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Pavement rutting refers to surface depression in the wheel-path along an asphalt road which causes loss of steering control and consequently leads to serious traffic accidents. Hence, it is necessary to develop powerful methods to accurately recognize pavement rutting during road condition survey. This study presents a novel computer vision-based model to automatically identify rutting on asphalt pavement road. The model is established based on a hybridization of image processing techniques (ITPs), least squares support vector classification (LSSVC), dynamic feature selection (FS) method, and forensic-based investigation (FBI). The ITPs, including Gabor filter and discrete cosine transform were employed to implement texture computation for image data. These techniques are used to generate an initial set of extracted features describing rutting and non-rutting states. The extracted features were then refined by a wrapper-based feature selection (FS) method to determine set of highly relevant features. LSSVC models were used to learn the categorization of rutting and non-rutting based on the refined features and hyper-parameters optimized by the FBI metaheuristic. The final LSSVC prediction model with the most desired prediction accuracy can be obtained once the process of the FBI's optimization terminates. A dataset of 2000 image samples has been collected during field trip of pavement survey in Da Nang city (Vietnam) to construct and evaluate the newly developed model. The statistical results obtained from a k-fold cross-validation have demonstrated that the hybrid FBI-LSSVC-FS model can achieve the most desired rutting recognition performance with accuracy rate, precision, recall, and F1 score of 98.9%, 0.994, 0.984 and 0.989, respectively. Therefore, this paper contributes to the body of knowledge by proposing a novel AI-based prediction model to assist transportation agencies in the task of periodic asphalt pavement survey. [ABSTRACT FROM AUTHOR]

Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of "green space" and "nongreen space". The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM's hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite's spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use. [ABSTRACT FROM AUTHOR]

• Propose a novel artificial intelligence model to recognize concrete spall. • Combine metaheuristic, image processing, and machine learning approaches. • Kapur's entropy criterion is employed for image segmentation. • Image texture analysis is used as feature extractor. • Metaheusitic optimized LogitBoost ensemble is used for pattern classification. This paper presents a novel artificial intelligence model to automatically recognize concrete spall appearing on building components. The model is constructed by integrating a metaheuristic optimization algorithm, advanced image processing techniques, and a powerful machine learning-based inference model. Kapur's entropy based image segmentation, statistical measurements of image color, gray level co-occurrence matrices, and local ternary pattern are used to extract numerical features presenting concrete surfaces on spall and non-spall samples. Subsequently, a LogitBoost based ensemble framework of classification and regression tree (CART) models (denoted as LBT) is employed to construct a decision boundary capable of recognizing spall/non-spall image samples. Moreover, in order to enhance the performance of the LogitBoost based ensemble framework, forensic-based investigation (FBI) metaheuristic is utilized to determine the most suitable set of the framework's hyper-parameters including the learning rate (μ), the learning cycle (L c), the minimum number of leaves (L min), and the maximum number of splits (S max). A data set including 486 image samples has been collected from field surveys at high-rise buildings in Da Nang city (Vietnam) to train and verify the proposed FBI optimized LBT model (denoted as F-LBT). Experimental results supported by statistical tests point out that the F-LBT is a capable method for concrete spall detection with a classification accuracy rate = 88.3%, precision = 0.889, recall = 0.874, F1 score = 0.881, and negative predictive value = 0.874. Hence, the proposed hybrid approach is a promising tool to support building maintenance agencies in the task of periodic structural inspection. [ABSTRACT FROM AUTHOR]