scholarly journals Influence of Fine Recycled Concrete Powder on the Compressive Strength of Self-Compacting Concrete (SCC) Using Artificial Neural Network

2021 ◽  
Vol 13 (6) ◽  
pp. 3111
Author(s):  
Sara Boudali ◽  
Bahira Abdulsalam ◽  
Amir Hossein Rafiean ◽  
Sébastien Poncet ◽  
Ahmed Soliman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Cementitious Materials ◽  
Recycled Concrete ◽  
Supplementary Cementitious Materials ◽  
Ann Model ◽  
Self Compacting Concrete ◽  
Unseen Data ◽  
Artificial Neural

This paper aims to investigate the effect of fine recycled concrete powder (FRCP) on the strength of self-compacting concrete (SCC). For this purpose, a numerical artificial neural network (ANN) model was developed for strength prediction of SCC incorporating FRCP. At first, 240 experimental data sets were selected from the literature to develop the model. Approximately 60% of the database was used for training, 20% for testing, and the remaining 20% for the validation step. Model inputs included binder content, water/binder ratio, recycled concrete aggregates’ (RCA) content, percentage of supplementary cementitious materials (fly ash), amount of FRCP, and curing time. The model provided reliable results with mean square error (MSE) and regression values of 0.01 and 0.97, respectively. Additionally, to further validate the model, four experimental recycled self-compacting concrete (RSCC) samples were tested experimentally, and their properties were used as unseen data to the model. The results showed that the developed model can predict the compressive strength of RSCC with high accuracy.

scholarly journals Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Kraiwut Tuntisukrarom ◽  
Raungrut Cheerarot
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Bottom Ash ◽  
Strength Development ◽  
Ann Model ◽  
Explicit Equation ◽  
Strength Behavior ◽  
Artificial Neural ◽  
Hidden Layer

The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.

scholarly journals Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Xiong ◽  
Feng Gao ◽  
Keping Zhou ◽  
Yuxu Gao ◽  
Chun Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Rock Mass ◽  
Ann Model ◽  
Cold Regions ◽  
Freeze Thaw ◽  
Triaxial Compressive Strength ◽  
Artificial Neural

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

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.

Predicting the Workability of Self-Compacting Concrete Using Artificial Neural Network

2010 ◽  
Vol 168-170 ◽  
pp. 1730-1734
Author(s):  
Fang Xian Li ◽  
Qi Jun Yu ◽  
Jiang Xiong Wei ◽  
Jian Xin Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Self Compacting Concrete ◽  
Data Set ◽  
Slump Flow ◽  
Super Plasticizer ◽  
Input Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) is presented to predict the workability of self compacting concrete (SCC) containing slump, slump flow and V-test. A data set of a laboratory work, in which a total of 23 concretes were produced, was utilized in the ANNs study. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, fly ash, blast furnace slag, super plasticizer, sand ratio and water/binder, three output parameters which are slump, slump flow and V-test of SCC. ANN-1, ANN-2 and ANN-3 models which containing 15 ,11 and 5 neurons in the hidden layers, respectively are found to predict workability of concrete well within the ranges of the input parameters considered. The three models are tested by comparing to the results to actual measured data. The results showed that ANN-2 is the best suitable for predicting the workability of SCC using concrete ingredients as input parameters.

scholarly journals Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network

2020 ◽  
Vol 12 (22) ◽  
pp. 9322
Author(s):  
Tawfiq Al-Mughanam ◽  
Theyazn H. H. Aldhyani ◽  
Belal Alsubari ◽  
Mohammed Al-Yaari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Palm Oil ◽  
Self Compacting Concrete ◽  
Anfis Model ◽  
Palm Oil Fuel Ash ◽  
Fuel Ash ◽  
Artificial Neural ◽  
Oil Fuel

The utilization of a high-volume of treated palm oil fuel ash (T-POFA) as a partial cement substitution is one of the solutions presented to reduce carbon dioxide emissions (CO2) and improve concrete sustainability. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is adapted as an artificial neural network (ANN) modeling tool to predict the compressive strength of self-compacting concrete (SCC) containing T-POFA. The ANFIS model has been developed and validated using concrete mixtures incorporating 0%, 10 wt%, 20 wt%, 30 wt%, 50 wt%, 60 wt%, and wt 70% T-POFA as a replacement of ordinary Portland cement (OPC) at a constant water/binder (W/B) ratio of 0.35. The experimental data were divided into 70% training data and 30% testing data. The experimental results of self-compacting concrete (SCC) containing T-POFA ensured comparable or higher compressive strengths, especially at later ages, when compared to the control SCC. However, the prediction results of the compressive strength of SCC samples using the ANFIS model are very close to the experimental values. The developed ANFIS model showed a highly-efficient performance to predict the SCC compressive strength. In addition, the obtained accurate predicted results using the developed ANN model will significantly affect the current experimental protocols, especially for costly and unsafe experiments.

scholarly journals Artificial neural network prediction of quantitative structure: Retention relationships of polycyclic aromatic hydocarbons in gas chromatography

2005 ◽  
Vol 70 (11) ◽  
pp. 1291-1300 ◽  
Author(s):  
Snezana Sremac ◽  
Biljana Skrbic ◽  
Antonije Onjia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Chromatography ◽  
Retention Indices ◽  
Retention Data ◽  
Ann Model ◽  
Linear Temperature ◽  
Unseen Data ◽  
Polycyclic Aromatic ◽  
Artificial Neural

A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature- programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al. [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (+-3 %).

scholarly journals Bayesian Regularized Artificial Neural Network Model to Predict Strength Characteristics of Fly-Ash and Bottom-Ash Based Geopolymer Concrete

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1729
Author(s):  
Sakshi Aneja ◽  
Ashutosh Sharma ◽  
Rishi Gupta ◽  
Doo-Yeol Yoo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Fly Ash ◽  
Bottom Ash ◽  
Geopolymer Concrete ◽  
Ann Model ◽  
Testing Procedures ◽  
Precursor Material ◽  
Artificial Neural

Geopolymer concrete (GPC) offers a potential solution for sustainable construction by utilizing waste materials. However, the production and testing procedures for GPC are quite cumbersome and expensive, which can slow down the development of mix design and the implementation of GPC. The basic characteristics of GPC depend on numerous factors such as type of precursor material, type of alkali activators and their concentration, and liquid to solid (precursor material) ratio. To optimize time and cost, Artificial Neural Network (ANN) can be a lucrative technique for exploring and predicting GPC characteristics. In this study, the compressive strength of fly-ash based GPC with bottom ash as a replacement of fine aggregates, as well as fly ash, is predicted using a machine learning-based ANN model. The data inputs are taken from the literature as well as in-house lab scale testing of GPC. The specifications of GPC specimens act as input features of the ANN model to predict compressive strength as the output, while minimizing error. Fourteen ANN models are designed which differ in backpropagation training algorithm, number of hidden layers, and neurons in each layer. The performance analysis and comparison of these models in terms of mean squared error (MSE) and coefficient of correlation (R) resulted in a Bayesian regularized ANN (BRANN) model for effective prediction of compressive strength of fly-ash and bottom-ash based geopolymer concrete.

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