Utilizing Artificial Neural Network and Multiple Linear Regression to Model the Compressive Strength of Recycled Geopolymer Concrete

Based on the heterogeneity nature of concrete constituents and variation in its compressive strength over several orders of magnitude for various types of concrete, predictive methods to evaluate its compressive strength has now been given an appropriate consideration. Therefore, this study investigates, the performance of Artificial Neural Network, ANN in forecasting the compressive strength of hybrid alkali-activated recycled concrete (HAARC) and compared with the traditional Multiple Linear Regression, MLR. The developed models utilized the experimental results where varying material quantities were used. The ANN and MLR models were developed using eight input variables namely; Ordinary Portland cement (OPC), Rice Husk Ash (RHA), Coal Fly Ash (CFA), Crushed granite (CG), Cupola Furnace Slag (CFS), Alkaline Solution (AS), Water-Binder Ratio (WB) and the Concrete Age (CA) while compressive strength was the only response (predicted) variable. The input data were trained, tested and validated using feedforward back-proportion and backward elimination approach for ANN and MLR, respectively. The most probable model architecture containing eight-input layer, thirteen-hidden layer, and one-output layer neurons was selected based on satisfactory performance in terms of means square error MSE, after several trials. MLR results revealed that only three input variables; CFA, CG and CA proves to be statistically significant with p-values below 0.05. Performance evaluations of the developed models using coefficient of determination, R2, Mean Square Error, MSE, Root Mean-Square Error, RMSE and Mean Absolute Percentage Error, MAPE, showed that ANN prediction accuracy is better than that of MLR with R2 = 0.9972, MSE = 0.4177, RMSE = 1.8201, MAPE = 2.2935 for ANN and R2 = 0.7410, MSE = 66.6308, RMSE = 290.4370, MAPE = 385.5221 for MLR

MAT-701: PREDICTING THE COMPRESSIVE STRENGTH OF ULTRA-LIGHTWEIGHT CONCRETE BY AN ARTIFICIAL NEURAL NETWORK

Ultra-lightweight concrete (ULWC) has potential applications for floating structures and architectural elements because of its dry density coming in at under 1000 kg/m3. The objective was to develop an artificial neural network (ANN) to aid the ULWC designer according to his needs. Boundary conditions were set for each material and 13 constraints based on the water binder ratio, density, air content, binder and aggregate content. The ANN predicted the compressive strength with a comfortable margin of error, with the gap encountered being attributed to variability in workability. Precise constraints and boundary conditions are needed to ensure a lower variability in workability. The ANN, coupled with a genetic algorithm, can generate millions of mixes for a given compressive strength in a short amount of time. The designer is able to choose mixes according to additional needs, such as the carbon footprint, absolute density, polymer content, cost, etc

Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks

[[abstract]]This paper is aimed at demonstrating the possibilities of adapting Quantile Regression Neural Network (QRNN) to estimate the distribution of compressive strength of high performance concrete (HPC). The database containing 1030 compressive strength data were used to evaluate QRNN. Each data includes the amounts of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate (in kilograms per cubic meter), the age, and the compressive strength. This study led to the following conclusions: (1) The Quantile Regression Neural Networks can build accurate quantile models and estimate the distribution of compressive strength of HPC. (2) The various distributions of prediction of compressive strength of HPC show that the variance of the error is inconstant across observations, which imply that the prediction is heteroscedastic. (3) The logarithmic normal distribution may be more appropriate than normal distribution to fit the distribution of compressive strength of HPC. Since engineers should not assume that the variance of the error of prediction of compressive strength is constant, the ability of estimating the distribution of compressive strength of HPC is an important advantage of QRNN.[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]CH

A Fuzzy Interface System to Predict Ultimate Strength of Circular Concrete Filled Steel Tubular Columns

In this study, a model for predicting the ultimate strength of circular concrete filled steel tubular columns (CCFST) under axial loads has been developed using fuzzy inference system (FIS). The available experimental results for (129) specimens obtained from open literature were used to build the proposed model. The predicted strengths obtained from the proposed FIS model were compared with the experimental values and with unfactored design strengths predicted using the design procedure specified in the AISC 2005 and Eurocode 4 for CCFST columns. Results showed that the predicted values by the proposed FIS model were very close to the experimental values and were more accurate than the AISC 2005 and Eurocode 4 values. As a result, FIS provided an efficient alternative method in predicting the ultimate strength of CCFST columns

Application of Artificial Neural Network to Predict the Properties of Permeable Concrete

The structure of permeable concrete has been the primary reason for its use in construction. Permeable concrete is composed of water, cement, aggregate, and little-to no-fines resulting in the presence of a significant number of voids. This makes permeable concrete an ideal solution to water accumulation issues as it acts as a drainage system. This study employs a feedforward backpropagation artificial neural network model that combines experimental laboratory data from previous studies with appropriate network architectures and training techniques. The purpose of the analysis is to develop a reliable functional relationship, based on water-cement ratio, aggregate-cement ratio, and density parameters, with which to estimate the compressive strength, porosity, and water permeability of permeable concrete. Multiple linear regression correlations are also established to predict and correlate these inputs and outputs. The two derived methods are then compared and discussed. The results reveal that ANN is better to anticipate the permeable concrete properties than regression analysis

Prediction of strength and slump of rice husk ash incorporated high-performance concrete

This paper describes the development of statistical models to predict strength and slump of rice husk ash (RHA) incorporated high-performance concrete (HPC). Sixty samples of RHA incorporated HPC mixes having compressive strength range of 42–92 MPa and slump range of 170–245 mm were prepared and tested in the laboratory. These experimental data of sixty RHA incorporated HPC mixes were used to develop two models. Six variables namely water-to-binder ratio, cement content, RHA content, fine aggregate content, coarse aggregate content and superplasticizer content were selected to develop the models and ultimately to predict strength and slump of RHA incorporated HPC. The models were developed by regression analysis. Additional five HPC mixes were prepared with the same ingredients and tested under the same testing conditions to verify the ability of the proposed models to predict the responses. The results of the prediction of the models showed good agreement with the experimental data. Thus the developed models can be used to predict slump and 28-day compressive strength of RHA incorporated HPC. The research demonstrated that strength and slump of HPC could be successfully modeled using statistical analysis

Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined