Prediction Model of Compressive Strength Development in Concrete Containing Four Kinds of Gelled Materials with the Artificial Intelligence Method

Abstract

Green concrete has been widely used in recent years because its production compliments environmental conservation. The prediction of the compressive strength of concrete using non-destructive techniques is of interest to engineers worldwide. Such methods are easy to carry out because they require little or no sample preparation. Conventional models and artificial intelligence models are two main types of models to predict the compressive strength of concrete. Artificial intelligence models main include the artificial neural network (ANN) model, back propagation (BP) neural network model, fuzzy model etc. Since both conventional models and artificial intelligence models are flawed. This study proposes to build a concrete compressive strength development over time (CCSDOT) model by using conventional method combined with the artificial intelligence method. The CCSDOT model performed well in predicting and fitting the compressive strength development in green concrete containing cement, slag, fly ash, and limestone flour. It is concluded that the CCSDOT model is stable through the use of sensitivity analysis. To evaluate the precision of this model, the prediction results of the proposed model were compared to that of the model based on the BP neural network. The results verify that the recommended model enjoys better flexibility, capability, and accuracy in predicting the compressive strength development in concrete than the other models.

The Influence and Application of Slag, Fly Ash, and Limestone Flour on Compressive Strength of Concrete Based on the Concrete Compressive Strength Development over Time (CCSDOT) Model

“…Besides the CCSDOT model, this study also adopts the modified sequence quadratic method, with the monitoring technology combining constraint condition and objective function, and temporary expansion of the feasible domain method, to obtain the optimum solution of Equation (1). Interested readers can refer to Liu [49]. Liu only applied the CCSDOT model to the green concrete.…”

“…Next, we adopted modified sequence quadratic programming (SQP) [49], which combined the monitoring technology combining constraint condition and objective function and temporary expansion of the feasible domain method, and then put the data of concrete proportions and the corresponding tested compressive strength at each concrete age into the model. Afterwards, the 12 parameters, including S C , ss SL , ss FA , ss LF , i C , i SL , i FA , i LF , λ C , λ SL , λ FA , λ LF , could be calculated.…”

“…Both aforementioned models are flawed. The concrete compressive strength development over time (CCSDOT) model [49] generates explicit formulas that provide important advantages to reveal the nature of influence of the supplementary cementitious materials on concrete compressive strength. Therefore, it is adopted in this study.…”

Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

“…Chithra et al [23] investigated the applicability of ANN for predicting the compressive strength of HPC containing nanosilica and copper slag. Several other researchers have used ANN-either individually, as a hybrid with other methods, or in ensemble models to predict the compressive strength of HPC [3,12,[24][25][26].…”

Implementasi Big Data untuk Pemodelan Estimasi Kuat Tekan Beton dengan Metode Linear Regression

“…Persamaan atau rumus ini diperoleh dari pelatihan dan pengujian data dengan metode linear regresi. Linear regresi merupakan model baru yang diusulkan untuk memprediksi kekuatan tekan beton dan elastisitas dari kecepatan pulsa ultrasonik [15]. Model ini dikembangkan untuk beton Concrete with alkali-activated binder (AAB) tanpa tekanan dan bertekanan dengan suhu yang berbeda.…”