# A Hybrid Learning Framework for Enhancing Bridge Damage Prediction
###### Abstract
[16] proposed the utilization of the DCNN framework to classify fissures across three publicly accessible datasets: CCIC, BCD, and SDNET. The proposed methodology facilitates the transfer of three distinct forms of knowledge derived from prior scholarly achievements: sample knowledge, parameter knowledge, and model knowledge. The VGG network was augmented with additional fully connected layers, thereby establishing a novel learning framework. The researchers substantiated the credibility and efficacy of the proposed methodology. [PERSON] et al. [17] employed a histogram of oriented gradients (HOG) in conjunction with uniform local binary patterns (ULBPs) to derive features from a dataset comprising over 3000 images, both uncracked and cracked, which encompassed various crack patterns and representations of concrete surfaces. Nonlinear dimensionality reduction techniques were executed through kernel principal component analysis (KPCA), and three machine learning classifiers were utilized to facilitate the classification process. The findings of the experiments indicate that the classification approach predicated on the SVM model and the feature-level fusion of HOG and ULBP features subsequent to the application of KPCA yielded optimal results, as the proposed classification framework accomplished an accuracy rate of 99.26%. [PERSON], with another team [18], introduced a publicly accessible benchmark-annotated image dataset comprising over 6900 images depicting cracked and non-cracked concrete bridges and culverts. The dataset presented encompasses a variety of challenging surface conditions and comprises concrete cracksexhibiting diverse sizes and patterns. The authors analyzed the proposed dataset employing three cutting-edge DCNNs, namely VGG16, VGG19, and InceptionV3, utilizing a transfer learning approach. The models above were employed to differentiate between cracked and non-cracked images, achieving a maximum testing accuracy of 95.89%. The outcomes of the experiments illustrate the promising applicability of this dataset for training deep learning networks aimed at the recognition of concrete cracks in bridge structures. [PERSON] et al. [19] created a convolutional neural network (CNN)-based model for crack detection that works completely from the beginning to the end. It does this by using atrous convolution, the atrous spatial pyramid pooling (ASPP) module, and depthwise separable convolution. An implementation of atrous convolution makes it easier to obtain a wider receptive field while maintaining the input data's resolution. When the ASPP module is used, the network can gather contextual data at different levels, and the depthwise separable convolution makes the computations easier. The model proposed by the authors achieved an impressive detection accuracy of 96.37% for the BCD dataset without the necessity of pre-training. In this study, we aim to build a classification model that minimizes the prediction errors. At first, we built a model based on the LBP-BoVW, Apriori, and MobileNet algorithms. We achieved excellent accuracy with this model, ranging from 98.27% to 100%, and very few misclassified instances with an error rate between 1.73% and 0% for all datasets. The contributions of our work are summarized as follows:  
1. Reliable hybrid supervised and unsupervised learning based on LBP-BoVW features for minimizing error rate;
2. A new algorithm for reliable learning on image datasets;
3. Employing the Apriori algorithm for selecting robust features and dimensionality reduction.