Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete

Abstract

Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected influential factors. The effectiveness of the proposed model was tested on a case study, and the high Pearson correlation coefficient (0.9821) and the low mean absolute percentage error and delta (0.0394 and 0.395, respectively) indicate that the proposed model can deliver accurate and reliable results.

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

“…where C is the parameter used to stabilize the model complexity and empirical risk term ||ω || 2 , and ξ * i denotes the distance and is called a slack variable. The Lagrange multipliers technique is used to solve the dual optimization problem as expressed in Equation (16).…”

“…The BGR model achieved a low root mean square error (RMSE = 1.51 MPa) in the testing phase while employing the 80-20% data division scenario, whereas the DTR and SVR models achieved RMSE = 2.55 and 2.33 MPa, respectively. Xu et al [16] used ML to forecast the compressive strength of ready-mix concrete. Random forest (RF) was used as the modelling technique to predict the compressive strength from the selected influential elements after GA was used to find the most relevant influential factors for compressive strength modeming.…”

Correlation Between Mechanical Properties and Magnetic Properties of Structural Reinforcement Under Variable Load

“…De-Cheng Feng et al presented a new intelligent approach for forecasting concrete compressive strength based on the AdaBoost algorithm in this work. [4].To facilitate a full evaluation of the concrete manufacturing process, influential aspects from five viewpoints were gathered by J. Xu et al [5].In A. K. Jha et al study, the first eight features are elements impacting concrete compressive strength, while the final characteristic is the value of concrete compressive strength. Some of the models used are the Linear Regressor, Ridge Regressor, Lasso Regressor, Decision Tree Regressor, Random Forest Regressor, AdaBoost Regressor, and Gradient Boosting Regressor.…”

Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

“…Back-propagating neural networks (RBPNNs) [10], artificial neural networks (ANNs) [11], [12], [13], [14] [15], [2], [16], [17], [18], [19], [20], adaptive neuro-fuzzy inference systems (ANFIS) [21], [22], [23], [24], and have been utilized to forecast concrete qualities, damage detection, shear, and flexural strength and compressive strength prediction in recent decades. Other machine learning techniques such as random forest (RF) [25], [26], support vector machine (SVM) [27], [28], [29] are widely using for optimization and to evaluate the compressive strength of the concrete specimens.…”