The effect of different mix ratios on the mechanical properties of concrete was investigated. The strength and deformation in terms of the strain of normal strength concrete were evaluated under concentric loading. The artificial neural network (ANN) technique was used for predicting the compressive stress and strain at peak stress of concrete. The input parameters for ANN architectures included water/cement ratio, aggregate/cement ratio, and slump values. An equation for predicting the strain of concrete at peak stress was proposed based on ANN output values for compressive stress and strain. The capability and performance of the proposed equation are compared with actual experimental results and predictions from existing fifty-three empirical equations, including several design codes and various strain models for normal and high strength, concretes, using several statistical indexes. The results showed that ANNs have good potential for predicting the compressive strength and strain at peak stress of concrete yielding close predictions with good agreement with the original ones.

• Application of artificial neural networks for prediction of concrete properties
  • 1. Introduction
  • 2. Materials and Methods
    • 2.1. Materials
    • 2.2. Methods of Testing
    • 2.3. Intelligent systems and methods
  • 3. Results and Discussions
    • 3.1. Strain models
    • 3.2. ANN models
    • 3.3. Statistical indices
    • 3.4. Strain at peak stress
    • 3.5. Pre/Exp ratio
    • 3.6. Applicability of strain models
  • 4. Conclusions

Magazine of Civil Engineering / Санкт-Петербургский политехнический университет Петра Великого. — Санкт-Петербург: СПбПУ, 2020-. — Периодичность: 8 раз в год. — Выходит с 2020 г. — Загл. с титул. экрана. — Свободный доступ из сети Интернет (чтение, печать, копирование). — Электрон. журнал. — Текст: электронный

Magazine of Civil Engineering / Санкт-Петербургский политехнический университет Петра Великого. — Санкт-Петербург: СПбПУ, 2020-. № 2 (110), 2022. — 1 файл (21,5 Мб). — Свободный доступ из сети Интернет (чтение, печать, копирование). — Электрон. журнал. — <URL:http://elib.spbstu.ru/dl/2/j22-201.pdf>.