kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-3-398-413kaz29-3201Research ArticleФИЗИЧЕСКИЕ НАУКИPHYSICAL SCIENCESЭМПИРИЧЕСКОЕ СРАВНИТЕЛЬНОЕ ИССЛЕДОВАНИЕ ГРАДИЕНТНОГО БУСТИНГА ДЛЯ ПРОГНОЗИРОВАНИЯ ТЕРМИЧЕСКОГО РАЗЛОЖЕНИЯ В ПОЛИМЕРНЫХ КОМПОЗИТАХ С МИНЕРАЛЬНЫМИ НАПОЛНИТЕЛЯМИEMPIRICAL BENCHMARKING OF GRADIENT BOOSTING FOR THERMAL DEGRADATION PREDICTION IN MINERAL–POLYMER COMPOSITEShttps://orcid.org/0009-0009-7588-8976ИзтлеуоваБ.IztleuovaB.

Докторант

Актобе

PhD student

Aktobe

b.iztleuova@zhubanov.edu.kzhttps://orcid.org/0000-0002-1835-2501ИманчиевА.ImanchiyevA.

К.ф.-м.н, ассоциированный профессор

Актобе

Associate Professor, candidate of physical and mathematical sciences

Aktobe

imanchiev_ae@mail.ruhttps://orcid.org/0000-0002-7038-4631БекешевА.BekeshevA.

К.ф.-м.н, ассоциированный профессор

Актобе

Associate Professor, candidate of physical and mathematical sciences

Aktobe

amirbek2401@gmail.comhttps://orcid.org/0000-0001-9540-6334ЖантуринаН.ZhanturinaN.

PhD, ассоциированный профессор

Актобе

Associate Professor, PhD

Aktobe

nzhanturina@mail.ruhttps://orcid.org/0000-0001-9417-755XУсенкуловаШ.UssenkulovaSh.

PhD, ассоциированный профессор

Астана

Associate Professor, PhD

Astana

Sholpan1990@gmail.comhttps://orcid.org/0000-0001-5881-4400ВангС.WangX.

PhD, ассоциированный профессор

Государственная ключевая лаборатория пожарной науки

Хэфэй

Associate Professor, PhD

State Key Laboratory of Fire Science

Hefei

wxcmx@ustc.edu.cnАктюбинский региональный университет им. К. ЖубановаКазахстанK. Zhubanov Aktobe Regional UniversityKazakhstanКазахский университет технологии и бизнеса им. К. КулажановаКазахстанKazakh University of Technology and BusinessKazakhstanУниверситет науки и технологий КитаяКитайUniversity of Science and Technology of ChinaChina202626092026233398413Copyright © Изтлеуова Б., Иманчиев А., Бекешев А., Жантурина Н., Усенкулова Ш.S., Ванг С., 20262026Изтлеуова Б., Иманчиев А., Бекешев А., Жантурина Н., Усенкулова Ш., Ванг С.Iztleuova B., Imanchiyev A., Bekeshev A., Zhanturina N., Усенкулова Ш.S., Wang X.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/3201

В данном исследовании представлен математический и эмпирический анализ алгоритмов градиентного бустинга, применяемых к данным термогравиметрического анализа (ТГА) композитов на основе винилэфирных и эпоксидных смол с минеральными наполнителями. Алгоритм градиентного бустинга пошагово представлен на численном примере ТГА. Далее формально проанализированы алгоритмы XGBoost и CatBoost. Сравнение 12 алгоритмов выполнено на основе 478 данных из рецензируемой литературы с использованием пятикратно повторенной 5-блочной кросс-валидации. Методы Extra Trees и CatBoost показали статистически эквивалентные наилучшие результаты (среднее R² = 0.7430 ± 0.0879 и 0.7313 ± 0.0793 соответственно; Nadeau & Bengio corrected t = 0.483, p = 0.634), превзойдя все линейные базовые модели, SVR и MLP. Для практического применения рекомендуется CatBoost: его упорядоченный бустинг обеспечивает несмещенные оценки градиентов на малых выборках (n < 1000), а SHAP-анализ структуры градиентных деревьев выявляет физически интерпретируемые атрибуции признаков, согласующиеся с известными механизмами ТГА. Значения SHAP показали, что пиковая температура TGA (T_max) и тип матрицы являются основными определяющими факторами. Предложенный пайплайн предварительной обработки, включающий преобразование phr в wt%, label encoding и медианную импутацию, обеспечивает воспроизводимую стратегию для разнородных литературных TGA-данных.

This study presents a mathematical and empirical analysis of gradient boosting algorithms applied to thermogravimetric analysis (TGA) data for composites based on vinyl ether matrices and epoxides with mineral fillers. The gradient boosting algorithm is output step by step with a numerical example of TGA. XGBoost and CatBoost are then analyzed formally. 12 algorithms were compared on 478 data from the peer-reviewed literature using 5-repeated 5-fold cross-validation. Extra Trees and CatBoost achieved statistically equivalent top performance (mean R² = 0.7430 ± 0.0879 and 0.7313 ± 0.0793, respectively; Nadeau & Bengio corrected t = 0.483, p = 0.634), outperforming all linear baselines, SVR, and MLP. CatBoost is recommended for practical deployment: its ordered boosting produces unbiased gradient estimates on small datasets (n < 1000), and SHAP analysis of its gradient tree structure reveals physically interpretable feature attributions aligned with known TGA mechanisms. SHAP values identified TGA peak temperature (T_max) and matrix type as the dominant predictors. The proposed preprocessing pipeline including phr to wt% conversion, label encoding, and median imputation provides a reproducible strategy for heterogeneous TGA literature datasets.

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The authors declare that there are no conflicts of interest present.