Prediction of Rubber Fiber Concrete Strength Using Extreme Learning Machine

Abstract

The conventional design method of concrete mix ratio relies on a large number of tests for trial mixing and optimization, and the workload is massive. It is challenging to cope with today's diverse raw materials and the concrete's specific performance to fit modern concrete development. To innovate the design method of concrete mix ratio and effectively use the various complex novel raw materials, the traditional mix ratio test method can be replaced with the intelligent optimization algorithm, and the concrete performance prediction can be realized rapidly and accurately. The mixed ratio of the rubber fiber concrete was designed with its 28-day strength test. Then the range and variance analysis of the orthogonal test results were carried out to determine the optimal mix ratio and its influencing factors. A data set containing 114 sets of valid test data was collected by combining the rubber concrete mix test data published in recent years. Based on this data set, there are six influencing factors; rubber content, rubber particle size, and polypropylene fiber content are considered as the input variables, and the 28-day concrete compression, splitting tensile, and flexural strength are considered as the output variables. A strength prediction model of rubber fiber concrete is established based on the extreme learning machine (ELM). For verifying the ELM prediction model's performance, this article has conducted a comparison experiment between this model and other intelligent algorithm models. The results show that the model has the advantages of high accuracy and high generalization ability compared with other algorithm models such as conventional neural networks. It can be used as an effective method for predicting concrete performance. The method allows for the innovation and development of concrete mixing technology.

Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

“…In order to improve the convergence rate of the network and avoid the deviation adjustment of weight caused by dimensional differences [44], normalization was used for data preprocessing. The scale transformation of original data was conducted according to Equation (2), which is realized by [y, ps] = mapminmax (x, y min , y max ) in MATLAB, where ps represents the mapping relationship; x and y are the data before and after normalization; y max , y min is the maximum and minimum of normalized boundary, respectively; and x max , x min is the maximum and minimum of input before normalization, respectively.…”

“…Furthermore, abundant soft computing methods have been successfully applied to various materials. These advanced techniques include regression tree, random forest, support vector machine, extreme learning machine, genetic programming, and Gaussian process regression [2,45,46,58]. With the help of deep learning, more work is foreseeable in the future to broaden the application under different experimental conditions, to concrete with different material compositions, and eventually to structural components [20,54,59].…”

“…As a complex multi-phase composite material, concrete exhibits significant discreteness in fatigue life [1]. Moreover, since a nonlinear mapping relationship exists between fatigue life and its influencing factors [2], fatigue life estimation has become the focus of concrete fatigue research.…”

Hybridizing Grid Search and Support Vector Regression to Predict the Compressive Strength of Fly Ash Concrete

“…To address these issues, machine learning techniques are used to predict the compressive strength of concrete. In fact, with the development of artificial intelligence, various machine learning algorithms such as artificial neural network (ANN), support vector machines (SVM), random forest, and extreme learning machine (ELM) have been applied to predict the mechanical properties of concrete [12][13][14][15][16][17][18][19][20][21][22][23]. Ly et al [24] employed an optimal deep neural network model on a database of 223 experimental data to predict the 28 days compressive strength of rubber concrete and achieved a high prediction accuracy of R � 0.9874.…”

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

“…However, the laboratory test method has many disadvantages, such as low efficiency and high cost [37][38][39][40][41][42][43]. To find a more efficient and low-cost method to predict the performance of concrete, many researchers choose to use machine learning models to predict the properties of concrete [44][45][46][47][48][49][50][51]. Salimbahrami et al studied the compressive strength prediction methods of recycled concrete based on the artificial neural network (ANN) and support vector machine (SVM), and the research results show that machine learning models have good prediction effects on the compressive strength of recycled concrete [52].…”