Safety Risk Assessment of Overturning Construction of Towering Structure Based on Cloud Matter–Element Coupled Model

Rapid urbanization has led to a surge in the number of towering structures, and overturning is widely used because it can better accommodate the construction of shaped structures such as variable sections. The complexity of the construction process makes the construction risk have certain randomness, so this paper proposes a cloud-based coupled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning construction of towering structures. In the pretended model, the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element, and calculate the cloud correlation of the risk assessment metrics through the correlation algorithm of the cloud model to build the computational model. Meanwhile, the improved hierarchical analysis method based on the cloud model is used to determine the weight of the index. The comprehensive evaluation scores of the evaluation event are then obtained through the weighted average method, and the safety risk level is determined accordingly. Through empirical analysis, (1) the improved hierarchical analysis method based on the cloud model can incorporate the data of multiple decision-makers into the calculation formula to determine the weights, which makes the assessment results more credible; (2) the evaluation results of the cloud-based matter-element coupled model method are basically consistent with those of the other two commonly used methods, and the confidence factor is less than 0.05, indicating that the cloud-based physical element coupled model method is reasonable and practical for towering structure overturning; (3) the cloud-based coupled element model method, which confirms the reliability of risk level by performing Spearman correlation on comprehensive assessment scores, can provide more comprehensive information of instances compared with other methods, and more comprehensively reflects the fuzzy uncertainty relationship between assessment indexes, which makes the assessment results more realistic, scientific and reliable.