Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS

By A. Sadrmomtazi, J. Sobhani, M. A. Mirgozar. 2013

215 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/38589afcf803d50dd10da1ef7742ab7258c04bf7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. S. Jamal
2. S. Londhe
3. Faezehossadat Khademi
4. N. Deshpande

In Civil and Structural Engineering

1. S. Jamal
2. S. Londhe
3. Faezehossadat Khademi
4. N. Deshpande

Citations in the corpus

• Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression (2016, methodology): “…of mandatory elements, nondimensional elements, and output elements described in the following: (A) Mandatory Elements (Raw Data): The weight per cubic meter is considered as raw data based on standard mix design procedures followed worldwide (Deshpande et al., 2014; Sadrmomtazi et al., 2013).”