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Despite advancements in computer vision technology for construction site safety, the identification and evaluation of potential safety risks stemming from on-site hazardous objects and their spatial interactions is under-researched. Moreover, the subsequent visualization of risks, essential for effective safety management, remains insufficiently explored. This paper presents a model combining computer vision and the TOPSIS method to automate safety risk quantification and visualization during the construction process. Uniquely, it enhances risk predictions by dynamically monitoring the quantity and distance changes of hazard sources and their risk-related objects in real-time, beyond pre-construction risk identification and predictions. Experimental validation shows the model's effectiveness in risk quantification and visualization, with a high consistency ratio of 95% compared to expert manual assessments. This model lays the groundwork for more precise risk calculation and evaluation, fostering improved safety management decisions. • The use of computer vision technology improves traditional safety risk management. • Proposed model automates safety risk quantification and visualization for site images. • Model assesses impact of spatial position and interactions of hazardous sources on safety. • Risk heatmap generation enhances risk visualization. • Experimental results show highly consistent risk assessments compared to manual assessments. [ABSTRACT FROM AUTHOR]

This paper presents alternative solutions for classifying concrete spall severity based on computer vision approaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center symmetric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern (ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification directly without the need for texture descriptors. Experimental results with a dataset containing real-world concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a classification accuracy rate of roughly 99%. • Propose a computer vision method for classifying concrete spall severity. • Extreme Gradient Boosting Machine and deep neural networks are employed. • Use local binary pattern variants as image texture descriptors. • The boosting machine is finetuned by the Aquila optimizer. • The proposed method achieves a high classification accuracy rate = 0.99. [ABSTRACT FROM AUTHOR]