Applications in engineering science最新文献_第6页

Quantification of Surface Roughness Effect on NMR T2 Relaxation Using a Novel 3D Pore Surface Roughness Characterization Workflow 使用一种新的三维孔隙表面粗糙度表征工作流程量化表面粗糙度对核磁共振T2弛豫的影响

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-12-01 Epub Date: 2025-08-27 DOI: 10.1016/j.apples.2025.100258

Yiteng Li , Xupeng He , Shouxiang Mark Ma , Hyung Kwak , Hussein Hoteit

Evaluation of pore size distributions in porous rocks using Nuclear Magnetic Resonance (NMR) T₂ relaxation time typically assumes spherical pores with smooth surfaces. This simplification leads to inaccuracies by neglecting the impact of surface roughness on NMR T₂ relaxation. Previous studies have attempted to incorporate the surface roughness effect into surface relaxivity to reduce these systematic errors in the estimation of pore size distribution, but these methods are often sample-specific, thereby limiting their broader applicability. To overcome these limitations, we propose a novel image-based surface sourghness characterization workflow and develop a correlation to correct the shortened T₂ relaxation times in rough spherical pores. Unlike previous approaches, our method decouples the geometric impact of surface roughness from surface relaxivity, preserving the fast diffusion limit and enhancing generalizability. The workflow simplifies roughness characterization by transforming each 3D volumetric pore structure into roughness profiles, deriving a dimensionless pore roughness coefficient (PRC). Random walk simulations are then employed to compute T₂ relaxation times for various pore configurations. The T₂ correction factor is defined as the ratio of the T₂ relaxation times in rough pores to those in the corresponding spherical pores of the same volume. A nonlinear mapping between PRC and T₂ correction factor is established to correct the NMR T₂ relaxation time. Numerical results demonstrate that the proposed method accurately predicts the intrinsic pore radius, making it a practical postprocessing tool for extracting representative pore sizes from NMR T₂ relaxation times while accounting for surface roughness effects.

利用核磁共振（NMR） T2弛豫时间评价多孔岩石孔隙尺寸分布时，通常假设孔隙表面为光滑球形。这种简化由于忽略了表面粗糙度对核磁共振T2弛豫的影响而导致不准确。先前的研究试图将表面粗糙度效应纳入表面弛豫，以减少估计孔径分布时的系统误差，但这些方法往往是针对样品的，从而限制了它们的广泛适用性。为了克服这些限制，我们提出了一种新的基于图像的表面粗糙度表征工作流程，并建立了一种相关性来纠正粗糙球形孔隙中缩短的T2弛豫时间。与以前的方法不同，我们的方法将表面粗糙度和表面弛豫的几何影响解耦，保持了快速扩散极限，增强了可泛化性。该工作流程通过将每个三维体积孔隙结构转换为粗糙度剖面，从而获得无因次孔隙粗糙度系数（PRC），从而简化了粗糙度表征。然后采用随机游走模拟来计算不同孔隙结构的T2松弛时间。T2修正系数定义为相同体积的粗孔中T2弛豫时间与相应的球形孔中T2弛豫时间之比。建立了PRC与T2校正因子之间的非线性映射关系来校正核磁共振T2弛豫时间。数值计算结果表明，该方法能够准确地预测本然孔隙半径，是一种实用的后处理工具，可以在考虑表面粗糙度影响的情况下，从核磁共振T2弛豫时间中提取具有代表性的孔隙大小。

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引用次数: 0

EAPEM: An edge-aware-prototype-based efficient MaskFormer for identifying rock mass structure of cantilever roadheader tunnel face EAPEM：一种基于边缘感知原型的悬臂掘进机掘进工作面岩体结构识别方法

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 10.1016/j.apples.2025.100274

Yongfa Guo , Wei Luo , Wenyun Ding , Yi He , Jinjing Yang , Yadong Xue

The rapid and accurate identification of rock mass structures in tunnel face is a fundamental prerequisite for optimizing dynamic excavation strategies and ensuring safety decision-making in cantilever roadheader construction. To address the poor performance of existing image segmentation methods in handling fuzzy boundaries, texture interference, and data scarcity, this study proposes a novel semantic segmentation framework named edge-aware-prototype-based efficient MaskFormer (EAPEM). Built upon the MaskFormer architecture, EAPEM integrates an efficient multi-scale pixel decoder, a prototype-based masked cross-attention, and an edge-aware module to achieve collaborative optimization from global semantic parsing to local boundary refinement of tunnel face structures. Experimental results demonstrate that the proposed model achieves a mean pixel accuracy of 84.56 % and a mean intersection over union (mIoU) of 75.58 % with only a small number of high-quality training images, significantly outperforming existing segmentation networks. Specifically, the model achieves 93.54 % accuracy and over 85 % boundary IoU in karst cave recognition tasks, showing its significant advantage in handling complex boundary structures. The proposed EAPEM framework offers a lightweight, efficient, and edge-aware segmentation solution for rock structure identification under complex construction scenarios.

快速准确地识别巷道围岩结构是悬臂掘进机动态开挖策略优化和安全决策保障的基本前提。针对现有图像分割方法在处理模糊边界、纹理干扰和数据稀缺性方面表现不佳的问题，本研究提出了一种新的基于边缘感知原型的高效MaskFormer （EAPEM）语义分割框架。EAPEM以MaskFormer架构为基础，集成了高效的多尺度像素解码器、基于原型的掩码交叉关注和边缘感知模块，实现了从全局语义解析到局部边界细化的隧道表面结构协同优化。实验结果表明，该模型在使用少量高质量训练图像的情况下，平均像素精度达到84.56%，平均mIoU （intersection over union）达到75.58%，显著优于现有分割网络。具体而言，该模型在溶洞识别任务中准确率达到93.54%，边界IoU超过85%，在处理复杂边界结构方面具有明显优势。提出的EAPEM框架为复杂施工场景下的岩石结构识别提供了一种轻量级、高效、边缘感知的分割解决方案。

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引用次数: 0

Enhancing geotechnical properties of Ulwe soil through waste plastic bottle fibers and nano zinc oxide stabilization 废塑料瓶纤维和纳米氧化锌稳定剂增强乌尔韦土的岩土性能

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-12-01 Epub Date: 2025-10-01 DOI: 10.1016/j.apples.2025.100266

Maheboobsab B. Nadaf , Vedprakash Maralapalle , Sharanappagouda Kadabinakatti , Seema Jagtap

Soil stabilization is a critical aspect of foundation engineering that improves subgrade soils' durability and load-bearing capacity. This study examines the effectiveness of two innovative stabilizing agents—waste plastic bottle fibers and Nano Zinc Oxide (ZnO)—in enhancing the geotechnical properties of Ulwe and Navi Mumbai soils. Waste plastic bottle fibers were added in proportions of 0.25 %, 0.5 %, and 0.75 % by weight with length 3 cm, width 1 mm, and thickness 0.1 mm, while ZnO was incorporated in proportions of 1 %, 2 %, and 3 % by weight of dry soil. Experimental tests such as the Standard Proctor, California Bearing Ratio (CBR), and Unconfined Compressive Strength (UCS) were performed to assess the soil's performance. Wet-dry and freeze-thaw cycle experiments, which mimic field conditions, were used to evaluate durability. According to the results, adding 0.5 % waste plastic bottle fibers increased CBR values by 32 %, while adding 2 % ZnO improved CBR by 25 %. Higher ZnO content, however, increased the soil's optimum moisture content (OMC) while decreasing its maximum dry density (MDD). These results emphasize the suitability of waste plastic bottle fibers and ZnO as cost-effective and environmentally friendly soil stabilizing agents, with significant improvements in CBR values.

土壤稳定是提高路基土壤耐久性和承载能力的关键环节。本研究考察了两种创新稳定剂——废弃塑料瓶纤维和纳米氧化锌（ZnO）——在增强乌尔韦和新孟买土壤岩土力学性能方面的有效性。废塑料瓶纤维按0.25%、0.5%和0.75%的重量添加，长度为3 cm，宽度为1 mm，厚度为0.1 mm，氧化锌按1%、2%和3%的重量添加在干土中。进行了标准普罗克特、加州承力比（CBR）和无侧限抗压强度（UCS）等试验测试来评估土壤的性能。采用模拟现场条件的干湿循环试验和冻融循环试验对其耐久性进行了评价。结果表明，添加0.5%的废塑料瓶纤维可使CBR值提高32%，添加2%的ZnO可使CBR值提高25%。ZnO含量的增加增加了土壤的最适含水量（OMC），降低了土壤的最大干密度（MDD）。这些结果强调了废塑料瓶纤维和氧化锌作为经济、环保的土壤稳定剂的适用性，并显著提高了CBR值。

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引用次数: 0

Integrating generative and parametric design with BIM: A literature review of challenges and research gaps in construction design 将生成式和参数化设计与BIM相结合：建筑设计中的挑战和研究空白的文献综述

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-09-01 Epub Date: 2025-07-28 DOI: 10.1016/j.apples.2025.100253

Álmos Á. Semjén , János Szép

Parametric Design (PD), Generative Design (GD), and Building Information Modelling (BIM) have emerged as transformative tools in the construction industry, offering significant potential for design optimisation, interdisciplinary collaboration, and data-driven decision making. This paper presents a comprehensive literature review to evaluate the current state of PD, GD, and BIM integration, highlighting practical applications and identifying research gaps. In addition to mapping the academic discourse, the review also highlights selected practical implementations from existing literature to illustrate how these technologies are being translated into applied workflows. Furthermore, the methodology section critically reflects on the limitations of the keyword-based search strategy and suggests future directions to mitigate potential literature gaps. While many studies demonstrate efficiency gains in early design phases, the integration of these technologies across the full building lifecycle remains limited. Key challenges include insufficient interoperability between platforms, lack of standardisation, and minimal adoption of GD-BIM combinations in construction and logistics. Furthermore, few studies address the regulatory compliance and real-world scalability of AI-assisted generative models. The review concludes that although these digital methods can accelerate innovation and sustainability, their practical implementation requires further research in construction management, code-based automation, and human-in-the-loop design workflows.

参数化设计（PD）、生成式设计（GD）和建筑信息模型（BIM）已经成为建筑行业的变革性工具，为设计优化、跨学科协作和数据驱动决策提供了巨大的潜力。本文介绍了一篇全面的文献综述，以评估PD， GD和BIM集成的现状，突出实际应用并确定研究空白。除了绘制学术论述之外，该综述还强调了从现有文献中选择的实际实现，以说明如何将这些技术转化为应用工作流程。此外，方法论部分批判性地反映了基于关键字的搜索策略的局限性，并提出了未来的方向，以减轻潜在的文献空白。虽然许多研究表明在早期设计阶段就能提高效率，但这些技术在整个建筑生命周期中的集成仍然有限。主要的挑战包括平台之间的互操作性不足，缺乏标准化，以及在建筑和物流中很少采用GD-BIM组合。此外，很少有研究涉及人工智能辅助生成模型的法规遵从性和现实世界的可扩展性。该综述得出结论，尽管这些数字方法可以加速创新和可持续性，但它们的实际实施需要在施工管理、基于代码的自动化和人在环设计工作流程方面进行进一步研究。

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引用次数: 0

Phenomenological–based strategy for the Gurson-Tvergaard-Needleman model parameters identification Gurson-Tvergaard-Needleman模型参数辨识的现象学策略

IF 2.2 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-09-01 Epub Date: 2025-06-20 DOI: 10.1016/j.apples.2025.100243

R. Bensaada , A. Imad , T. Kanit , A. EL Moumen , M. Almansba , A. Saouab

The Gurson–Tvergaard–Needleman GTN model is widely used for ductile fracture simulations. However, the selection of its parameters is often done randomly to reproduce the experimental data without considering the physical consistency of the set of parameters selected. This contribution is dedicated to the relevance of the GTN model parameters selection using a consistent method based on the ductile fracture phenomenology.

The proposed method consists of the prioritization of the void volume fraction parameters in accordance with the kinetics of the ductile fracture phenomenon, while the nucleation and fracture parameters are chosen in conformity to the strength to fracture. Experimental data obtained from 12NC6 steel is used through various tests. Axisymmetric notched and compact tension specimens are used. The interest of the use of this variety of tests lies in the variation of stress triaxiality ratio given its widely proven importance in the ductile fracture process. The strategy is finally applied to a CT (Compact-Tension) test simulation. Satisfactory result are achieved while the number of attempts to reproduce the experimental data is considerably reduced, this lead to considerable saving of computational time.

Gurson-Tvergaard-Needleman GTN模型广泛用于韧性断裂模拟。然而，其参数的选择往往是随机的，以再现实验数据，而不考虑所选参数集的物理一致性。这一贡献致力于使用基于韧性断裂现象学的一致方法选择GTN模型参数的相关性。提出的方法是根据韧性断裂现象的动力学来优选孔隙体积分数参数，而根据断裂强度来选择成核和断裂参数。实验数据来源于12NC6钢，通过各种试验得到。采用轴对称缺口和压实拉伸试样。鉴于应力三轴比在韧性断裂过程中的重要性已被广泛证实，使用各种试验的兴趣在于应力三轴比的变化。最后将该策略应用于CT （Compact-Tension）试验仿真。在得到满意结果的同时，大大减少了再现实验数据的尝试次数，从而大大节省了计算时间。

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引用次数: 0

Three-dimensional analysis of flow physics around a sphere inside a cone 圆锥体内球体周围流动物理的三维分析

IF 2.2 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-09-01 Epub Date: 2025-06-09 DOI: 10.1016/j.apples.2025.100238

Hamidreza Zarei, Seyed Reza Maadi

This study presents a comprehensive three-dimensional numerical investigation of the flow physics around a sphere positioned inside a conical geometry, focusing on the influence of varying Reynolds numbers (102 to 10,268) and the sphere's spatial configuration on flow characteristics and drag forces. Initially, the numerical methodology was validated by simulating the flow around a sphere in an unbounded domain and comparing results with established experimental data. Subsequently, simulations were conducted within a conical enclosure, where the sphere (diameter 0.3 m) was placed at various vertical distances relative to the inlet (ranging from 1.4 m to 0.6 m) and near the cone wall (maintaining a 10 cm gap). Results revealed a significant dependence of the drag coefficient on the sphere’s position and the development of the boundary layer within the cone. As the vertical distance from the inlet increased, the drag coefficient decreased, particularly at lower Reynolds numbers. When the sphere was positioned closer to the wall, the drag coefficient was notably affected by the growth of the boundary layer, leading to substantial reductions as the vertical distance increased. These findings highlight the complex interplay between viscous and inertial forces in confined flows and provide valuable insights for optimizing fluidic systems, micro-devices, and industrial applications involving particle dynamics in constricted geometries. The research underscores the importance of spatial positioning in modulating flow behavior and drag reduction strategies in non-uniform domains.

本研究对圆锥体内部球体的流动物理进行了全面的三维数值研究，重点研究了不同雷诺数（102至10,268）和球体的空间结构对流动特性和阻力的影响。首先，通过在无界区域内模拟球体绕流，并将结果与已有的实验数据进行比较，验证了数值方法的有效性。随后，在一个锥形罩内进行了模拟，其中球体（直径0.3 m）被放置在相对于入口的不同垂直距离上（范围从1.4 m到0.6 m）和靠近锥形壁（保持10 cm的间隙）。结果表明，阻力系数与球的位置和锥内边界层的发展有很大的关系。随着与进气道垂直距离的增加，阻力系数减小，尤其是在雷诺数较低时。当球靠近壁面时，阻力系数受边界层增长的影响显著，随着垂直距离的增加阻力系数显著减小。这些发现突出了受限流动中粘性和惯性力之间复杂的相互作用，并为优化流体系统、微设备和涉及受限几何形状颗粒动力学的工业应用提供了有价值的见解。该研究强调了空间定位在非均匀区域调节流动行为和减阻策略中的重要性。

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引用次数: 0

Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning 利用机器学习优化钻进参数，以最大限度地减少碳纳米管填充GFRP复合材料的分层

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-09-01 Epub Date: 2025-08-11 DOI: 10.1016/j.apples.2025.100257

Aveen K P , Ullal Vignesh Nayak , K M Pranesh Rao , Shivaramu H T , V Londhe Neelakantha , Shashikumar C M

Composites are most commonly fastened in assemblies by drilling. The current investigation examines the effect of the drilling factors on the quality of the drilled holes. The holes were drilled on epoxy resin polymer composites reinforced using glass fibers with Carbon nano tube (CNT) as fillers. Hand-layup was done to fabricate the composites. The laminated composites were produced with 0 %, 1 %, and 1.5 % of CNT fillers. Operating parameters such as spindle speeds-1000 rpm, 2000 rpm, and 3000 rpm, feed rates- 50 mm/min, 100 mm/min and 150 mm/min were used during the experiments. Torque (T) and thrust force (F) were measured using a digital drilling machine with a dynamometer. A machine learning based multi-output random forest regression model with hyper parameter tuning was used to predict the T, F, and delamination factor (F_d). The algorithm showed that the most important parameter that influenced delamination was speed (s) followed by the feed rate (f) and filler content respectively. Further, it predicted the thrust force and F_d with ±5% accuracy and T with ±10% accuracy. The best combination of speed, feed, filler which would result in a minimized F_d was arrived at with the help of a Bayesian optimization.

复合材料通常通过钻孔固定在组件中。本研究考察了钻孔因素对钻孔质量的影响。以碳纳米管（CNT）为填料，在玻璃纤维增强的环氧树脂聚合物复合材料上钻孔。手工铺层制作复合材料。分别用0%、1%和1.5%的碳纳米管填充剂制备了层合复合材料。实验采用主轴转速为1000rpm、2000rpm和3000rpm，进给速度为50mm /min、100mm /min和150mm /min等操作参数。扭矩(T)和推力(F)使用带测功机的数字钻孔机进行测量。采用基于机器学习的多输出随机森林超参数调整回归模型预测T、F和分层因子（Fd）。该算法表明，影响分层的最重要参数是速度(s)，其次是进料速度(f)和填料含量。预测推力和Fd精度为±5%，预测T精度为±10%。在贝叶斯优化的帮助下，得到了速度、进料和填料的最佳组合，这将导致Fd最小化。

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引用次数: 0

A socio-spatial analysis of commuter preferences and behavior for driving sustainable rail network development 驱动可持续轨道网络发展的通勤偏好和行为的社会空间分析

IF 2.2 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-09-01 Epub Date: 2025-05-25 DOI: 10.1016/j.apples.2025.100236

Üsame Ekici , Hediye Tuydes-Yaman , Sarbast Moslem

To support sustainable transportation network development, Türkiye made big investments in the rail sector, including High-Speed Rail (HSR) services. To better understand the rail commuter sector and its potential connection to the Eurasian corridors in the future, current trends in the Turkish rail commuter transportation sector are investigated using the nationwide conducted rail commuter survey. Spatial analysis of the trips of 7028 rail commuters showed major desire lines, which are centered around a few cities with relatively shorter intercity travel distances. Further analysis of sociodemographic information showed a higher rate of young and middle-aged travelers who were mostly male, suggesting gender inequality in the rail commuter sector. Rail commuters preferred HSR mostly for “shorter time” and “more comfort” reasons and conventional rail for “lower cost” and “more comfort". Binary logit analysis of the business trips also revealed that being male, having a higher age, and returning the same day have a positive impact on the likelihood of making a business trip.

为了支持可持续交通网络的发展， rkiye对铁路部门进行了大量投资，包括高速铁路（HSR）服务。为了更好地了解铁路通勤部门及其未来与欧亚走廊的潜在联系，我们使用全国范围内进行的铁路通勤调查来调查土耳其铁路通勤运输部门的当前趋势。对7028名铁路通勤者出行的空间分析显示，主要的愿望线集中在几个城际旅行距离相对较短的城市周围。对社会人口统计信息的进一步分析显示，以男性为主的中青年旅客比例更高，这表明铁路通勤领域存在性别不平等。铁路通勤者选择高铁主要是出于“时间更短”和“更舒适”的原因，而传统铁路是出于“成本更低”和“更舒适”的原因。对出差的二元logit分析也显示，男性、年龄较大、当天返回对出差的可能性有正向影响。

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引用次数: 0

Neural network-based metamodel for scaffolding behavior: Application for structural load analysis and safety enhancement 基于神经网络的脚手架行为元模型：在结构荷载分析和安全性增强中的应用

IF 2.2 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-09-01 Epub Date: 2025-06-21 DOI: 10.1016/j.apples.2025.100240

Mohamad Al Omari , Mojtaba Eslahi , Rani El Meouche , Amine Ammar , Laure Ducoulombier , Laurent Guillaumat

Scaffolding safety remains a critical challenge in construction, contributing significantly to site accidents and injuries. This study addresses the issue by developing a metamodel to simulate scaffolding behavior under dynamic loads, such as wind and worker activities, to prevent failure and enhance structural reliability. A finite element model (FEM) was developed to analyze 30,000 scenarios, significantly reducing the time required for structural assessments. To optimize efficiency further, a neural network was trained to accurately predict scaffolding responses, achieving an impressive R

^{2}

value of 0.9996, thus minimizing reliance on time-intensive FEM simulations. While not a full digital twin implementation, this research establishes a strong foundation for integrating such technology in the future. By demonstrating the potential of metamodeling for improving safety and efficiency, the study offers valuable insights for advancing digital solutions in construction safety and sets the stage for further exploration of digital twin systems in the industry.

脚手架的安全是施工中的一大难题，是造成现场事故和伤害的重要因素。本研究通过开发一个元模型来模拟脚手架在动态载荷（如风和工人活动）下的行为，以防止破坏并提高结构的可靠性。开发了一个有限元模型（FEM）来分析30,000种情况，大大减少了结构评估所需的时间。为了进一步优化效率，我们训练了一个神经网络来准确预测脚手架响应，R2值达到了令人印象深刻的0.9996，从而最大限度地减少了对时间密集型FEM模拟的依赖。虽然不是一个完整的数字孪生实现，但这项研究为未来集成此类技术奠定了坚实的基础。通过展示元模型在提高安全和效率方面的潜力，该研究为推进建筑安全的数字解决方案提供了有价值的见解，并为行业中进一步探索数字孪生系统奠定了基础。

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引用次数: 0

Improved YOLOv5-based multi-crack detection in concrete wall surfaces 改进的基于yolov5的混凝土墙体多裂纹检测

IF 2.2 Q2 ENGINEERING, MULTIDISCIPLINARY

Applications in engineering science

Pub Date : 2025-09-01 Epub Date: 2025-06-29 DOI: 10.1016/j.apples.2025.100247

Remya Elizabeth Philip , Diana Andrushia A , N Anand , Mervin Ealiyas Mathews , M.Z. Naser , Eva Lubloy

In engineering and infrastructure management, it is essential to ensure the safety and longevity of structures. This can be carried out by properly detecting and monitoring surface cracks. From this lens, this paper presents a novel method for identifying surface cracks using the YOLOv5 (You Only Look Once) deep learning architecture to provide real-time object detection capabilities. First, the YOLOv5 was used with predefined hyperparameters to evaluate its pre-trained knowledge in concrete cracking. Then, the YOLOv5 architecture was fine-tuned to accommodate the specific characteristics of surface cracks within concrete structural components. Finally, the model's backbone was replaced with ResNet-50, and its performance was examined. The experiments in this study involved a diverse dataset of surface crack images and aimed to compare the performance of the three approaches in terms of Precision, Recall, and mean Average Precision metrics. Our findings indicate that YOLOv5-based approaches possess good surface crack identification, with the backbone replacement approach demonstrating the potential for improved adaptability to various structural environments. By combining the capabilities of YOLOv5 and the training strategies, the approach enhances the accuracy and reliability of the surface crack detection systems, resulting in the overall safety and durability of critical infrastructure.

在工程和基础设施管理中，保证结构的安全和寿命是必不可少的。这可以通过适当的检测和监测表面裂缝来实现。从这个角度来看，本文提出了一种使用YOLOv5 （You Only Look Once）深度学习架构来识别表面裂缝的新方法，以提供实时对象检测功能。首先，将YOLOv5与预定义的超参数一起使用，以评估其在混凝土开裂中的预训练知识。然后，YOLOv5建筑进行了微调，以适应混凝土结构部件内部表面裂缝的特定特征。最后用ResNet-50替换模型的主干，并对其性能进行了检验。本研究的实验涉及不同的表面裂纹图像数据集，旨在比较三种方法在精度、召回率和平均平均精度指标方面的性能。我们的研究结果表明，基于yolov5的方法具有良好的表面裂纹识别能力，骨干替换方法显示出对各种结构环境的适应性。通过将YOLOv5的能力与训练策略相结合，该方法提高了表面裂纹检测系统的准确性和可靠性，从而提高了关键基础设施的整体安全性和耐久性。

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引用次数: 0