{"title":"Regional assessment of mold growth risk in light wood-framed wall envelope based on meteorological data-driven and neural network model","authors":"Yanyu Zhao, Xinmiao Meng, Shiyi Mei, Xudong Zhu, Juan Yang, Ying Gao","doi":"10.1007/s00107-024-02156-1","DOIUrl":null,"url":null,"abstract":"<div><p>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 R<sup>2</sup> 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.</p></div>","PeriodicalId":550,"journal":{"name":"European Journal of Wood and Wood Products","volume":"83 2","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Wood and Wood Products","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s00107-024-02156-1","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FORESTRY","Score":null,"Total":0}
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.
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.