Regional assessment of mold growth risk in light wood-framed wall envelope based on meteorological data-driven and neural network model

IF 2.4 3区 农林科学 Q1 FORESTRY
Yanyu Zhao, Xinmiao Meng, Shiyi Mei, Xudong Zhu, Juan Yang, Ying Gao
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引用次数: 0

Abstract

Wood is a renewable material ideal for environmentally friendly buildings, but wooden building envelopes may face mold growth risks across different climates. To ensure the long-term service life of wooden buildings in China, it is imperative to evaluate the mold growth risk in each region. Nevertheless, large-scale regional assessments require significant effort and time. This study proposes a method based on meteorological data and a neural network (NN) model for regional mold risk assessment in light wood-framed wall envelopes. The NN model, comprising a one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM), is trained on meteorological data from the hot summer and cold winter (HSCW) region, which is one of China's five climatic regions. It is validated using simulated data and one year of field monitoring data. Finally, the model predicts time series of relative humidity and temperature with a mold index from the empirical VTT model to assess mold growth risk in the HSCW region. The validation results with simulated data show good performance, with average R2 values of 0.969 and 0.984 for predicting interior wall relative humidity and temperature, respectively. However, validation with monitoring data shows a decline in performance due to real-world complexities. The results of the risk assessment indicate that the wall used in this study is commonly at risk in the HSCW region. The proposed method is suitable for assessing mold risk in walls across diverse regional climates, thereby providing tailored improvements to the hygrothermal performance of walls.

木材是一种可再生材料,是环保建筑的理想材料,但在不同气候条件下,木质建筑围护结构可能面临霉菌生长的风险。为确保中国木结构建筑的长期使用寿命,必须对各地区的霉菌生长风险进行评估。然而,大规模的区域评估需要大量的精力和时间。本研究提出了一种基于气象数据和神经网络(NN)模型的方法,用于轻型木结构墙体围护结构的区域霉菌风险评估。神经网络模型由一维卷积神经网络(1D-CNN)和长短期记忆(LSTM)组成,以中国五大气候区之一的夏热冬冷地区(HSCW)的气象数据为基础进行训练。利用模拟数据和一年的实地监测数据对该模型进行了验证。最后,该模型利用经验 VTT 模型中的霉菌指数预测相对湿度和温度的时间序列,以评估夏热冬冷地区的霉菌生长风险。模拟数据的验证结果表明该模型性能良好,预测内墙相对湿度和温度的平均 R2 值分别为 0.969 和 0.984。不过,由于现实世界的复杂性,使用监测数据进行的验证结果显示性能有所下降。风险评估结果表明,本研究中使用的墙体在 HSCW 地区普遍存在风险。所提出的方法适用于评估不同地区气候条件下墙体的霉菌风险,从而有针对性地改善墙体的湿热性能。
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来源期刊
European Journal of Wood and Wood Products
European Journal of Wood and Wood Products 工程技术-材料科学:纸与木材
CiteScore
5.40
自引率
3.80%
发文量
124
审稿时长
6.0 months
期刊介绍: European Journal of Wood and Wood Products reports on original research and new developments in the field of wood and wood products and their biological, chemical, physical as well as mechanical and technological properties, processes and uses. Subjects range from roundwood to wood based products, composite materials and structural applications, with related jointing techniques. Moreover, it deals with wood as a chemical raw material, source of energy as well as with inter-disciplinary aspects of environmental assessment and international markets. European Journal of Wood and Wood Products aims at promoting international scientific communication and transfer of new technologies from research into practice.
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