Construction is considered to be one of the most dangerous industries due to its wide variety of accidents and high risk. Although people have been using various means to improve safety levels, the frequent occurrence of accidents still plagues the development of this industry. Accurately grasping the characteristics and analyzing the complex relationships between the risks of different accident types are the keys to a comprehensive understanding and promotion of construction safety. Simultaneously, we need to note that the level and frequency of accidents affects the risk characteristics, but previous studies have not paid sufficient attention to these features. Using complex network theory, based on the risk factors and risk triggering relationship extracted from construction accidents and considering the accident level and the frequency of occurrences as their weights, several weighted risk networks are generated according to different accident types. Then, several network analysis metrics are introduced to reveal the characteristics of the risk network and identify the key risk factors. The analytical framework helps to pose targeted suggestions to reduce the occurrence of frequent major accidents.
The automated valuation of benchmark land price plays an essential role in regulating land demand in Chinese real-estate market as the big data are currently accumulated rapidly. However, this problem becomes highly challenging due to the multidimension, large volume, and nonlinearity of the land price-influencing factors. In this paper, an effective data-driven automated valuation framework is proposed for valuing real estate assets by combining a GIS (geographic information system) and neural network technologies. This framework can automatically obtain the values of spatial factors affecting land price from GIS and generate training set data for training the neural network to identify the complex relationship between all kinds of factors and benchmark land prices. The effectiveness and universality of the framework is verified via the data of benchmark land prices in Wuhan. The framework can be applied for automated benchmark land price valuation in other cities.
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