Jan 2, 2024 · Machine learning (ML) is a notable benefit in effectively handling extensive and sophisticated datasets. This capability allows for identifying underlying patterns and producing precise predictions with remarkable accuracy.

Jan 10, 2020 · In this paper, an intelligent approach based on the machine learning technique is proposed for predicting the compressive strength of concrete. This approach employs the adaptive boosting algorithm to construct a strong learner by integrating several weak learners, which can find the mapping between the input data and output data.

Dec 16, 2023 · Accurate prediction of compressive strength (CS), a crucial mechanical property, is essential for optimal results. The complex nature of SCC mixtures has led researchers to explore modern days tool like machine learning and artificial intelligence.

Apr 22, 2025 · This study presents a robust framework for predicting the compressive strength of ultra-high performance concrete (UHPC) using machine learning models, based on a comprehensive data set of 761 data points derived from various UHPC mix designs.

Jan 8, 2025 · In this study, the advanced machine learning techniques are employed to predict the strength. The paper explores multiple base models, such as linear regression (including polynomial features up to degree 3), decision trees, support vector …

Nov 16, 2024 · To enhance the accuracy of concrete strength prediction, this paper introduces an interpretable framework for machine learning (ML) models to predict the compressive strength of...

Mechanical properties, such as compressive strength and modulus of elasticity can determine the workability, serviceability, durability, and quality control of concrete.

Apr 23, 2025 · Traditional machine learning (ML) models have primarily focused on deterministic predictions of compressive strength, often overlooking the uncertainty associated with these estimates. However, concrete is a non-homogeneous material with complex and variable behaviour, making it inherently difficult to predict compressive strength with precision.

Machine learning methods such as the “Group Methods Data Handling Neural Network (GMDH-NN)”, “Generalized Support Vector Regression (GSVR), “K-Nearest Neighbors (KNN)”, “Tree Decision (Tree)”, “Random Forest (RF)” and “Extreme Gradient Boosting (XGBoost)” were applied to model the compressive strength of the self-healing ...

Nov 29, 2019 · Herein, seven different regression methods (i.e., DT, RF, support vector machine, partial least squares, artificial neural networks, bootstrap aggregation (bagging), and FL) were used to predict the concrete compressive strength and slump values.