A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model selection criterion

Identification and analysis of hoisting safety risk factors for IBS construction based on the AcciMap and cases study

Hoisting is an essential aspect of Industrial Building System (IBS) construction. Although research on hoisting safety in China has made strides to focus on "worker," "data," "task," "site," and "accident," there still needs to be more approaches based on multi-dimensional social system thinking. Therefore, the paper aims to fill this gap. We investigated 105 hoisting accidents in China and found that hoisting accidents occurred most frequently in China's southeast coastal region; truck-mounted cranes and tower cranes were the most common types of machinery involved in accidents; hoisting load off, capsizing of crane machinery, and workers falling from height are the three most common accident types; the average impact of a single hoisting accident is approximately RMB 2.43 million direct economic loss, 1.543 deaths and 0.829 injured. This study used three algorithms (Rindge regression, Lasson regression, and partial least squares regression) to explore the impact of deaths and injuries on direct economic losses. By combining Rasmussen's risk framework with the characteristics of hoisting construction, six risk domains and thirty-six safety risk factors were identified. Finally, we used AcciMap technology to construct a qualitative IBS hoisting management model, which exhaustively presents the systematic levels and propagation paths of the influencing factors by the PDCA method. The research helps academics explore strategies to improve the safety of hoisting construction in IBS. Moreover, the study outcomes can inform the policy-making process towards promoting healthy and sustainable construction development.