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

Abstract

Support vector regression (SVR) has been applied to the prediction of mechanical properties of concrete, but the selection of its hyperparameters has been a key factor affecting the prediction accuracy. To this end, hybrid machine learning combines the SVR model and grid search (GS), namely, the GS-SVR model was proposed to predict the compressive strength of concrete and sensitivity analysis in this work. The hybrid model was trained and tested on a total of 98 datasets retrieved from literature, and the model performance was compared with the original SVR model under the same datasets. The obtained results in terms of R of 0.981, MSE of 3.44, RMSE of 1.85, MAE of 1.17, and MAPE of 0.05 demonstrate that the GS-SVR model proposed can be a candidate method for compressive strength prediction in subsequent related studies. Additionally, a graphical user interface (GUI) was developed to conveniently provide some initial estimates of the outcomes before performing extensive laboratory or fieldwork. Finally, the effect of each variable on the compressive strength in a random environment was analyzed.

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

“…Discussing the results obtained and comparing them with the results previously obtained by other authors, it should be noted that the chosen question is unconditionally novel. The fact is that predicting the properties of concrete was previously known and described in works [6,11,15,17,18,20,21,[23][24][25][26][27][28]30,33,36,40,41,55]. The same study touches upon the topic of predicting the properties of special concretes, that is, variatropic concretes obtained using vibrocentrifuge technology.…”

“…For example, in studies [19], the SVR method was used to assess the durability of high-performance basalt fiber reinforced concrete and showed fairly high accuracy. Similarly, in research [20][21][22][23][24], this machine learning method makes it possible to quite accurately predict the concrete characteristics by including various additives (fly ash, microsilica) and subject to various types of impacts. In general, the introduction of machine learning methods allows to save costs on the production of many experimental samples and the procedure for testing them, as well as significantly speeds up the process of obtaining future properties of a concrete composite [25].…”

Walk-Through Corrosion Assessment of Slurry Pipeline Using Machine Learning

“…Grid search, also known as parameter sweep, has traditionally been the preferred method for hyperparameter optimization, which involves an exhaustive search through a manually specified subset of the algorithm's hyperparameter space. Grid search is guided by a performance metric, which is typically measured by cross-validation on the training set [44,45]. For the SVM model, we aim to identify the optimal values for C and gamma.…”

Prediction of Second-Hand Sailboat Prices Based on GA-BP Model

“…A small learning rate can lead to slow model convergence, while a large learning rate may lead to the model not being able to converge. The traditional parameter optimization methods include grid search (GS) [8] and random search (RS) [9]. GS is a search performed in parameter space with a certain step size.…”