Hongxing Li¹, Xizhong Xu^2,*, Liang Wang¹, Jiabo Hu², Zhice Zhao¹

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081450 - 24 August 2026

Abstract Differential settlement control in highway widening projects on soft soil remains a major challenge. This study investigates the mechanisms of differential settlement in widened embankments and develops an intelligent prediction framework by integrating high-fidelity numerical simulations with physics-constrained deep learning. First, comprehensive numerical simulations were performed using a Hardening Soil (HS) model considering structural degradation in PLAXIS 2D. This work revealed the redistribution of additional stress under widening loads and elucidated the evolution mechanisms of plastic zone development and interface shear behavior at the junction of new and existing subgrades. A reasonable step width range… More >

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.079495 - 24 August 2026

Abstract The goal of this paper is to improve the monitoring of civil structures when we pair unmanned aerial vehicles (UAVs) technology with the current proposed algorithm, particularly to identify cracks in concrete structures. Typically, the current UAV methods are more about creating state maps of these structures, but they struggle with the impact of the drone’s movement on crack detection accuracy. This presents challenges for using intelligent systems for concrete crack detection. The current approach combines advanced technologies with a network of high-definition cameras mounted on inspection UAV systems and distributed in different parts of… More >

Caiping Huang^*, Yulong Mei, Wangyuan Tian, Zihang Yu

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.073282 - 24 August 2026

Abstract Bridge damage detection is critical to bridge maintenance practices. However, traditional inspection methods are plagued by high labour intensity and low operational efficiency. To enhance the intelligence, objectivity, and efficiency of bridge damage detection, this study proposes an automated approach for the identification and quantitative measurement of concrete defects. Specifically, this method adopts the Visual Geometry Group (VGG) network as the backbone of the U-Net architecture to perform semantic segmentation on images containing typical concrete defects, including spalling, cracks, and exposed reinforcement bars. Subsequently, mathematical morphology algorithms are employed to optimise the segmented images, thereby… More >

Bayan Alabdullah¹, Muhammad Waqas Ahmed², Mohammad Shorfuzzaman^3,*, Jasem Almotiri⁴, Mohammed Alonazi⁵, Ahmad Jalal^6,7,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086985 - 13 August 2026

Abstract Multimodal data fusion and deep learning have opened new frontiers in the analysis of complex visual data acquired from heterogeneous sensing systems. Flood inundation mapping represents one of the most demanding applications in this domain, requiring robust interpretation of complementary but conflicting image modalities under severe real-world constraints. This paper presents CAG-Transformer, a novel multimodal AI architecture for bi-temporal flood change detection through intelligent fusion of Sentinel-1 SAR and Sentinel-2 multispectral imagery. Three tightly integrated contributions address the core challenges of heterogeneous multimodal image analysis. A Change Attention Gate (CAG) performs adaptive channel-wise representation learning,… More >

Xiruo Chen, Qi Ouyang^*, Sihong Meng, Yuke Meng

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086341 - 13 August 2026

Abstract Visual simultaneous localization and mapping (VSLAM) is a key technology for mobile robotics, autonomous driving, and embodied intelligence, enabling self-localization, environment reconstruction, and scene understanding. Although conventional geometric methods have achieved notable success, their performance often degrades in challenging conditions, such as low-texture scenes, severe illumination changes, dynamic interference, and long-term environmental variations. Recent advances in deep learning have created new opportunities to improve VSLAM through stronger feature representations, learned priors, semantic perception, and emerging map representations. At the same time, the increasing adoption of learning-based modules has raised important questions about integration strategies, generalization,… More >

Jinlin Chen^1,2, Yiquan Wu^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085761 - 13 August 2026

Abstract Accurate waterline detection is critical for automated ship draft monitoring but remains challenging due to weak textures, low contrast, and dynamic maritime interferences. This paper presents TGNet, a task-guided framework that jointly optimizes character recognition and waterline keypoint localization. TGNet introduces a triple attention network (TAnet) with channel, spatial, and texture attention modules to enhance discriminative feature extraction. Crucially, a task-to-task guidance mechanism leverages detected draft characters to spatially constrain and crop feature maps, focusing the keypoint detection head on the most relevant waterline region. Extensive experiments on three large-scale aerial datasets show that TAnet More >

Rui Yan¹, Botao Fan², Xueqi Qu², Cuihuan Ren^1,*, Xu Zhou^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085749 - 13 August 2026

Abstract Accurate prediction of coal spontaneous combustion (CSC) temperatures is crucial for safe coal mine production. To further improve the accuracy of CSC temperature prediction and the interpretability of the model, this study proposes an interpretable Chebyshev chaotic mapping-Lévy flight-Enhanced pinhole imaging inverse learning-Adaptive weighted Osprey Optimization Algorithm optimized Bidirectional Long Short-Term Memory (CLEA-OOA-BiLSTM) framework for predicting CSC temperatures. First, we optimized the Osprey Optimization Algorithm (OOA) by incorporating the Chebyshev chaotic map, Lévy flights, enhanced pinhole imaging backpropagation, and an adaptive weighting strategy, thereby developing the CLEA-OOA algorithm. Through comparative experiments using eight benchmark test… More >

Nourah Fahad Janbi^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084270 - 13 August 2026

Abstract Automatic pain assessment from speech is an emerging approach for non-invasive and objective healthcare monitoring. However, many existing methods are evaluated on single datasets or under controlled conditions, which limits their robustness under diverse real-world conditions. This paper presents a lightweight, end-to-end deep learning framework for speech-based pain level classification that explicitly targets multi-dataset robustness assessment. The proposed approach uses log-Mel spectrograms with an EfficientNetV2B0 backbone to learn discriminative acoustic features without relying on handcrafted feature engineering or multi-stage pipelines. The model is evaluated on heterogeneous datasets, including clinical recordings, controlled experimental data, and a More >

Tuan Nguyen Kim^1,*, Son Doan Trung¹, Nguyen Minh Nhut Pham²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084268 - 13 August 2026

Abstract Static Windows Portable Executable (PE) malware detection remains a significant challenge due to the growing use of packing, obfuscation, and code reuse techniques, which gradually reduce the effectiveness of signature-based and manually engineered feature approaches. Recent deep learning models that operate directly on binary code or static features have achieved encouraging results; however, most still rely on global file-level representations. Such approaches are susceptible to noise introduced by padding or obfuscation and may overlook localized malicious regions. Moreover, many multi-view methods process different feature sources independently, lacking mechanisms to enforce semantic consistency across views. This… More >

Ahtisham Waheed¹, Yunfei Yin^1,*, Abu Fatema Mohammad Abdun Noor², Md Imam Ahasan¹, Kah Ong Michael Goh^3,*, S. M. Hasan Mahmud^2,*, Umar Rashid⁴

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083456 - 13 August 2026

Abstract Robust lane detection is a fundamental perception task for autonomous driving and Advanced Driver Assistance Systems. However, it remains challenging in real-world environments due to degraded lane markings, complex road topologies, occlusions, and adverse illumination conditions. This work aims to improve lane detection robustness by explicitly modeling lane geometric properties while preserving end-to-end efficiency. We propose a direction-curvature aware lane detection framework that integrates a novel Direction-Curvature Aware (DCA) attention module into an anchor-based architecture. The DCA module enables tangent-aligned feature aggregation guided by learned direction fields and curvature-consistent attention. In addition, we introduce a More >