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Cement paste possesses complex microstructural features including defects/pores over a range of length-scales, from nanometres to millimetres in size. As a consequence, it exhibits different behaviour under loading depending on the size. In this work, cubic specimens in a size range of 1: 400 were produced and tested by a onesided splitting concept using different testing instruments. The smallest specimen with size of 100 μm showed a high nominal splitting strength (18.81 MPa), an order of magnitude higher than the measured strength of 40 mm specimen (1.8 MPa). The test results were used to fit existing analytical size effect models. Although a good fit can be found for the existing size effect models, special attention should be given to the physical meaning behind these empirical parameters. In addition, a multi-scale modelling strategy that considers microstructural features at different length scales was adopted to model the trend of decreasing strength with specimen size observed in experiments. A good agreement between experimental observations and modelling results indicates that the featured material structure dominates the observed size effect on measured strength in the size range considered.

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Compressive strength is the most significant metric to evaluate the mechanical properties of concrete. Machine learning (ML) methods have shown promising results for predicting compressive strength of concrete. However, at present, no in-depth studies have been devoted to the influence of dimensionality reduction on the performance of different ML models for this application. In this work, four representative ML models, i.e., Linear Regression (LR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are trained and used to predict the compressive strength of concrete based on its mixture composition and curing age. For each ML model, three kinds of features are used as input: the eight original features, six Principal Component Analysis (PCA)-selected features, and six manually selected features. The performance as well as the training speed of those four ML models with three different kinds of features is assessed and compared. Based on the obtained results, it is possible to make a relatively accurate prediction of concrete compressive strength using SVR, XGBoost, and ANN with an R-square of over 0.9. When using different features, the highest R-square of the test set occurs in the XGBoost model with manually selected features as inputs (R-square = 0.9339). The prediction accuracy of the SVR model with manually selected features (R-square = 0.9080) or PCA-selected features (R-square = 0.9134) is better than the model with original features (R-square = 0.9003) without dramatic running time change, indicating that dimensionality reduction has a positive influence on SVR model. For XGBoost, the model with PCA-selected features shows poorer performance (R-square = 0.8787) than XGBoost model with original features or manually selected features. A possible reason for this is that the PCA-selected features are not as distinguishable as the manually selected features in this study. In addition, the running time of XGBoost model with PCA-selected features is longer than XGBoost model with original features or manually selected features. In other words, dimensionality reduction by PCA seems to have an adverse effect both on the performance and the running time of XGBoost model. Dimensionality reduction has an adverse effect on the performance of LR model and ANN model because the R-squares on test set of those two models with manually selected features or PCA-selected features are lower than models with original features. Although the running time of ANN is much longer than the other three ML models (less than 1s) in three scenarios, dimensionality reduction has an obviously positive influence on running time without losing much prediction accuracy for ANN model.