Crane usage is pervasive on construction sites, however, it is associated with a notably high accident rate. The analyzing of crane accident risks is essential for accident prevention, control, and ensuring the safety of lifting operations. Hence, significant emphasis should be placed on understanding the interaction among various risk factors. This paper proposes a quantitative coupling method for human, machine, management, and environmental risk factors in crane accidents, leveraging Bayesian networks (BN) and the N-K model. Firstly, text mining technology and fault tree analysis are employed to analyze the causes of crane accidents and categorize the associate risk factors. Secondly, the types of risk coupling resulting from human, machine, management, and environmental risk factors are defined. Thirdly, the BN model is developed based on the analysis of crane accident risksand its N-K model. Fourthly, the parameters of the risk coupling nodes in the developed BN are determined based on the calculation results of the N-K model. Finally, for the risk coupling types with high coupling values and the first-level node and second-level node, the failure probability is analyzed through posterior probability and sensitivity analysis. The results indicate that factors related to man and management significantly impact crane accidents and warrant enhanced attention. The interplay among multiple risk factors significantly influences the probability of crane accidents, necessitating careful attention.

Urban floods research has been attracting extensive attention with the increasing threat of flood risk and environmental hazards due to global climate change and urbanization. However, there is rarely a comprehensive review of this field and it remains unclear how the research topics on urban floods have evolved. In this study, we analyzed the development of urban floods research and explored the hotspots and frontiers of this field by scientific knowledge mapping. In total, 3314 published articles from 2006 to 2021 were analyzed. The results suggest that the number of published articles in the field of urban floods generally has an upward trend year by year, and the research focus has shifted from exploring hydrological processes to adopting advanced management measures to solve urban flood problems. Moreover, urban stormwater management and low impact development in the context of climate change and urbanization have gradually become research hotspots. Future research directions based on the status and trends of the urban floods field were also discussed. This research can not only inspire other researchers and policymakers, but also demonstrates the effectiveness of scientific knowledge mapping analysis by the use of the software CiteSpace and VOSviewer.

The effectiveness and the safety of road construction depend on many factors that pose the greatest risk to system safety. The aim of this study is to conduct a comprehensive assessment for these risk factors to contribute to the safety performance of road construction. To achieve this goal, this study constructs a hierarchical safety assessment framework comprising comprehensive risk indicators according to rich work experience and a relevant literature review and then proposes a group AHP-PCA (group analytic hierarchy process-principal component analysis) to calculate the weights of relevant risk factors. The important rankings of a case study using GAHP-PCA show that professional skill, safety education, and work attitude are the most important factors, which are consistent with the experience and knowledge of road construction safety management. The results also demonstrate that judgment dispersion naturally existing in classical AHP can be effectively reduced when combined with PCA, which is the innovation point of the method given in this article.

Tower crane safety governance is an important issue related to the sustainable development of China’s construction industry. The complex collaborative relationship among stakeholders determines the efficiency of tower crane safety governance. From the perspective of social networks, this study constructs a collaborative governance structure model of tower crane safety from four dimensions, i.e., transaction, supervision, dependency, and communication, and analyzes the structural characteristics of tower crane safety collaborative governance and the mutual relationship among stakeholders. The results show that the tower crane safety governance process has a strong collaborative effect, but that collaboration in terms of supervision and communication among stakeholders is currently poor. The tower crane property owner occupies the core position, so their decisions have a great impact on tower crane safety. The power of the government is too large, and the power of supervision is too small, which affects the collaboration enthusiasm of other stakeholders, thus reducing the overall collaboration efficiency. The findings provide theoretical support for tower crane safety management in the construction industry in China. The social network perspective presented in this study can be applied to clarify relationships among stakeholders in other construction safety governance fields.

To address the problems of traditional hydrological and hydraulic methods of estimating disasters in urban flood areas, such as small scale, poor timeliness, and difficulty of obtaining data, an inversion method of estimating urban flood disaster area based on remote sensing spectroscopy is proposed. In this paper, the spatial distribution of urban flood disasters is first inverted based on large-scale multidimensional remote sensing spectral orthography. Then, spatial coupling inversion of the remote sensing spectrum-urban economy-flood disaster is performed by simulating the urban economic density through single spectral remote sensing at night. Finally, losses at the urban flood area are estimated. The results show that (1) the heavy rain in Henan Province on 20 July is centered in Zhengzhou, and the spatial distribution of urban flood disasters accords with Zipf’s law; (2) the estimated damage to the urban flood area in Henan Province is 132,256 billion yuan, and Zhengzhou has the most serious losses at 43,147 billion yuan, accounting for 32.6% of the entire province’s losses. These results are consistent with the official data (accuracy ≥ 90%, R² ≥ 0.95). This study can provide a new approach for accurately and efficiently estimating urban flood damage at a large scale.

Improving the hazard identification ability of workers is an important way to reduce safety accidents at construction sites. Although previous studies have succeeded in improving hazard identification performance, an important gap is that they consider only two factors, the worker’s safety experience and objective environmental conditions, to analyze the impact on hazard identification performance. To fill the above gap, a visual cognitive model of hazard identification was established. Sixteen field scenes were selected to represent construction sites in each environmental condition. Eye-movement data were extracted through eye-tracking experiments, and the differences between experts’ and novices’ gazes during danger recognition in these scenes were analyzed. The results indicate the following: bright construction sites can significantly improve the correct recognition rate and information processing in hazard identification; tidy construction sites can improve the search efficiency and correct recognition rate of hazard identification; safety experience can improve workers’ correct recognition rates and information processing; and reducing distractions can effectively improve the correct identification rate of hazards. Overall, optimal site brightness needs to be further studied to improve the efficiency of hazard search and reduce the distraction effect. This study provides recommendations for the direction of safety training and safety management on site.