ПРИМЕНЕНИЕ НЕЙРОННОЙ СЕТИ ДЛЯ ПРОГНОЗИРОВАНИЯ КОЭФФИЦИЕНТА КОНСОЛИДАЦИИ ГЛИНИСТОГО ГРУНТА Application of Artificial Neural Network in Predicting the Consolidation Coefficient of Clayey Soil

Aparna R.P.

Аннотация


Скорость компрессионного сжатия глинистого слоя представляет собой важный показатель при проектировании геотехнических сооружений. Эта скорость может быть определена через коэффициент консолидации в условиях вертикального дренирования (Cv). Вся процедура нахождения значения Cv, начиная от лабораторных испытаний, требует значительного времени и усилий. Рассмотрена возможность прогноза коэффициента консолидации глинистого грунта с помощью искусственной нейронной сети (ИНС) на основании таких характеристик, как удельный вес грунта, предел пластичности, предел текучести, индекс пластичности, гранулометрический состав и интенсивность давления. Для анализа
собранных данных использована простая прямая нейронная сеть с алгоритмом обратного распространения ошибки. Проверка результатов осуществлена методами оценки эффективности модели, включая перекрестную проверку с исключением одного образца. Установлено, что разработанная модель ИНС представляет собой инструмент, пригодный для прогнозирования Cv глинистого грунта.


Полный текст статьи публикуется в английской версии журнала
«Soil Mechanics and Foundation Engineering”, vol.63, No.3


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