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.

中文翻译：

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利用企业信用信息大数据重估活荷载占用时间


占用持续时间是建立活荷载时变模型的重要变量。人工询问和询问是占用时间的常见调查方法，但成本高昂且耗时。这阻碍了数据基础的更新和活载研究的进展。本研究提出了一种新颖的大数据调查方法，利用2.1亿条企业信用信息收集入住时长样本。与大数据调查方法一起，提出了一种面向地址的挖掘方法，以有效地从大数据中提取目标类型的持续时间样本。根据提取的持续时间数据的实际分布，建议使用更通用的时变模型（称为复合更新过程）来描述活荷载。然后采用随机调和函数表示方法和概率密度演化方法来获得活荷载过程的最大荷载分布。计算持续载荷和组合载荷的最大载荷分布，并与以前的研究进行比较，以评估新调查结果的影响。对比发现，办公楼的入住时间明显缩短，导致最大负荷增加。所提出的数据调查方法可以持续更新持续时间样本，并且复合更新过程可以更好地兼容新的调查数据。最新的持续时间数据和现实的时变模型为确定设计活荷载提供了新的思路。