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Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.7 ) Pub Date : 2020-11-26 , DOI: 10.1177/0361198120967943
Eslam Mohammed Abdelkader 1, 2 , Osama Moselhi 1 , Mohamed Marzouk 2 , Tarek Zayed 3
Affiliation  

Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition (SVD) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network (ENN) and the invasive weed optimization (IWO) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.



中文翻译:

混合Elman神经网络和入侵杂草优化方法的桥梁缺陷识别。

现有的桥梁正在老化和恶化,引起人们对公共安全和这些宝贵资产的保护的关注。此外,管理许多桥梁的运输网络面临预算限制。这种状况需要开发一种基于计算机视觉的方法来缓解基于视觉检查的方法的缺点。在这种情况下,本研究提出了一种自动检测和识别桥梁缺陷的三层方法。在第一层,奇异值分解(SVD)通过映射图像中最主要的空间域特征来制定特征向量集。第二层包括Elman神经网络的混合(ENN)和侵入性杂草优化(IWO)算法来增强ENN的预测性能。这是通过设计变量优化机制来实现的,该机制旨在在神经网络中寻求最佳的勘探与开发权衡。第三层涉及通过利用性能预测和统计显着性测试与一组传统的机器学习和深度学习模型进行比较来进行验证。在C#.net中编写了一个计算机化平台,以方便用户实施。结果发现,该方法的性能优于其他预测模型,分别达到了总体准确性,F度量,Kappa系数,平衡准确性,Matthews相关系数和曲线下面积分别为0.955、0.955、0.914、0.965、0.937和0.904。交叉验证。

更新日期:2020-11-27
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