Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

Abstract

Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable’s effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.

Forecasting the rheological state properties of self-compacting concrete mixes using the response surface methodology technique for sustainable structural concreting

“…Self-compacting concrete (SCC) is a specialized type of concrete that flows and settles under its own weight without the need for mechanical vibration, making it particularly useful in applications where traditional concrete placement methods are impractical [ 1 – 15 ]. Rheological properties, which include yield stress, plastic viscosity, flowability, stability, etc., play a crucial role in determining the flow behavior and stability of SCC mixes [ 14 – 17 ]. Flowability is one of the most important rheological properties of SCC.…”

“…Flowability is one of the most important rheological properties of SCC. It refers to the ability of the concrete mix to flow and spread into formwork under its own weight without segregation or excessive bleeding [ 14 ]. Flowability is typically assessed using slump flow tests, where the diameter of the concrete spread is measured after it has been allowed to flow freely [ 16 ].…”

Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods

“…The SHAP algorithm is widely used in order to explain the impact of different input features on the predictions of the machine learning models [40][41][42][43][44][45]. The SHAP methodology is based on an additive feature attribution procedure in which an explanation function g is defined as a linear combination of simplified input values 𝑥 ′ ∈ {0,1} 𝑀 , where M is the total number of simplified input features.…”

Use of Machine Learning Techniques in Soil Classification

“…In recent years, the architecture, engineering, and construction (AEC) industry has been benefiting much from artificial intelligence and machine learning. Several studies on machine learning in civil engineering exist in the recent literature, including a classification of failure modes [15,16], performance classifications and the prediction of reinforced masonry structures [17], a prediction of the optimum parameters of passive-tuned mass dampers [18,19], an estimation of the optimum design of structural systems [20], prediction models for optimum fiber-reinforced polymer beams [21,22], a prediction of the axial compression capacity of concrete-filled steel tubular columns [23], a prediction of the bearing strength of double shear bolted connections [24], and predictions of the shear stress and plastic viscosity of self-compacting concrete [25] and for the optimum design of cylindrical walls [26].…”