Prediction of Compressive Strength of Aerated Lightweight Aggregate Concrete by Artificial Neural Network

2011 ◽  
Vol 84-85 ◽  
pp. 177-182
Author(s):  
Yoo Jae Kim ◽  
Jiong Hu ◽  
Soon Jae Lee ◽  
Benjamin J. Broughton
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Lightweight Aggregate ◽  
Lightweight Aggregate Concrete ◽  
Fine Aggregate ◽  
Volume Percentage ◽  
Aggregate Concrete ◽  
Design Variables ◽  
Artificial Neural

This paper presents artificial neural network techniques for predicting the compressive strength of Aerated Lightweight Aggregate Concrete (ALAC) based on the effects of the concrete mix parameters. The compressive strength of sixty different concretes with densities ranging from 551 to 1948 kg/m3 was used and trained. The primary mix design variables studied included amount of cement, water, coarse aggregate, fine aggregate, surfactant, the volume percentage of air in the matrix (A/M), and the volume percentage of matrix of the total mix (M/T). The training and testing results indicate that the model explains 0.984 and 0.979 of the variability in compressive strength for the single aggregate used in the study, respectively.

scholarly journals A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete

2021 ◽  
Vol 11 (22) ◽  
pp. 11077
Author(s):  
David Suescum-Morales ◽  
Lorenzo Salas-Morera ◽  
José Ramón Jiménez ◽  
Laura García-Hernández
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Recycled Concrete ◽  
Recycled Aggregate Concrete ◽  
Recycled Aggregate ◽  
Dry Density ◽  
Aggregate Concrete ◽  
Concrete Production ◽  
Artificial Neural

Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation of RCA in concrete production. The compressive strength of recycled aggregate concrete is closely related to the dosage of its constituents. This article proposes a novel artificial neural network (ANN) model to predict the 28-day compressive strength of recycled aggregate concrete. The ANN used in this work has 11 neurons in the input layer: the mass of cement, fly ash, water, superplasticizer, fine natural aggregate, coarse natural or recycled aggregate, and their properties, such as: sand fineness modulus of sand, water absorption capacity, saturated surface dry density of the coarse aggregate mix and the maximum particle size. Two training methods were used for the ANN combining 15 and 20 hidden layers: Levenberg–Marquardt (LM) and Bayesian Regularization (BR). A database with 177 mixes selected from 15 studies incorporating RCA were selected, with the aim of having an underlying set of data heterogeneous enough to demonstrate the efficiency of the proposed approach, even when data are heterogeneous and noisy, which is the main finding of this work.

scholarly journals Influence of Aggregate Gradation on the Engineering Properties of Lightweight Aggregate Concrete

2018 ◽  
Vol 8 (8) ◽  
pp. 1324 ◽  
Author(s):  
How-Ji Chen ◽  
Chung-Hao Wu
Keyword(s):  
Compressive Strength ◽  
Lightweight Aggregate ◽  
Normal Weight ◽  
Engineering Properties ◽  
Lightweight Aggregate Concrete ◽  
Aggregate Gradation ◽  
Aggregate Concrete ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Expanded Shale

Expanded shale lightweight aggregates, as the coarse aggregates, were used to produce lightweight aggregate concrete (LWAC) in this research. At the fixed water-cement ratio, paste quantity, and aggregate volume, the effects of various aggregate gradations on the engineering properties of LWAC were investigated. Comparisons to normal-weight concrete (NWC) made under the same conditions were carried out. From the experimental results, using normal weight aggregates that follow the specification requirements (standard gradation) obtained similar NWC compressive strength to that using uniform-sized aggregates. However, the compressive strength of LWAC made using small uniform-sized aggregates was superior to that made from standard-grade aggregates. This is especially conspicuous under the low water-cement ratio. Even though the workability was affected, this problem could be overcome with developed chemical additive technology. The durability properties of concrete were approximately equal. Therefore, it is suggested that the aggregate gradation requirement of LWAC should be distinct from that of NWC. In high strength LWAC proportioning, following the standard gradation suggested by American Society for Testing and Materials (ASTM) is optional.

Sign in / Sign up

Export Citation Format

Share Document