This paper presents the continuous monitoring of an eight-story cross-laminated timber (CLT) building. The monitoring process includes daily acceleration measurements at the rooftop, along with external temperature, humidity, and wind velocity data. Additionally, moisture content (MC) of timber at various locations in the internal and perimeter walls is measured. The extraction of modal parameters is automated and is based on the Stochastic Subspace Identification method. This research primarily evaluates how environmental factors, particularly temperature, wood MC, snow height, and wind velocity, affect the building’s modal parameters and vibrational response. The data has been found to significantly correlate with temperature, wood MC, and snow level. Subsequently, the authors performed a Bayesian model updating of the building to estimate the relationship between the shear modulus of CLT and the MC. This analysis has led to an empirical formula for predicting the stiffness properties of CLT walls based on wood MC derived from long-term monitoring of a timber building. To the authors’ knowledge, it is the first empirical expression relating a mechanical property of timber and MC, indirectly estimated from ambient vibration data.

中文翻译：

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对一栋八层 CLT 建筑进行长期连续动态监测


本文介绍了对八层正交胶合木 （CLT） 建筑的连续监测。监控过程包括屋顶的日常加速度测量，以及外部温度、湿度和风速数据。此外，还测量了内墙和周边墙不同位置的木材的含水率 （MC）。模态参数的提取是自动的，并且基于随机子空间识别方法。本研究主要评估环境因素，特别是温度、木材 MC、雪高和风速，如何影响建筑物的模态参数和振动响应。已发现该数据与温度、木材 MC 和积雪水平显着相关。随后，作者对建筑物进行了贝叶斯模型更新，以估计 CLT 的剪切模量与 MC 之间的关系。该分析得出了一个经验公式，用于预测基于木结构建筑长期监测的木材 MC 预测 CLT 墙的刚度特性。据作者所知，这是第一个将木材和 MC 的机械性能联系起来的经验表达式，根据环境振动数据间接估计。