Gordan Meisam, Chao Ong Zhi, Sabbagh-Yazdi Saeed-Reza, Wee Lai Khin, Ghaedi Khaled, Ismail Zubaidah
Department of Civil Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Civil Engineering, K. N. TOOSI University of Technology, Tehran, Iran.
Front Psychol. 2022 Mar 24;13:846610. doi: 10.3389/fpsyg.2022.846610. eCollection 2022.
Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring (SHM)-based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence (AI)-based algorithms, i.e., Artificial Neural Network (ANN), hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN.
目视检查通常用于基础设施的状况评估。然而，它们基于人类判断，对数据的解读可能与获取的结果不同。在心理学中，这种差异被称为认知偏差，它直接影响基于结构健康监测（SHM）的决策。此外，桥梁状况状态与安全性之间的混淆是桥梁监测中认知偏差的另一个例子。因此，基于计算机的综合方法作为强大工具可在SHM系统中得到显著应用。本文通过实施一种基础设施监测方法，探讨了先进计算智能的使用与SHM解决方案开发之间的关系。基于人工智能（AI）的算法，即人工神经网络（ANN）、基于混合ANN的帝国竞争算法和基于混合ANN的遗传算法，被开发用于使用实验室规模的复合桥面板结构进行损伤评估。基于结果的比较，所采用的进化算法通过增强ANN的学习过程，可以改善预先开发网络的预测误差。
