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

WANG Yixi; ZHENG Jiaxiang; HU Keyang; FU Jiayan; HU Xianglei

doi:10.3724/j.gyjzG25111704

Volume 56 Issue 5

May 2026

Turn off MathJax

Article Contents

Article Navigation > INDUSTRIAL CONSTRUCTION > 2026 > 56(5): 167-175

WANG Yixi, ZHENG Jiaxiang, HU Keyang, FU Jiayan, HU Xianglei. 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

Citation:

WANG Yixi, ZHENG Jiaxiang, HU Keyang, FU Jiayan, HU Xianglei. 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

Citation:

WANG Yixi, ZHENG Jiaxiang, HU Keyang, FU Jiayan, HU Xianglei. 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

PDF( 1561 KB)

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

doi: 10.3724/j.gyjzG25111704

1. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China;
2. International School of Engineering, Tianjin Chengjian University, Tianjin 300384, China

Received Date: 2025-11-17
Available Online: 2026-06-06
Publish Date: 2026-05-20

Abstract

Abstract

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

FullText(HTML)

References(44)

References

[1]	AGRAWAL A，CHOUDHARY A. Perspective：materials informatics and big data：realization of the“fourth paradigm” of science in materials science［J］. APL Materials，2016，4（5）：053208.
[2]	SHEN C，WANG C，WEI X，et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel［J］. Acta Materialia，2019，179：201- 214.
[3]	CAI J Z，CHU X，XU K，et al. Machine learning-driven new material discovery［J］. Nanoscale Advances，2020，2：3115- 3130.
[4]	CHAN C H，SUN M，HUANG B. Application of machine learning for advanced material prediction and design［J］. EcoMat，2022，4：e12194.
[5]	YANG C，REN C，JIA Y F，et al. A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness［J］. Acta Materialia. 2022，222：117431.
[6]	TAO Q L，XU P C，LI M J，et al. Machine learning for perovskite materials design and discovery［J］. NPJ Computational Materials，2021，7：23.
[7]	LIU X J，XU P C，ZHAO J J，et al. Material machine learning for alloys:applications，challenges and perspectives［J］. Journal of Alloys and Compounds，2022，921：165984.
[8]	XU P C，CHEN H M，LI M J，et al. New opportunity：machine learning for polymer materials design and discovery［J］. Advanced Theory and Simulations，2022，5：2100565.
[9]	康孟羽，朱月琴，陈晨，等. 基于多元非线性回归和BP神经网络的滑坡滑动距离预测模型研究［J］. 地质通报，2022，41（12）：2281- 2289.
[10]	魏乐，李承霖，房方，等. 小样本下基于改进麻雀算法优化卷积神经网络的飞轮储能系统损耗［J］. 电网技术，2025，49（1）：366- 372.
[11]	凌薇，杨遵挥，张芷薇，等. 基于BP神经网络和遗传算法的封闭式厨房污染物模拟与优化［J］. 建筑科学，2024，40（2）：111- 119.
[12]	蔡安辉刘永刚，孙国雄. 基于正交试验的BP神经网络预测研究［J］. 中国工程科学，2003（7）：67- 71.
[13]	ZHOU Y，DING F. A novel recursive multivariate nonlinear time-series modeling method by using the coupling identification concept［J］. Applied Mathematical Modelling，2024，127：571- 587.
[14]	MELO A P，VERSAGE R S，SAWAYA G，et al. A novel surrogate model to support building energy labelling system：a new approach to assess cooling energy demand in commercial buildings［J］. Energy and Buildings，2016，131：233- 247.
[15]	KELCHNER S A，GROUP B P. Higher level phylogenetic relationships within the bamboos（Poaceae：Bambusoideae）based on five plastid markers［J］. Molecular Phylogenetics and Evolution，2013，67（2）：404- 413.
[16]	JANIESCH C，ZSCHECH P，HEINRICH K. Machine learning and deep learning［J］. Electronic Markets，2021，31：685- 695.
[17]	BI Q F，GOODMAN K E，KAMINSKY J，et al. What is machine learning? a primer for the epidemiologist［J］. American Journal of Epidemiology，2019，188：2222- 2239.
[18]	WARIN T，STOJKOV A. Machine learning in finance：a metadata-based systematic review of the literature［J］. Journal of Risk and Financial Management，2021，14（7）：302.
[19]	AHMED S，ALSHATER M M，AMMARI A E，et al. Artificial intelligence and machine learning in finance：a bibliometric review［J］. Research in International Business and Finance，2022，61：101646.
[20]	MUELLER B，KINOSHITA T，PEEBLES A，et al. Artificial intelligence and machine learning in emergency medicine：a narrative review［J］. Acute Medicine & Surgery，2022，9：e740.
[21]	SABRY F，ELTARAS T，LABDA W，et al. Machine learning for healthcare wearable devices:the big picture［J］. Joural of Healthcare Engineering，2022，2022（1）：4653923.
[22]	OKOROAFOR E R，SMITH C M，OCHIEET K I，et al. Machine learning in subsurface geothermal energy：two decades in review［J］. Geothermics，2022，102：102401.
[23]	CIOFFI R，TRAVAGLIONI M，PISCITELLI G，et al. Artificial Intelli gence and machine learning applications in smart production：progress，trends，and directions［J］. Sustainability，2020，12：492.
[24]	CRAMPON K，GIORKALLOS A，DELDOSSI M，et al. Machine learning methods for ligand-protein molecular docking［J］. Drug Discovery Today，2022，27：151- 164.
[25]	JIANG Y R，LUO J，HUANG D，et al. Machine learning advances in microbiology：a review of methods and applications［J］. Frontiers in Microbiology，2022，13：925454.
[26]	SHAO Q M，ZHANG Z S. Berry-Esseen bounds for multivariate nonlinear statistics with applications to M-estimators and stochastic gradient descent algorithms［J］. Bernoulli，2022，28（3）：1548- 1576.
[27]	李紫微，林波荣，陈洪钟. 建筑方案能耗快速预测方法研究综述［J］. 暖通空调，2018，48（5）：1- 8.
[28]	ZHANG Q，CHANG D，ZHAI X，et al. OCPMDM：online computation platform for materials data mining［J］. Chemometrics and Intelligent Laboratory Systems，2018，177：26- 34.
[29]	LI L，TAO Q L，XU P C，et al. Studies on the regularity of perovskite formation via machine learning［J］. Computational Materials Science，2021，199：110712.
[30]	YANG X，LI L，TAO Q L，et al. Rapid discovery of narrow bandgap oxide double perovskites using machine learning［J］. Computational Materials Science，2021，196：110528.
[31]	TAO Q L，CHANG D P，LU T，et al. Multiobjective stepwise design strategy-assisted design of high-performance perovskite oxide photocatalysts［J］. The Journal of Physical Chemistry C，2021，125：21141- 21150.
[32]	XU P C，JI X B，LI M J，et al. Small data machine learning in materials science［J］. NPJ Computational Materials，2023，9（1）：42.
[33]	WANG Y X，SHI Y，ZHOU B，Improvement of the Hygrothermal performance of mud-coated material used in traditional bamboo-woven mud walls［J］. International Journal of Architectural Heritage，2023，17：1630- 1647.
[34]	FENG H. Machine learning for composite material analysis and optimization［J］. Madison：The University of Wisconsin-Madison Press，2023.
[35]	谢建新，宿彦京，薛德祯，等. 机器学习在材料研发中的应用［J］. 金属学报，2021，57（11）：1343- 1361.
[36]	WANG Y Q，YAO Q M，KWOK J T，et al. Generalizing from a few examples:a survey on few-shot learning［J］. ACM computing Surveys，2020，53（3）：1- 34.
[37]	LU J，GONG P，YE J，et al. A survey on machine learning from few samples［J］. Pattern Recognition，2023，139：109480.
[38]	LI D C，CHEN S C，LIN Y S，et al. A generative adversarial network structure for learning with small numerical data sets［J］. Applied Sciences，2021，11（22）：10823.
[39]	SANCHEZ-LENGELING B，ASPURU-GUZIK A. Inverse molecular design using machine learn-ing：generative models for matter engineering［J］. Science，2018，361：360- 365.
[40]	BASH D，CAI Y，CHELLAPPAN V，et al. Multi‐fidelity high‐throughput optimization of electrical conductivity in P3HT‐CNT composites［J］. Advanced Functional Materials，2021，31（36）：2102606.
[41]	中华人民共和国建设部. 民用建筑热工设计规范：GB/T 50176—1993［S］. 北京：中国计划出版社，1993.
[42]	中华人民共和国住房和城乡建设部. 公共建筑节能设计标准：GB 50189—2015［S］. 北京：中国建筑工业出版社，2015.
[43]	中国建筑科学研究院. 夏热冬冷地区居住建筑节能设计标准［J］. 上海建材，2001（5）：8- 10.
[44]	中华人民共和国建设部. 夏热冬暖地区居住建筑节能设计标准：JGJ 75—2003［S］. 北京：中国建筑工业出版社，2003.