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Research on Machine Learning-Based Prediction and Optimization Methods for the Performance of Traditional Building Wall Materials： a Case Study of Bamboo-Woven Mud Walls
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Yixi WANG1, Jiaxiang ZHENG1, Keyang HU2, Jiayan FU1, Xianglei HU1
Industrial Construction | 2026, 56(5) : 167 - 175
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Industrial Construction | 2026, 56(5): 167-175
Research on Machine Learning-Based Prediction and Optimization Methods for the Performance of Traditional Building Wall Materials： a Case Study of Bamboo-Woven Mud Walls
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Yixi WANG1, Jiaxiang ZHENG1, Keyang HU2, Jiayan FU1, Xianglei HU1
Affiliations
  • 1College of Architecture and Urban Planning， Tongji University， Shanghai200092， China
  • 2International School of Engineering， Tianjin Chengjian University， Tianjin300384， China
Published: 2026-05-20 doi: 10.3724/j.gyjzG25111704
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This study applied machine learning to predict and optimize the hygrothermal performance of bamboo-woven mud walls， highlighting their potential in addressing environmental challenges. Generative adversarial networks （GANs） were first used to augment limited experimental data， addressing small-sample constraints. A back propagation （BP） neural network was employed to analyze and predict the performance of the wall materials. After optimization via a genetic algorithm （GA）， the model’s R² improved to 0.77， indicating significantly enhanced predictive performance. These findings confirm the feasibility of using machine learning in the reuse of traditional building materials and provide a digital theoretical basis and technical support for the preservation and renewal of bamboo-woven mud walls.

bamboo-woven mud wall  /  machine learning  /  hygrothermal properties  /  generative adversarial networks  /  genetic algorithm
Yixi WANG, Jiaxiang ZHENG, Keyang HU, Jiayan FU, Xianglei HU. Research on Machine Learning-Based Prediction and Optimization Methods for the Performance of Traditional Building Wall Materials： a Case Study of Bamboo-Woven Mud Walls[J]. Industrial Construction, 2026 , 56 (5) : 167 -175 . DOI: 10.3724/j.gyjzG25111704
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Year 2026 volume 56 Issue 5
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doi: 10.3724/j.gyjzG25111704
  • Receive Date：2025-11-17
  • Online Date：2026-06-25
  • Published：2026-05-20
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  • Received：2025-11-17
Affiliations
    1College of Architecture and Urban Planning， Tongji University， Shanghai200092， China
    2International School of Engineering， Tianjin Chengjian University， Tianjin300384， China
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https://castjournals.cast.org.cn/joweb/gyjz/EN/10.3724/j.gyjzG25111704
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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