Search

Construction waste is a serious problem that should be addressed to protect environment and save resources, some of which have a high recovery value. To efficiently recover construction waste, an online classification system is developed using an industrial near-infrared hyperspectral camera. This system uses the industrial camera to capture a region of interest and a hyperspectral camera to obtain the spectral information about objects corresponding to the region of interest. The spectral information is then used to build classification models based on extreme learning machine and resemblance discriminant analysis. To further improve this system, an online particle swarm optimization extreme learning machine is developed. The results indicate that if a near-infrared hyperspectral camera is used in conjunction with an industrial camera, construction waste can be efficiently classified. Therefore, extreme learning machine and resemblance discriminant analysis can be used to classify construction waste. Particle swarm optimization can be used to further enhance the proposed system.

A large proportion of construction waste has a high recovery value, and some of the existing recycling-classification methods rely mainly on physical properties for vibration screening. In order to effectively recover construction waste, an industrial near-infrared hyperspectral camera is proposed in this paper to distinguish the spectral characteristics of the objects. The testing results were verified using an extreme learning machine, an adaptive-learning multilayer perceptron, and a one-dimensional convolutional neural network classification model. By establishing several different models to classify and identify the same kinds of experimental materials, the experimental results not only output the correct recognition rate, but also use the recognition efficiency and stability as indices for comprehensive evaluation. The results show that different classification models have different efficiencies and levels of correctness. Under different analytical conditions, such as when using data from different bands, it is very important to select the appropriate classification model to classify construction waste. [ABSTRACT FROM AUTHOR]

Identification and classification of construction waste are two important aspects of construction waste recycling. This study proposes a method based on image processing to identify gray brick and concrete, the most difficult types to identify that have the highest percentage of construction waste. By combining color features with texture features, machine learning algorithms are used for training and recognition. We paid great attention to the comparison of the performance of different color models, which includes Red, Green, Blue (RGB), Hue, Saturation, Value (HSV), and Lab. We found that gray histograms and color moments were suitable as color features of concrete and gray bricks. Meanwhile, the eigenvalues of the gray-level co-occurrence matrix (GLCM) were also discussed. Contrast, angular second moment, inverse different moment, and correlation in the five eigenvalues of GLCM were selected as texture features via experiments. We used three machine learning algorithms to train the extracted data. The results showed that the extreme learning machine had the lowest accuracy (96.25%), whereas the support vector machine and back propagation algorithm had higher accuracy of 96.875% and 98.125%, respectively. The online testing had the accuracy of 95%, indicating that the selected features are effective, and the accuracy can meet the engineering needs. [ABSTRACT FROM AUTHOR]