The fracture properties (e.g. tensile strength, fracture toughness) are regarded as the material constants, which could be used to predict the fracture behavior of concrete. However, the scattered distribution and size effect of test results make the prediction in conventional methods difficult. In this study, the artificial intelligence (AI) approaches and the Boundary Effect Model (BEM) closed-form solution were tried to analyze such complex relations between the fracture test results and specimen geometries in the 3 PB tests. Firstly, a cluster of data were collected and divided into the training set and testing set. Then, based on the training data, the ensemble algorithm (Random Forests) and the Particle Swarm Optimization (PSO) were combined to establish the hybrid AI predictive model, and the fracture properties and predictive domain were determined with the BEM closed-form solution and normal distribution analysis. After that, the testing data were used to evaluate the behavior of these two predictive methods. The performance of the AI predictive model was quantified with R² = 0.947, and the unknown data in testing set all fell into the predicted domain which was determined by using the BEM predictive model. The merits of the two predictive methods in predicting the fracture performance of concrete specimens were compared and expected to integrate together in the future work.

断裂性能（例如抗张强度，断裂韧性）被视为材料常数，可用于预测混凝土的断裂行为。然而，测试结果的分散分布和尺寸效应使常规方法的预测变得困难。在这项研究中，尝试使用人工智能（AI）方法和边界效应模型（BEM）封闭形式解决方案来分析3 PB测试中断裂测试结果与试样几何形状之间的这种复杂关系。首先，收集一组数据并将其分为训练集和测试集。然后，基于训练数据，将集成算法（随机森林）和粒子群优化算法（PSO）相结合，以建立混合AI预测模型，通过BEM闭式解和正态分布分析确定断裂特性和预测范围。之后，将测试数据用于评估这两种预测方法的行为。AI预测模型的性能用R量化²  = 0.947，并且测试集中的未知数据全部落入了使用BEM预测模型确定的预测域中。比较了两种预测方法在预测混凝土试样断裂性能方面的优点，并有望在未来的工作中整合在一起。