ОБЗОР ПРИМЕНЕНИЯ ГЛУБИННОГО ОБУЧЕНИЯ В СТРОИТЕЛЬНОМ ПРОЕКТИРОВАНИИ: ПРОБЛЕМЫ И БУДУЩИЕ НАПРАВЛЕНИЯ
ОБЗОР ПРИМЕНЕНИЯ ГЛУБИННОГО ОБУЧЕНИЯ В СТРОИТЕЛЬНОМ ПРОЕКТИРОВАНИИ: ПРОБЛЕМЫ И БУДУЩИЕ НАПРАВЛЕНИЯ
DOI:
https://doi.org/10.22337/2587-9618-2025-21-1-146-156Ключевые слова:
глубинное обучение, машинное обучение, нейронные сети, проектирование конструкций, мониторинг состояния конструкций, оптимизация конструкцийАннотация
Глубинное обучение (DL), основная часть искусственного интеллекта (AI), рассматривается как технология трансформации в различных областях науки, таких как строительное проектирование. Данный критический обзор раскрывает потенциальный вклад глубинного обучения в решение сложных проблем, стоящих перед строительным проектированием, таких как оптимизация конструкции, прогнозирование и мониторинг поведения материалов, а также мониторинг состояния конструкции в режиме реального времени. Благодаря разработанным архитектурам нейронных сетей, таким как генеративные адверсивные сети (GAN), рекуррентные нейронные сети (RNN) и конволюционные нейронные сети (CNN), инженеры могут находить решения на основе традиционного детерминированного извлечения данных. Однако при этом широко распространены такие проблемы, как вычислительные требования, интерпретируемость моделей и нехватка данных. В данном обзоре освещаются последние достижения, практические применения и ограничения глубинного обучения в строительном проектировании, предлагаются пути будущих исследований для повышения его эффективности и интеграции в реальные практические задачи.
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