Fang Zhichun, Wang Xiuhong, Gao Jiaojiao, Eskandarpour Behrooz
Institute of Civil and Architectural Engineering, Tongling University, Tongling, 244061, Anhui, China.
Department of Architecture and Civil Engineering, Hebei Vocational College of Labour Relations, Shijiazhuang, 050093, Hebei, China.
Sci Rep. 2025 Jan 2;15(1):40. doi: 10.1038/s41598-024-84458-1.
There are numerous reasons for concrete buildings cracks, like stress loads, material flaws, and environmental impacts. It is important to find and investigate the concrete cracks during analyzing the safety and structural soundness of buildings, bridges, and other infrastructure. However, there are many models available for concrete crack detection, an efficient approach is needed because the existing methods often have flaws like overfitting, high computational complexity, and noise sensitivity, which can lead to accurate crack detection and classification. This paper proposes an enhanced version of the fish migration optimization (IFMO) method combined with an optimized echo state network (ESN) model for concrete fracture detection using the combination form is established for improving the detection accuracy of the model by optimal arrangement of the ESN. The suggested ESN/IFMO model was tested on the SDNET2018 dataset, which comprises concrete photos with diverse fracture patterns, and the results were compared to several other state-of-the-art approaches. The suggested ESN/IFMO model shows potential as a more effective solution for concrete crack identification, increasing accuracy by 3.75-8.19% over current models such as DL, DINN, AlexNet, CNN, and LSTM, as well as increasing F1 score by 5.14-12.55%.
混凝土建筑出现裂缝的原因众多，如应力荷载、材料缺陷和环境影响等。在分析建筑物、桥梁及其他基础设施的安全性和结构完整性时，找出并研究混凝土裂缝至关重要。然而，虽然有许多用于混凝土裂缝检测的模型，但由于现有方法往往存在诸如过拟合、高计算复杂度和噪声敏感性等缺陷，可能导致无法准确进行裂缝检测和分类，因此需要一种高效的方法。本文提出了一种改进的鱼群迁移优化（IFMO）方法，并结合优化的回声状态网络（ESN）模型用于混凝土裂缝检测，通过对ESN进行优化布置，以组合形式建立模型来提高检测精度。所提出的ESN/IFMO模型在SDNET2018数据集上进行了测试，该数据集包含具有不同裂缝模式的混凝土照片，并将结果与其他几种先进方法进行了比较。所提出的ESN/IFMO模型显示出作为混凝土裂缝识别更有效解决方案的潜力，与当前的DL、DINN、AlexNet、CNN和LSTM等模型相比，准确率提高了3.75 - 8.19%，F1分数提高了5.14 - 12.55%。
