Alazwari Sana, AlEisa Hussah Nasser, Rizwanullah Mohammed, Marzouk Radwa
Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Sci Rep. 2025 May 13;15(1):16621. doi: 10.1038/s41598-025-99849-1.
Visually impaired individuals face daily challenges in social engagement and routine activities due to limited access to real-time environmental information. Damage detection is a common approach in infrastructure that combines steel and concrete reinforcement to achieve optimal durability and structural strength. These bridges, designed to withstand diverse loads such as seismic forces, traffic weight, and environmental factors, are significant for maintaining structural integrity. Damage detection comprises applying advanced structural health monitoring methods to identify and assess potential deterioration or damage in concrete bridge components. Machine learning (ML) models, pattern detection, and statistical analysis are extensively adopted to identify subtle changes and process sensor information in structural response that might indicate corrosion, cracks, or other structural problems. Earlier detection and continuous monitoring of damage enable prompt intervention, ensuring longevity and safety while reducing the need for extensive repairs or the risk of unexpected failures. This study proposes an Automated Damage Detection using a Modified Seagull Optimizer with Ensemble Learning (ADD-MSGOEL) method for visually impaired people. The ADD-MSGOEL method is designed to enhance the social life and daily functioning of visually impaired people by accurately detecting damage and potential hazards in their surroundings. Initially, the ADD-MSGOEL method utilizes contrast enhancement (CLAHE) to enhance the image quality. Next, the features are extracted using the Dilated Convolution Block Attention Module with EfficientNet (DCBAM-EfficientNet) module, which derives the intrinsic and complex features. Moreover, the MSGO model is employed to choose the optimal parameter for the DCBAM-EfficientNet module. At last, an ensemble of three models, namely long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and sparse autoencoder (SAE) models, are implemented for the classification and detection of the damages. To demonstrate the effectiveness of the ADD-MSGOEL technique, a series of experiments were conducted using the CODEBRIM dataset. The experimental validation of the ADD-MSGOEL technique portrayed a superior accuracy value of 97.59% over existing models.
由于获取实时环境信息的机会有限，视障人士在社交参与和日常活动中面临着诸多日常挑战。损伤检测是基础设施中的一种常见方法，它将钢材和混凝土加固相结合，以实现最佳耐久性和结构强度。这些桥梁旨在承受地震力、交通重量和环境因素等各种荷载，对于维持结构完整性至关重要。损伤检测包括应用先进的结构健康监测方法，以识别和评估混凝土桥梁部件中潜在的劣化或损伤。机器学习（ML）模型、模式检测和统计分析被广泛用于识别结构响应中的细微变化并处理可能表明腐蚀、裂缝或其他结构问题的传感器信息。早期检测和持续监测损伤能够实现及时干预，确保桥梁的使用寿命和安全性，同时减少大规模维修的需求或意外故障的风险。本研究提出了一种使用改进海鸥优化器与集成学习的自动损伤检测（ADD-MSGOEL）方法，用于视障人士。ADD-MSGOEL方法旨在通过准确检测视障人士周围环境中的损伤和潜在危险，来改善他们的社交生活和日常功能。首先，ADD-MSGOEL方法利用对比度增强（CLAHE）来提高图像质量。接下来，使用带有高效网络（DCBAM-EfficientNet）模块的扩张卷积块注意力模块提取特征，该模块可导出内在和复杂的特征。此外，采用MSGO模型为DCBAM-EfficientNet模块选择最优参数。最后，实现了长短期记忆（LSTM）、双向门控循环单元（BiGRU）和稀疏自动编码器（SAE）三种模型的集成，用于损伤的分类和检测。为了证明ADD-MSGOEL技术的有效性，使用CODEBRIM数据集进行了一系列实验。ADD-MSGOEL技术的实验验证表明，其准确率比现有模型高出97.59%。
