Non-Destructive Concrete Strength Prediction Using AI: A Comparative Study of Machine Learning and Deep Learning Models DOI

Nima Ekhteraey,

Milad Ekhteraei, Mohammad Sattari

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

Abstract Accurate prediction of concrete's mechanical properties is a crucial aspect civil engineering, ensuring the structural integrity and durability constructions. Traditional destructive testing methods, while reliable, are time-consuming resource-intensive. This study presents novel, non-destructive approach for predicting compressive, tensile, flexural strengths concrete using only two input parameters: Ultrasonic Pulse Velocity (UPV) Electrical Resistivity (ER). A comparative analysis was conducted utilizing five machine learning deep models: Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN). The results demonstrated that CNN outperformed all other models, achieving lowest Root Mean Square Error (RMSE) Relative (MRE) across three strength predictions. Specifically, achieved an MRE 1.37% compressive strength, 1.25% tensile 1.76% highlighting its superior predictive accuracy compared to traditional models. CNN's strong performance stems from ability learn deep, non-linear feature hierarchies minimal inputs. By capturing complex spatial functional dependencies between UPV ER, can model intricate behavior more effectively than shallow makes it particularly suitable tasks involving highly physical phenomena, such as characteristics indirect measurements. research highlights potential AI-driven efficient alternative offering significant advantages in terms cost reduction, speed, sustainability construction industry.

Язык: Английский

Non-Destructive Concrete Strength Prediction Using AI: A Comparative Study of Machine Learning and Deep Learning Models DOI

Nima Ekhteraey,

Milad Ekhteraei, Mohammad Sattari

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 9, 2025

Abstract Accurate prediction of concrete's mechanical properties is a crucial aspect civil engineering, ensuring the structural integrity and durability constructions. Traditional destructive testing methods, while reliable, are time-consuming resource-intensive. This study presents novel, non-destructive approach for predicting compressive, tensile, flexural strengths concrete using only two input parameters: Ultrasonic Pulse Velocity (UPV) Electrical Resistivity (ER). A comparative analysis was conducted utilizing five machine learning deep models: Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN). The results demonstrated that CNN outperformed all other models, achieving lowest Root Mean Square Error (RMSE) Relative (MRE) across three strength predictions. Specifically, achieved an MRE 1.37% compressive strength, 1.25% tensile 1.76% highlighting its superior predictive accuracy compared to traditional models. CNN's strong performance stems from ability learn deep, non-linear feature hierarchies minimal inputs. By capturing complex spatial functional dependencies between UPV ER, can model intricate behavior more effectively than shallow makes it particularly suitable tasks involving highly physical phenomena, such as characteristics indirect measurements. research highlights potential AI-driven efficient alternative offering significant advantages in terms cost reduction, speed, sustainability construction industry.

Язык: Английский

Процитировано

0