# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 3 Discussion
### Deep Learning Applications in Solutions
The results showed that the deep learning technique can not only be utilized in the detection of the solution concentration, but can also be applied in the solid detection of suspending liquids. Additionally, it also carries out fast identification, accurate quantity counting, and size detection for the suspended particles in mineral water in a timely manner. Although the investigations into the detection of solid content in suspending liquids are fresh and limited, [PERSON]'s results provide technical support and a methodological basis for the automatic detection of the concrete solid content of wastewater in mixing stations. Nevertheless, the rapid sedimentation of solids in the concrete wastewater requires an accurate recognition of the solid-liquid separation surface, which is also critical in realizing smart monitoring. Besides the application of the deep learning technique for particle detection in mineral water, [PERSON] et al. [27] conducted experiments of water level monitoring using image processing. A set of optical lenses were used to measure different depths of water levels, with timely image captures. Then, distortion correction, gray scaling, and binarization techniques were carried out for image recognition. The Hough transform algorithm was used to scale the horizontal line of the water level on a real-time basis. Backpropagation was repeated to compute the gradients of the objective function, which corresponded to the weights of a multi-layer module stack. Specifically, backpropagation allows the gradients to flow backward through the network, making the model learn from image data iteratively and adjust the weights accordingly to minimize the loss until the weights reach the optimum. The results showed that image processing can be used to accurately monitor the water level, which is a similar process as that for the targeted solid-liquid separation detection. Although this research focused on river water, it can be applied to the idea of detecting the slitting surface between the solid and liquid in water. Moreover, the rapid response of the recognition system facilitates its real-time application in the industry. Hence, the successful detection of solid particles in mineral water and the real-time recognition of the water level may somehow pave the way for concrete solid detection in wastewater in future experimental investigations. More specifically, wastewater detection is executed every 20 min due to the variation in the solid content, which is caused by the continuous wastewater input from industrial  
Figure 2: (**a**) Detection structure; (**b**) image recognition system. practices. In such a time period, the gradually reduced speed of the solid sedimentation in the concrete wastewater leads to the different solid-liquid separation locations. To meet the time requirement, the solid-liquid separation location at a certain level needs to be recorded to determine the in-time value of the solid content. Therefore, the timely and accurate recognition of solid-liquid separation locations via deep learning is a necessity. The above research results demonstrated that the deep learning technique can be used to recognize the solid content, especially for water level markings. They encourage us to solve practical problems that occur in the concrete mixing stations, and moreover, such a technique can also save circulated labor costs and time. In summary, deep learning algorithms, such as those used for the latest research achievements in pattern recognition and object detection, have had significant success in image recognition. Compared with traditional machine learning techniques, deep learning does not require manual feature extraction to perform multilayered training. It also provides globally optimal solutions to problems, and thus displays better classification and approximation effects. Therefore, deep learning techniques can be further introduced to construct efficient and robust structural reliability assessment models, with higher accuracy and efficiency.