title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ ÑÑÐµÐ´ÑÑÐ²Ð° Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð±ÐµÑÐ¾Ð½Ð° Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of concrete stength prediction application using machine learning methods
Creators: name: ÐÐ°Ð´ÑÐ³Ð¸Ð½Ð°, ÐÐ¾Ð»Ð¸Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¿ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð±ÐµÑÐ¾Ð½Ð°
ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
machine learning
prediction
concrete strength
software development
regression

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description: The purpose of the work is development of concrete stength prediction application using machine learning algorithms. To achive the purpose of the work the following tasks have been solved: 1. Dataset analysis. 2. Study of various machine learning algorithms applied to the problem of concrete strength prediction based on the parameters of the concrete mixture. 3. Assessment of the effectiveness of algorithms and choice of the model with minimal root mean square error. 4. Development of concrete strength prediction application. 5. Application testing. During the work the subject area was studied. Besides, overview of existing applications of machine learning methods for the problem of concrete strength prediction was provided. As a result of the work, application for the concrete strength prediction was created. This application has the ability to calculate the concrete strength using traditional method (Bolomey equation) or to predict concrete strength using machine learning algorithms. Besides, there is an ability to see previous predictions, to remove them from database, to retrain embedded models supplementing dataset with userâs data with the same parameters, and to use retrained models for the prediction.