Self-compacting mortar (SCM) has superior workability and long-term durable performance compared to traditional mortar. The strength of SCM, including both its compressive and flexural strengths, is a crucial property that is determined by appropriate curing conditions and mix design parameters. In the context of materials science, predicting the strength of SCM is challenging because of multiple influencing factors. This study employed machine learning techniques to establish SCM strength prediction models. Based on ten different input parameters, the strength of SCM specimens were predicted using two different types of hybrid machine learning (HML) models, namely Extreme Gradient Boosting (XGBoost) and the Random Forest (RF) algorithm. HML models were trained and tested by experimental data from 320 test specimens. In addition, the Bayesian optimization method was utilized to fine tune the hyperparameters of the employed algorithms, and cross-validation was employed to partition the database into multiple folds for a more thorough exploration of the hyperparameter space while providing a more accurate assessment of the model’s predictive power. The results show that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model demonstrated higher accuracy (R² = 0.96 for training and R² = 0.91 for testing phases) for predicting flexural strength with low error. In terms of compressive strength prediction, the employed BO-RF model performed very well, with R² = 0.96 for train and R² = 0.88 testing stages with minor errors. Moreover, the SHAP algorithm, permutation importance and leave-one-out importance score were used for sensitivity analysis to explain the prediction process and interpret the governing input variable parameters of the proposed HML models. Finally, the outcomes of this study might be applied to guide the future mix design of SCM specimens.

This study presents a comprehensive evaluation of three hybrid machine learning models XGB-LGB, RF-XGB, and ET-LGB for predicting the mechanical performance of Ultra-High-Performance Concrete (UHPC), including compressive strength (CS), flexural strength (FS), and tensile strength (TS). Each dataset was standardized and split into training (80%) and testing (20%) subsets. Hyperparameter optimization was conducted using a random search algorithm to improve prediction accuracy. The performance of each model was assessed using five key metrics: mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²). This work integrates hybrid ensemble models with SHAP-based explainable AI and uncertainty quantification to achieve high accuracy, model interpretability, and robustness assessment, which are rarely combined in UHPC prediction studies. Among the models, ET-LGB reliably achieved the highest accuracy across all target outputs, with R² values reaching 0.99 in both training and testing phases. XGB-LGB also demonstrated strong performance, particularly for CS and FS, achieving R² values of 0.99 and 0.98, respectively. In contrast, RF-XGB showed relatively lower accuracy, especially for TS, with R² values around 0.93. To improve model interpretability, SHAP-based sensitivity analysis including feature importance plots, beeswarm plots, and heatmaps were employed to analyze the contribution of input features to model predictions. Additionally, an uncertainty analysis was performed to measure the robustness of predictions. Overall, the ET-LGB model proved to be the most reliable and accurate, followed closely by XGB-LGB, demonstrating strong potential for practical UHPC property prediction.

In recent decades, wood structures have gained significant attention for their ecological benefits and architectural versatility. The performance of wood, a popular construction material, often depends on the integrity of its connections. This study focuses on bolted glulam timber connections, which are strong but prone to cracks that pose structural health challenges. Traditional crack evaluation methods are manual, time-consuming, and error-prone. To address these issues, this research proposes a two-stage performance evaluation method. In the first stage, an innovative approach called ‘Enhanced Deep Line Segment Detection’ (Deep LSD), a non-supervised machine learning technique, is used for crack detection without relying on large, annotated datasets, thus enhancing efficiency and adaptability. In the second stage, cyclic loading assays simulate varying damage stages to collect data and establish a correlation between crack states and connection damage. The Park and Ang damage model is employed within this framework to assess the extent of damage. The efficacy of enhanced deep LSD is confirmed by comparing detected crack areas with ground truth measurements, yielding a high R-squared value of 0.98 and a minimal error margin of 1.41. Additionally, a damage index based on the Chinese standard (GB/T 24335-2009) is used to classify damage across different connection groups, ensuring robustness and alignment with established practices.