Revaluation of occupancy duration for live load using big data of enterprise credit information

Abstract

The occupancy duration is an essential variable in establishing the time-varying model of the live load. The manual questioning and inquiries, which are the common survey methods for occupancy duration, suffer from being expensive and time-consuming. This has hindered the updating of the data basis and progress in live load research. This study proposes a novel big data survey method, which utilizes 210 million enterprise credit information to collect occupancy duration samples. Along with the big data survey method, an address-oriented mining method is presented to efficiently extract duration samples of the target type from big data. Based on the actual distribution of the extracted duration data, a more general time-varying model, called the compound renewal process, is suggested to describe the live load. The stochastic harmonic function representation method and probability density evolution method are then adopted to access the maximum load distribution of the live load process. The maximum load distributions of the sustained and combined loads are calculated and compared with previous studies to evaluate the impact of the new survey results. Comparison shows that the occupancy duration of office buildings is significantly shorter, resulting in an increase in the maximum load. The proposed data survey method enables continuous updating of duration samples, and the compound renewal process allows better compatibility for new survey data. The up-to-date duration data and a realistic time-varying model provide new ideas for determining the design live load.