Jian Ge¹, Qin Qin^1,*, Jinhua Jiang¹, Zhiwei Shen², Zimei Tu¹, Yahui Zhang¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3211-3226, 2025, DOI:10.32604/cmc.2025.059389 - 16 April 2025

Abstract Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality. Deep learning algorithms show promise in this field, but challenges remain, especially in detecting small-scale defects under harsh industrial conditions with multimodal data. This paper proposes an enhanced version of You Only Look Once (YOLO)v8 for improved defect detection in automotive sealing rings. We introduce the Multi-scale Adaptive Feature Extraction (MAFE) module, which integrates Deformable Convolutional Network (DCN) and Space-to-Depth (SPD) operations. This module effectively captures long-range dependencies, enhances spatial aggregation, and minimizes information loss of small objects during feature More >

Chao Luo¹, Zhengsong Xiang^1,2, Yin Zhou^1,*, Dingsong Qin³, Tianlei Cheng⁴, Qizhi Tang¹

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 683-703, 2025, DOI:10.32604/sdhm.2025.061549 - 03 April 2025

Abstract Failure tests were conducted on two concrete-filled steel tubular (CFST) truss arch bridges with a span of approximately 12 m to investigate the influence of initial geometric defects on the in-plane bearing capacity of CFST truss arch bridges. The effects of antisymmetric defect on the ultimate bearing capacity, failure mode, structural response, and steel–concrete confinement effect of CFST truss arch bridges under quarter-point loading were analyzed. On this basis, numerical simulations were conducted to investigate the in-plane bearing capacity of CFST truss arch bridges further under different scenarios. The initial defect form of the arch… More >

Yaojin Fan, Huaqing Dong^*, Zixuan Zong, Tingting Long, Qianglin Huang, Guoqiang Huang

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 731-769, 2025, DOI:10.32604/sdhm.2025.061056 - 03 April 2025

Abstract This study presents a comprehensive investigation of residual strength in corroded pipelines within the Yichang-Qianjiang section of the Sichuan-East Gas Pipeline, integrating advanced numerical simulation with experimental validation. The research methodology incorporates three distinct parameter grouping approaches: a random group based on statistical analysis of 389 actual corrosion defects detected during 2023 MFL inspection, a deviation group representing historically documented failure scenarios, and a structural group examining systematic parameter variations. Using ABAQUS finite element software, we developed a dynamic implicit analysis model incorporating geometric nonlinearity and validated it through 1:12.7 scaled model testing, achieving prediction… More >

Yueshun Chen^1,2,*, Yupeng Zhou¹, Cao Yin³

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 575-592, 2025, DOI:10.32604/sdhm.2024.057005 - 03 April 2025

Abstract Computed tomography (CT) can inspect the internal structure of concrete with high resolution, but improving the accuracy of measurements remains a key challenge due to the reliance on complex image processing and significant manual intervention. This study aims to optimize CT scanning parameters to enhance the accuracy of measuring crack widths and rebar volumes in reinforced concrete. Nine sets of specimens, each with varying rebar diameters and concrete cover thicknesses, were scanned before and after corrosion using an Optima CT scanner, followed by three-dimensional reconstructions using Avizo software. The effects of threshold values and “Erosion” More >

Yi-Ze Wu, Yi-Cheng Huang^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 97-114, 2025, DOI:10.32604/cmc.2025.063441 - 26 March 2025

Abstract Precision steel balls are critical components in precision bearings. Surface defects on the steel balls will significantly reduce their useful life and cause linear or rotational transmission errors. Human visual inspection of precision steel balls demands significant labor work. Besides, human inspection cannot maintain consistent quality assurance. To address these limitations and reduce inspection time, a convolutional neural network (CNN) based optical inspection system has been developed that automatically detects steel ball defects using a novel designated vertical mechanism. During image detection processing, two key challenges were addressed and resolved. They are the reflection caused… More >

Farah Mohammad^1,2,*, Saad Al-Ahmadi³, Jalal Al-Muhtadi^1,3

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1327-1345, 2025, DOI:10.32604/cmc.2025.060228 - 26 March 2025

Abstract Software defect prediction is a critical component in maintaining software quality, enabling early identification and resolution of issues that could lead to system failures and significant financial losses. With the increasing reliance on user-generated content, social media reviews have emerged as a valuable source of real-time feedback, offering insights into potential software defects that traditional testing methods may overlook. However, existing models face challenges like handling imbalanced data, high computational complexity, and insufficient integration of contextual information from these reviews. To overcome these limitations, this paper introduces the SESDP (Sentiment Analysis-Based Early Software Defect Prediction)… More >

Xin Wen^*, Xiao Zheng, Yu He

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025

Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >

Qi Fei^1,2,*, Haojun Hu³, Guisheng Yin¹, Zhian Sun²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3251-3279, 2025, DOI:10.32604/cmc.2024.058931 - 17 February 2025

Abstract Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy… More >

Haitao He¹, Bingjian Yan¹, Ke Xu^1,*, Lu Yu^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2077-2108, 2025, DOI:10.32604/cmc.2024.058779 - 17 February 2025

Abstract Software defect prediction aims to use measurement data of code and historical defects to predict potential problems, optimize testing resources and defect management. However, current methods face challenges: (1) Coarse-grained file level detection cannot accurately locate specific defects. (2) Fine-grained line-level defect prediction methods rely solely on local information of a single line of code, failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line, making it difficult to capture the interaction between global and local information. Therefore, this paper proposes a… More >

Fengyu Yang^1,2,*, Fa Zhong², Xiaohui Wei¹, Guangdong Zeng²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3563-3590, 2025, DOI:10.32604/cmc.2024.057697 - 17 February 2025

Abstract The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph… More >