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\n\n \n \n \n \n \n Simulations of microclimates for wood-decaying fungi in the built environment using environmental analysis.\n \n \n \n\n\n \n van Niekerk, P. B.; Niklewski, J.; Hosseini, S. H.; Nicholas, B.; Frimannslund, I.; Thiis, T. K.; and Brischke, C.\n\n\n \n\n\n\n In
Proceedings IRG Annual Meeting, pages 17, Cairns, Australia, June 2023. \n
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@inproceedings{van_niekerk_simulations_2023,\n\taddress = {Cairns, Australia},\n\ttitle = {Simulations of microclimates for wood-decaying fungi in the built environment using environmental analysis},\n\tdoi = {IRG/WP 23-20703},\n\tabstract = {Simulations of fungal decay risk were run on two similar building geometries exposed to typical annual climate conditions of two different geographical locations, Brunswick (Germany) and Cairns (Australia). The simulations were conducted to capture the effect of wind-driven rain and solar irradiation exposure over nodes of the common building geometry. The moisture content and temperature variations were then calculated point-by-point using simulation outputs, climate data and various models. A supervised machine-learning algorithm using artificial neural networks was used to calculate moisture content to more efficiently handle processing requirements. Time series of moisture content and temperature were used as input into fungal decay models, and in turn, service life planning (SLP) frameworks, where cumulative daily dose was used as the risk metric. Here, we applied the established SLP framework used in project CLICKdesign, which uses a doseresponse exposure model in combination with the Meyer-Veltrup resistance model. With this specific SLP framework, various materials can be evaluated or troubleshot based on their adherence to design life specifications. Dose represents the material climate (MC and temperature), and adding surface conditions as opposed to ambient macro climate estimates presents a step forward in capturing the microclimate surrounding the material. The examples shown indicate the importance of addressing the unique variation introduced with the combination of geometry and geographical location.},\n\tlanguage = {en},\n\tbooktitle = {Proceedings {IRG} {Annual} {Meeting}},\n\tauthor = {van Niekerk, Philip Bester and Niklewski, Jonas and Hosseini, Seyyed Hasan and Nicholas, Brendan and Frimannslund, Iver and Thiis, Thomas Kringlebotn and Brischke, Christian},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {17},\n\tfile = {van Niekerk et al. - Simulations of microclimates for wood-decaying fun.pdf:C\\:\\\\Users\\\\Eva\\\\Zotero\\\\storage\\\\JUIUDA85\\\\van Niekerk et al. - Simulations of microclimates for wood-decaying fun.pdf:application/pdf},\n}\n\n
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\n Simulations of fungal decay risk were run on two similar building geometries exposed to typical annual climate conditions of two different geographical locations, Brunswick (Germany) and Cairns (Australia). The simulations were conducted to capture the effect of wind-driven rain and solar irradiation exposure over nodes of the common building geometry. The moisture content and temperature variations were then calculated point-by-point using simulation outputs, climate data and various models. A supervised machine-learning algorithm using artificial neural networks was used to calculate moisture content to more efficiently handle processing requirements. Time series of moisture content and temperature were used as input into fungal decay models, and in turn, service life planning (SLP) frameworks, where cumulative daily dose was used as the risk metric. Here, we applied the established SLP framework used in project CLICKdesign, which uses a doseresponse exposure model in combination with the Meyer-Veltrup resistance model. With this specific SLP framework, various materials can be evaluated or troubleshot based on their adherence to design life specifications. Dose represents the material climate (MC and temperature), and adding surface conditions as opposed to ambient macro climate estimates presents a step forward in capturing the microclimate surrounding the material. The examples shown indicate the importance of addressing the unique variation introduced with the combination of geometry and geographical location.\n
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\n\n \n \n \n \n \n Utilising novel service life prediction methods for robust and precise Life-Cycle-Costing (LCC).\n \n \n \n\n\n \n van Niekerk, P. B.; Alfredsen, G.; Kalamees, T.; Modaresi, R.; Sandak, A.; Niklewski, J.; and Brischke, C.\n\n\n \n\n\n\n In
Proceedings IRG Annual Meeting, pages 6, Cairns, Australia, June 2023. \n
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@inproceedings{van_niekerk_utilising_2023,\n\taddress = {Cairns, Australia},\n\ttitle = {Utilising novel service life prediction methods for robust and precise {Life}-{Cycle}-{Costing} ({LCC})},\n\tdoi = {IRG/WP 23-50384},\n\tabstract = {Simulations of fungal decay risk were run on two similar building geometries exposed to typical annual climate conditions of two different geographical locations, Brunswick (Germany) and Cairns (Australia). The simulations were conducted to capture the effect of wind-driven rain and solar irradiation exposure over nodes of the common building geometry. The moisture content and temperature variations were then calculated point-by-point using simulation outputs, climate data and various models. A supervised machine-learning algorithm using artificial neural networks was used to calculate moisture content to more efficiently handle processing requirements. Time series of moisture content and temperature were used as input into fungal decay models, and in turn, service life planning (SLP) frameworks, where cumulative daily dose was used as the risk metric. Here, we applied the established SLP framework used in project CLICKdesign, which uses a doseresponse exposure model in combination with the Meyer-Veltrup resistance model. With this specific SLP framework, various materials can be evaluated or troubleshot based on their adherence to design life specifications. Dose represents the material climate (MC and temperature), and adding surface conditions as opposed to ambient macro climate estimates presents a step forward in capturing the microclimate surrounding the material. The examples shown indicate the importance of addressing the unique variation introduced with the combination of geometry and geographical location.},\n\tlanguage = {en},\n\tbooktitle = {Proceedings {IRG} {Annual} {Meeting}},\n\tauthor = {van Niekerk, Philip Bester and Alfredsen, Grey and Kalamees, Targo and Modaresi, Roja and Sandak, Anna and Niklewski, Jonas and Brischke, Christian},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {6},\n\tfile = {van Niekerk et al. - 2023 - Utilising novel service life prediction methods fo.pdf:C\\:\\\\Users\\\\Eva\\\\Zotero\\\\storage\\\\35AQAPL9\\\\van Niekerk et al. - 2023 - Utilising novel service life prediction methods fo.pdf:application/pdf},\n}\n\n
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\n Simulations of fungal decay risk were run on two similar building geometries exposed to typical annual climate conditions of two different geographical locations, Brunswick (Germany) and Cairns (Australia). The simulations were conducted to capture the effect of wind-driven rain and solar irradiation exposure over nodes of the common building geometry. The moisture content and temperature variations were then calculated point-by-point using simulation outputs, climate data and various models. A supervised machine-learning algorithm using artificial neural networks was used to calculate moisture content to more efficiently handle processing requirements. Time series of moisture content and temperature were used as input into fungal decay models, and in turn, service life planning (SLP) frameworks, where cumulative daily dose was used as the risk metric. Here, we applied the established SLP framework used in project CLICKdesign, which uses a doseresponse exposure model in combination with the Meyer-Veltrup resistance model. With this specific SLP framework, various materials can be evaluated or troubleshot based on their adherence to design life specifications. Dose represents the material climate (MC and temperature), and adding surface conditions as opposed to ambient macro climate estimates presents a step forward in capturing the microclimate surrounding the material. The examples shown indicate the importance of addressing the unique variation introduced with the combination of geometry and geographical location.\n
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\n\n \n \n \n \n \n Einfluss von Leckagen auf die Gebrauchsdauer von Dachkonstruktionen bei einem FlachdachWarmdach.\n \n \n \n\n\n \n Odinius, T.\n\n\n \n\n\n\n Technical Report Wood Biology and Wood Products, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Göttingen, Germany, September 2023.\n
Prüfer: Dr. S. Bollmus, P. van Niekerk\n\n
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@techreport{odinius_einfluss_2023,\n\taddress = {Göttingen, Germany},\n\ttype = {Bachelor thesis},\n\ttitle = {Einfluss von {Leckagen} auf die {Gebrauchsdauer} von {Dachkonstruktionen} bei einem {FlachdachWarmdach}},\n\tlanguage = {de ger},\n\tinstitution = {Wood Biology and Wood Products, Faculty of Forest Sciences and Forest Ecology, University of Goettingen},\n\tauthor = {Odinius, Thilo},\n\tmonth = sep,\n\tyear = {2023},\n\tnote = {Prüfer: Dr. S. Bollmus, P. van Niekerk},\n\tpages = {35},\n\tfile = {Odinius - 2023 - Einfluss von Leckagen auf die Gebrauchsdauer von D.pdf:C\\:\\\\Users\\\\Eva\\\\Zotero\\\\storage\\\\X4IRULL8\\\\Odinius - 2023 - Einfluss von Leckagen auf die Gebrauchsdauer von D.pdf:application/pdf},\n}\n\n
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\n\n \n \n \n \n \n \n The effect of weathering on the surface moisture conditions of Norway spruce under outdoor exposure.\n \n \n \n \n\n\n \n Niklewski, J.; Van Niekerk, P. B.; and Marais, B. N.\n\n\n \n\n\n\n
Wood Material Science & Engineering, 18(4): 1394–1404. July 2023.\n
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@article{niklewski_effect_2023,\n\ttitle = {The effect of weathering on the surface moisture conditions of {Norway} spruce under outdoor exposure},\n\tvolume = {18},\n\tissn = {1748-0272, 1748-0280},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/17480272.2022.2144444},\n\tdoi = {10.1080/17480272.2022.2144444},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-12-12},\n\tjournal = {Wood Material Science \\& Engineering},\n\tauthor = {Niklewski, Jonas and Van Niekerk, Philip Bester and Marais, Brendan Nicholas},\n\tmonth = jul,\n\tyear = {2023},\n\tpages = {1394--1404},\n\tfile = {Volltext:C\\:\\\\Users\\\\Eva\\\\Zotero\\\\storage\\\\MAKWYHCH\\\\Niklewski et al. - 2023 - The effect of weathering on the surface moisture c.pdf:application/pdf},\n}\n\n
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\n\n \n \n \n \n \n Time-dependent moisture thresholds for fungal growth and decay of Trametes versicolor.\n \n \n \n\n\n \n Brischke, C.; and van Niekerk, P. B.\n\n\n \n\n\n\n In
Proceedings of the 19th Meeting of the Northern European Network for Wood Science and Engineering (WSE), pages 184–186, Ås, Norway, October 2023. \n
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@inproceedings{brischke_time-dependent_2023,\n\taddress = {Ås, Norway},\n\ttitle = {Time-dependent moisture thresholds for fungal growth and decay of {Trametes} versicolor},\n\tbooktitle = {Proceedings of the 19th {Meeting} of the {Northern} {European} {Network} for {Wood} {Science} and {Engineering} ({WSE})},\n\tauthor = {Brischke, Christian and van Niekerk, Philip B.},\n\tmonth = oct,\n\tyear = {2023},\n\tpages = {184--186},\n}\n\n
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\n\n \n \n \n \n \n \n Modelling in-ground wood decay using time-series retrievals from the 5 $^{\\textrm{th}}$ European climate reanalysis (ERA5-Land).\n \n \n \n \n\n\n \n Marais, B. N.; Schönauer, M.; Van Niekerk, P. B.; Niklewski, J.; and Brischke, C.\n\n\n \n\n\n\n
European Journal of Remote Sensing, 56(1): 2264473. December 2023.\n
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@article{marais_modelling_2023,\n\ttitle = {Modelling in-ground wood decay using time-series retrievals from the 5 $^{\\textrm{th}}$ {European} climate reanalysis ({ERA5}-{Land})},\n\tvolume = {56},\n\tissn = {2279-7254},\n\turl = {https://www.tandfonline.com/doi/full/10.1080/22797254.2023.2264473},\n\tdoi = {10.1080/22797254.2023.2264473},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-12-15},\n\tjournal = {European Journal of Remote Sensing},\n\tauthor = {Marais, Brendan N. and Schönauer, Marian and Van Niekerk, Philip Bester and Niklewski, Jonas and Brischke, Christian},\n\tmonth = dec,\n\tyear = {2023},\n\tpages = {2264473},\n\tfile = {Volltext:C\\:\\\\Users\\\\Eva\\\\Zotero\\\\storage\\\\QBUYUXXH\\\\Marais et al. - 2023 - Modelling in-ground wood decay using time-series r.pdf:application/pdf},\n}\n\n
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\n\n \n \n \n \n \n \n Moisture prediction of timber for durability applications using data-driven modelling.\n \n \n \n \n\n\n \n Hosseini, S. H.; Niklewski, J.; and Van Niekerk, P. B.\n\n\n \n\n\n\n In
World Conference on Timber Engineering (WCTE 2023), pages 3808–3815, Oslo, Norway, 2023. World Conference on Timber Engineering (WCTE 2023)\n
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@inproceedings{hosseini_moisture_2023,\n\taddress = {Oslo, Norway},\n\ttitle = {Moisture prediction of timber for durability applications using data-driven modelling},\n\tisbn = {978-1-71387-329-7 978-1-71387-327-3},\n\turl = {http://www.proceedings.com/069179-0495.html},\n\tdoi = {10.52202/069179-0495},\n\turldate = {2023-12-15},\n\tbooktitle = {World {Conference} on {Timber} {Engineering} ({WCTE} 2023)},\n\tpublisher = {World Conference on Timber Engineering (WCTE 2023)},\n\tauthor = {Hosseini, Seyyed Hasan and Niklewski, Jonas and Van Niekerk, Philip Bester},\n\tyear = {2023},\n\tpages = {3808--3815},\n\tfile = {Volltext:C\\:\\\\Users\\\\Eva\\\\Zotero\\\\storage\\\\ARV7EIGN\\\\Hosseini et al. - 2023 - MOISTURE PREDICTION OF TIMBER FOR DURABILITY APPLI.pdf:application/pdf},\n}\n\n
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\n\n \n \n \n \n \n \n Modelling in-ground wood decay using time-series retrievals from the 5th European climate reanalysis (ERA5-Land).\n \n \n \n \n\n\n \n Marais, B. N.; Schönauer, M.; van Niekerk, P. B.; Niklewski, J.; and Brischke, C.\n\n\n \n\n\n\n
European Journal of Remote Sensing, 56(1): 2264473. December 2023.\n
Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/22797254.2023.2264473\n\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{marais_modelling_2023-1,\n\ttitle = {Modelling in-ground wood decay using time-series retrievals from the 5th {European} climate reanalysis ({ERA5}-{Land})},\n\tvolume = {56},\n\tissn = {null},\n\turl = {https://doi.org/10.1080/22797254.2023.2264473},\n\tdoi = {10.1080/22797254.2023.2264473},\n\tabstract = {This article presents models to predict the time until mechanical failure of in-ground wooden test specimens resulting from fungal decay. Historical records of decay ratings were modelled by remotely sensed data from ERA5-Land. In total, 2,570 test specimens of 16 different wood species were exposed at 21 different test sites, representing three continents and climatic conditions from sub-polar to tropical, spanning a period from 1980 until 2022. To obtain specimen decay ratings over their exposure time, inspections were conducted in mostly annual and sometimes bi-annual intervals. For each specimen’s exposure period, a laboratory developed dose–response model was populated using remotely sensed soil moisture and temperature data retrieved from ERA5-Land. Wood specimens were grouped according to natural durability rankings to reduce the variability of in-ground wood decay rate between wood species. Non-linear, sigmoid-shaped models were then constructed to describe wood decay progression as a function of daily accumulated exposure to soil moisture and temperature conditions (dose). Dose, a mechanistic weighting of daily exposure conditions over time, generally performed better than exposure time alone as a predictor of in-ground wood decay progression. The open-access availability of remotely sensed soil-state data in combination with wood specimen data proved promising for in-ground wood decay predictions.},\n\tnumber = {1},\n\turldate = {2024-03-13},\n\tjournal = {European Journal of Remote Sensing},\n\tauthor = {Marais, Brendan N. and Schönauer, Marian and van Niekerk, Philip Bester and Niklewski, Jonas and Brischke, Christian},\n\tmonth = dec,\n\tyear = {2023},\n\tnote = {Publisher: Taylor \\& Francis\n\\_eprint: https://doi.org/10.1080/22797254.2023.2264473},\n\tkeywords = {dose–response model, Fungal wood decay, geospatial modelling, IRG-WP durability database, soil moisture, soil temperature},\n\tpages = {2264473},\n\tfile = {Full Text PDF:C\\:\\\\Users\\\\Eva\\\\Zotero\\\\storage\\\\JUMXMVZT\\\\Marais et al. - 2023 - Modelling in-ground wood decay using time-series r.pdf:application/pdf},\n}\n\n
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\n This article presents models to predict the time until mechanical failure of in-ground wooden test specimens resulting from fungal decay. Historical records of decay ratings were modelled by remotely sensed data from ERA5-Land. In total, 2,570 test specimens of 16 different wood species were exposed at 21 different test sites, representing three continents and climatic conditions from sub-polar to tropical, spanning a period from 1980 until 2022. To obtain specimen decay ratings over their exposure time, inspections were conducted in mostly annual and sometimes bi-annual intervals. For each specimen’s exposure period, a laboratory developed dose–response model was populated using remotely sensed soil moisture and temperature data retrieved from ERA5-Land. Wood specimens were grouped according to natural durability rankings to reduce the variability of in-ground wood decay rate between wood species. Non-linear, sigmoid-shaped models were then constructed to describe wood decay progression as a function of daily accumulated exposure to soil moisture and temperature conditions (dose). Dose, a mechanistic weighting of daily exposure conditions over time, generally performed better than exposure time alone as a predictor of in-ground wood decay progression. The open-access availability of remotely sensed soil-state data in combination with wood specimen data proved promising for in-ground wood decay predictions.\n
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