To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system survey.
Continuous girder bridges become increasingly popular because of the rapid development of highway throughout the world. Most of previous researches on vibration analysis of a multispan continuous bridge subject to complex traffic loading and vehicle dynamic interaction focus on the girder displacement not considering braking effects. In current literature, few studies have discussed the effects of braking on continuous girder bridges. In this study, we employ the finite element method (FEM) to investigate the dynamic response of continuous girder bridge due to three-axle vehicle. Vertical reaction forces of axles that change with time make bending vibration of girder increase significantly. The braking in the first span is able to create response in other spans. In addition, the dynamic impact factors are investigated by both FEM and experiments on a real bridge structure. The results of this study extend the current understanding of the bridge dynamic behaviors and can be used as additional references for bridge codes by practicing engineers.
This study presents a novel computer vision based approach to automatically identify rutting appeared on asphalt pavement of road. The developed model is established base on a hybridization of image processing techniques and an advanced machine learning model with support of a metaheuristic optimization engine. Gabor filter and discrete cosine transform are employed to implement context computation for image data, accordingly generate initially extracted features of rutting and non-rutting. Least Squares Support Vector Classification (LSSVC) is then used to learn categorization of rutting and non-rutting based on the extracted features. The final LSSVC prediction model is constructed via a loop of optimization process which is controlled by a novel metaheuristic optimization algorithm, called forensic-based investigation (FBI), to attain optimal model's configuration with ultimate prediction accuracy. This study further utilized a dynamic feature selection (FS) method to integrate in the searching loop to appropriately remove redundant features that provide inconsistent information leading to the compromising of model performance. A dataset of 2000 image samples has been collected during field trip of pavement survey in Da Nang city to form and verify the newly developed model. The statistical results of 20 run times using k-fold cross validation method have demonstrated the hybrid model of FBI-LSSVC-FS to achieve the most desired rutting detection performance with classification accuracy rate, precision, recall, and F1 score of 98.9%, 0.994, 0.984 and 0.989, respectively. Hence, this paper contributes to the core body of knowledge a novel AI-based prediction model to assist transportation agencies in the task of periodic asphalt pavement survey.
During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.
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