub Date: 2025-08-25 DOI: 10.1016/j.advengsoft.2025.104013

Nathan Shauer

This paper presents a simple self-contained finite element implementation of the phase-field method for fracture mechanics. The implementations are done in Python, and they only use the standard NumPy and SciPy libraries for basic matrix operations and to solve the resulting systems of equations. The AT2 phase-field model is adopted and the additive decomposition of the energy density is employed to prevent fracture propagation under compressive stresses. The alternate minimization algorithm is adopted for solving the nonlinear system of equations. The implementation is verified using three examples: a bar under tension, a notched plate under tension, and a three-point bending test. The results display good agreement with analytical solutions and solutions from other authors. Each example is less than 500 lines long, and they are available on GitHub at https://github.com/nathanshauer/phasefield-jr-py and as supplementary data to this article. These Python scripts are intended for educational purposes and to provide a simple starting point for those interested in the phase-field method for fracture mechanics.

本文给出了断裂力学相场法的一个简单的自包含有限元实现。这些实现是用Python完成的，它们只使用标准的NumPy和SciPy库进行基本的矩阵运算和求解得到的方程组。采用AT2相场模型，采用能量密度加性分解防止压应力作用下的断裂扩展。采用交替极小化算法求解非线性方程组。通过三个实例验证了该方法的实现：受拉杆、受拉缺口板和三点弯曲试验。结果与解析解和其他作者的解吻合较好。每个示例的长度都不超过500行，可以在GitHub上（https://github.com/nathanshauer/phasefield-jr-py）获得，也可以作为本文的补充数据。这些Python脚本用于教学目的，并为那些对断裂力学的相场方法感兴趣的人提供一个简单的起点。

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引用次数: 0

CH:ALK - Rapid automatic labeling toolkit to develop training images for concrete damage segmentation models 用于开发混凝土损伤分割模型的训练图像的快速自动标记工具包

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-11-15 Epub Date: 2025-08-12 DOI: 10.1016/j.advengsoft.2025.104010

Hyojae Shin , Byunghyun Kim , Soojin Cho

With the growing demand for automated structural inspection due to the aging of civil infrastructure, deep segmentation models have been increasingly adopted with the imaging of structures. However, training the models using common supervised learning requires labeled data, and traditional manual labeling is labor-intensive, inconsistent, and time-consuming. This study introduces CH:ALK (Concrete Highlighter: Accelerated Labeling Toolkit), a rapid labeling toolkit designed to produce fast, accurate, and consistent training images for supervised learning of damage segmentation models. CH:ALK integrates automatic labeling (AL) using pre-trained CGNet (Context-Guided Network) and SAM (Segment Anything Model) to label four types of concrete damage: cracks, efflorescence, rebar exposure, and spalling. CH:ALK supports pixel-level AL that can be followed by manual correction via brush tools in an intuitive GUI. Performance validation using 80 images labeled by four users demonstrated an average time reduction of 87.97 %, accuracy of 67.07 % (mIoU), and inter-user consistency of 78.44 %, compared with traditional manual labeling (ML). Furthermore, two segmentation models, CGNet and DeepLabV3+, trained with AL data showed comparable performance to those trained with ML data. CH:ALK offers a scalable solution for developing high-quality labeled datasets for civil infrastructure inspection.

随着民用基础设施老化对自动化结构检测的需求日益增长，随着结构的成像，深度分割模型被越来越多地采用。然而，使用普通监督学习训练模型需要标记数据，而传统的手动标记是劳动密集型的，不一致的，并且耗时。本研究介绍了CH:ALK (Concrete Highlighter: Accelerated Labeling Toolkit)，这是一个快速标记工具包，旨在为损伤分割模型的监督学习生成快速、准确和一致的训练图像。CH:ALK集成了自动标记（AL），使用预训练的CGNet（上下文引导网络）和SAM（任何部分模型）来标记四种类型的混凝土损伤：裂缝、风化、钢筋暴露和剥落。CH:ALK支持像素级人工智能，可以在直观的GUI中通过画笔工具进行手动校正。使用4个用户标记的80张图像进行性能验证，与传统的手动标记（ML）相比，平均时间减少了87.97%，准确率为67.07% (mIoU)，用户间一致性为78.44%。此外，用人工智能数据训练的两种分割模型CGNet和DeepLabV3+的性能与用ML数据训练的模型相当。CH:ALK为开发用于民用基础设施检查的高质量标记数据集提供了可扩展的解决方案。

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引用次数: 0

On the effectiveness of multigrid preconditioned iterative methods for large-scale frequency response topology optimization problems 多网格预处理迭代法求解大规模频响拓扑优化问题的有效性研究

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-11-15 Epub Date: 2025-08-30 DOI: 10.1016/j.advengsoft.2025.104017

Yongxin Qu , Niels Aage , Quhao Li

Large-scale static topology optimization of mechanical structures has been successfully realized for linear problems, including giga-voxel resolution aircraft wings and suspension bridges. Wherein the multigrid preconditioned conjugate gradient method (MG-CG) plays an important role in the repetitive solution of the state equations. However, research on large-scale dynamic topology optimization, e.g., frequency response problems, is still limited. Since the coefficient matrix of the dynamic equation is no longer a positive definite symmetric matrix, yet an indefinite, non-Hermitian and complex matrix, the conjugate gradient method (CG) is no longer applicable and the standard weapon-of-choice, the geometric multigrid preconditioner is no longer guaranteed to work. It is therefore of interest to investigate which iterative methods, if any, posses excellent generality and low computational-cost. In this paper, the effectiveness of several typical preconditioned iterative methods is studied, including conjugate gradient method, biconjugate gradient stabilized method (BICGSTAB), induced dimensionality reduction (IDR), generalized minimum residual method (GMRES). A detailed comparison and analysis of iterative methods' convergence, mesh dependence, and sensitivity to stiffness distribution in dealing with indefinite problems is given first. Then, despite its known disabilities, the geometric multigrid method is applied as a preconditioner for GMRES, BICGSTAB and IDR, i.e., MG-GMRES, MG-BICGSTAB and MG-IDR, to facilitate the efficient solution of large-scale frequency response analysis. In addition, the influence of several smoothers, including damped Jacobian iteration, successive over relaxation, symmetric SOR, and incomplete LU factorization, on the convergence of geometric multigrid iterative methods is also discussed. Numerical examples show that MG-BICGSTAB deals with low-frequency problems well, but for the whole frequency range, MG-GMRES with ILU smoother converges quickly and steadily, even if the model is extremely large. Furthermore, the effectiveness of the proposed procedure is further verified in dynamic topology optimization with up to 2.8 million degrees of freedom using a standard desktop computer.

机械结构的大规模静态拓扑优化已经成功地实现了线性问题，包括千兆体素分辨率的飞机机翼和悬索桥。其中，多网格预条件共轭梯度法（MG-CG）在状态方程的重复求解中起着重要作用。然而，对频率响应等大规模动态拓扑优化问题的研究仍然有限。由于动力方程的系数矩阵不再是正定对称矩阵，而是不定的非厄米复矩阵，因此共轭梯度法（CG）不再适用，几何多网格预调节器也不再保证起作用。因此，研究哪些迭代方法（如果有的话）具有良好的通用性和较低的计算成本是有意义的。本文研究了几种典型的预条件迭代方法的有效性，包括共轭梯度法、双共轭梯度稳定法、诱导降维法、广义最小残差法。首先对求解不确定问题的迭代方法的收敛性、网格依赖性和对刚度分布的敏感性进行了详细的比较和分析。然后，尽管存在已知的缺陷，将几何多重网格方法作为GMRES、BICGSTAB和IDR的前置条件，即MG-GMRES、MG-BICGSTAB和MG-IDR，以促进大规模频响分析的有效求解。此外，还讨论了阻尼雅可比迭代、逐次过松弛、对称SOR和不完全LU分解等平滑项对几何多网格迭代方法收敛性的影响。数值算例表明，MG-BICGSTAB可以很好地处理低频问题，但对于整个频率范围，即使模型非常大，具有ILU平滑的MG-GMRES也可以快速稳定地收敛。此外，在标准台式计算机上，进一步验证了该方法在高达280万自由度的动态拓扑优化中的有效性。

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引用次数: 0

A machine learning-based inverse analysis procedure for concrete softening law prediction using non-experimental datasets 基于机器学习的非实验数据集混凝土软化规律预测逆分析方法

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software

Pub Date : 2025-11-15 Epub Date: 2025-08-27 DOI: 10.1016/j.advengsoft.2025.104016

H. Rodrigo Amezcua , A. Gustavo Ayala , Carlos E. González

This paper studies the mechanical behaviour of concrete as one of the most widely used quasi-brittle construction materials emphasizing on the importance of knowing its mechanical parameters and their evolution during the inelastic stage, i.e., the softening law. The softening curve, which describes the response of the material under damage or cracking, is critical for predicting the behaviour of concrete structures subjected to extreme loads. Experimental tests are commonly employed to obtain this information either directly or indirectly. Some of the indirect methods are based on inverse analysis and/or artificial intelligence techniques, both of which capable of predicting the mechanical parameters of concrete from the experimental results of one test, e.g., a notched beam subjected to vertical loads. However, an important drawback of these procedures is that they require a large dataset constructed from data gathered in multiple experiments in order to be developed. Consequently, most existing methods are tailored to specific types of experiments and even limited to certain specimen dimensions. Additionally, these procedures primarily focus on predicting mechanical parameters rather than determining the softening law. To address these limitations, this paper proposes a machine learning-based algorithm for the inverse analysis of an experimental test capable of predicting both the softening law and the mechanical parameters of concrete. By generating a non-experimental dataset through the Sequentially Linear Analysis (SLA) procedure, the proposed algorithm can be applied to other experimental setups suitable for analysis with SLA. The results of the application example demonstrate the effectiveness of the proposed approach.

混凝土作为一种应用最广泛的准脆性建筑材料，本文对其力学性能进行了研究，强调了解其非弹性阶段的力学参数及其演变，即软化规律的重要性。软化曲线描述了材料在损伤或开裂下的反应，对于预测混凝土结构在极端载荷下的行为至关重要。通常采用实验测试来直接或间接地获得这一信息。一些间接方法基于逆分析和/或人工智能技术，这两种方法都能够从一次测试的实验结果中预测混凝土的力学参数，例如，承受垂直载荷的缺口梁。然而，这些程序的一个重要缺点是，它们需要一个从多个实验中收集的数据构建的大型数据集才能进行开发。因此，大多数现有的方法都是针对特定类型的实验量身定制的，甚至仅限于某些样品尺寸。此外，这些程序主要侧重于预测力学参数，而不是确定软化规律。为了解决这些限制，本文提出了一种基于机器学习的算法，用于能够预测混凝土软化规律和力学参数的实验测试的逆分析。通过顺序线性分析（SLA）过程生成非实验数据集，该算法可以应用于其他适合使用SLA进行分析的实验设置。应用实例的结果表明了该方法的有效性。

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引用次数: 0