Krishnan S Siva Rama, Karuppan M K Nalla, Khadidos Adil O, Khadidos Alaa O, Selvarajan Shitharth, Tandon Saarthak, Balusamy Balamurugan
School of Computer Science and Information Systems, Vellore Institute of Technology, Katpadi, Vellore, 632014, Tamilnadu, India.
Balaji Institute Modern Management, Sri Balaji University, Pune, 411033, India.
Sci Rep. 2025 Jan 16;15(1):2125. doi: 10.1038/s41598-025-85983-3.
Life-time of the buildings is generally challenged by the act of nature. In-spite of the fact that the constructions provide minimum guarantee on quality and durability, certain mismatch in the composition of the materials, stress on the building, and chemical or physical imbalance of the materials, lead to surface crack. Cracks are also generated due to the shuffle of climatic conditions, which leads to the contraction and expansion of the building surfaces, and other damages. The guarantee on building safety and serviceability depends on how these buildings are successfully assessed and maintained. The development of Artificial Intelligence (AI) techniques, provide favourable solutions in-order to handle, manage and solve building cracks, through analysis using deep image neural network models, that perform classification of the building with crack images. As a result, a critical challenge for many civil engineering applications is the precise, quick, and automated identification of cracks on structural surfaces is addressed with the solutions provided by the deep image neural networks. In this research, we tackle the research gap and data scarcity by developing and curating a novel deep learning image processing for detecting cracks in brickwork. We also train and validate several deep learning models to classify brickwork images as either cracked or normal. The dataset of the proposed work contains 24,000 images which are classified through binary classes. These classes are generated for crack and non-crack images. The various parameters such as Batch size, Pooling, Activation functions Learning-rate, Kernel-Size, Normalization, and Optimizers are used for the evaluation of the model. The proposed work performs a comparative analysis of four deep image models such as Inception V3, VGG-16, RESNET-50 VGG-19, Inception ResNetV2 and CNN-RES MLP. With the analysis of all these models, the Inception V3 provides the best of all with the accuracy value of 99.98%. The InceptionV3 tops the Precision value of 99.99% and RESNET-50 tops the Recall value of 99.98%. The IncpetionV2 provided the best of the Region of Convergence value of 0.9999 which is the best among all the models for reliable and stable performance.
建筑物的使用寿命通常受到自然因素的挑战。尽管建筑在质量和耐久性方面提供了最低保障，但材料组成中的某些不匹配、建筑物上的应力以及材料的化学或物理失衡，都会导致表面裂缝。裂缝也会由于气候条件的变化而产生，这会导致建筑物表面的收缩和膨胀以及其他损坏。建筑物的安全和适用性保障取决于对这些建筑物的成功评估和维护。人工智能（AI）技术的发展，通过使用深度图像神经网络模型进行分析，为处理、管理和解决建筑物裂缝提供了有利的解决方案，该模型可以对带有裂缝图像的建筑物进行分类。因此，深度图像神经网络提供的解决方案解决了许多土木工程应用中的一个关键挑战，即精确、快速和自动地识别结构表面的裂缝。在这项研究中，我们通过开发和策划一种用于检测砖砌体裂缝的新型深度学习图像处理方法来解决研究差距和数据稀缺问题。我们还训练和验证了几个深度学习模型，以将砖砌体图像分类为有裂缝或正常。所提出工作的数据集包含24000张图像，这些图像通过二元类别进行分类。这些类别是针对裂缝和无裂缝图像生成的。诸如批量大小、池化、激活函数、学习率、内核大小、归一化和优化器等各种参数用于模型评估。所提出的工作对四种深度图像模型进行了比较分析，如Inception V3、VGG - 16、RESNET - 50、VGG - 19、Inception ResNetV2和CNN - RES MLP。通过对所有这些模型的分析，Inception V3以99.98%的准确率在所有模型中表现最佳。InceptionV3的精确率值最高，为99.99%，RESNET - 50的召回率值最高，为99.98%。InceptionV2提供了最佳的收敛区域值0.9999，在所有模型中性能最可靠、最稳定。
