Foamed concrete is special not only in terms of its unique properties, but also in terms of its challenging compositional mixture design, which necessitates multiple experimental trials before obtaining the desired property like compressive strength. Regardless of design challenges, artificial intelligence (AI) techniques have shown to be useful in reliably estimating desired concrete properties based on optimized mixture proportions. This study proposes AI-based models to predict the compressive strength of foamed concrete. Three novel AI approaches, namely artificial neural network (ANN), gene expression programming (GEP), and gradient boosting tree (GBT) models, were employed. The models were developed using 232 experimental results, considering easily acquired variables, such as the density of concrete, water-cement ratio and sand-cement ratio as inputs to estimate the compressive strength of foamed concrete. In training the models, 80% of the experimental data was used and the rest was used to validate the models. The optimized models were selected using their respective best hyper-parameters on trial and error basis; variable number of hidden layers, number of neurons and training algorithms were used for ANN, number of chromosomes, head size, number of genes, variable function set for the GEP and GBT employed number of trees, maximal depth and learning rate. The trained models were validated using parametric and sensitivity analyses of a simulated dataset. The prediction abilities of proposed models were evaluated using the coefficient of correlation (R), mean absolute error (MAE), and root mean squared error (RMSE). For the validation data, empirical results from the performance evaluation revealed that GBT model (R = 0.977, MAE = 1.817 and RMSE = 2.69) has relative superior performance with highest correlation and least error in comparison with ANN (R = 0.975, MAE = 2.695 and RMSE = 3.40) and GEP (R = 0.96, MAE = 2.07 and RMSE = 2.80). The study concludes that the developed GBT model offered reliable accuracy in predicting the compressive strength of foamed concrete. Finally, the simple prediction equation generated from the GEP model signifies its importance and can reliably be used in estimating compressive strength of foamed concrete. It is recommended that the prediction models shall be used for the ranges of input variables employed in this study.

泡沫混凝土的特殊之处不仅在于其独特的性能，而且还在于其具有挑战性的组成混合物设计，这需要进行多次实验试验才能获得所需的性能，如抗压强度。无论设计挑战如何，人工智能 (AI) 技术已被证明可用于基于优化的混合比例可靠地估计所需的混凝土性能。本研究提出了基于人工智能的模型来预测泡沫混凝土的抗压强度。采用了三种新的人工智能方法，即人工神经网络 (ANN)、基因表达编程 (GEP) 和梯度提升树 (GBT) 模型。这些模型是使用 232 个实验结果开发的，考虑了容易获得的变量，例如混凝土的密度，水灰比和砂灰比作为输入来估计泡沫混凝土的抗压强度。在训练模型时，使用了 80% 的实验数据，其余的用于验证模型。在反复试验的基础上，使用它们各自的最佳超参数选择优化模型；ANN 使用了可变数量的隐藏层、神经元数量和训练算法、染色体数量、头部大小、基因数量、GEP 和 GBT 的可变函数集使用的树数、最大深度和学习率。使用模拟数据集的参数和敏感性分析对训练后的模型进行了验证。使用相关系数 (R)、平均绝对误差 (MAE) 和均方根误差 (RMSE) 评估所提出模型的预测能力。对于验证数据，性能评估的经验结果表明，与人工神经网络（R = 0.975，MAE = 2.695 和 RMSE = 3.40）相比，GBT 模型（R = 0.977，MAE = 1.817 和 RMSE = 2.69）具有相对优越的性能，相关性最高，误差最小。和 GEP（R = 0.96，MAE = 2.07 和 RMSE = 2.80）。研究得出结论，开发的 GBT 模型在预测泡沫混凝土的抗压强度方面提供了可靠的准确性。最后，从 GEP 模型生成的简单预测方程表明了它的重要性，并且可以可靠地用于估计泡沫混凝土的抗压强度。建议将预测模型用于本研究中使用的输入变量范围。69）与 ANN（R = 0.975，MAE = 2.695 和 RMSE = 3.40）和 GEP（R = 0.96，MAE = 2.07 和 RMSE = 2.80）相比，具有相对优越的性能，相关性最高，误差最小。研究得出结论，开发的 GBT 模型在预测泡沫混凝土的抗压强度方面提供了可靠的准确性。最后，从 GEP 模型生成的简单预测方程表明了它的重要性，并且可以可靠地用于估计泡沫混凝土的抗压强度。建议将预测模型用于本研究中使用的输入变量范围。69）与 ANN（R = 0.975，MAE = 2.695 和 RMSE = 3.40）和 GEP（R = 0.96，MAE = 2.07 和 RMSE = 2.80）相比，具有相对优越的性能，相关性最高，误差最小。研究得出结论，开发的 GBT 模型在预测泡沫混凝土的抗压强度方面提供了可靠的准确性。最后，从 GEP 模型生成的简单预测方程表明了它的重要性，并且可以可靠地用于估计泡沫混凝土的抗压强度。建议将预测模型用于本研究中使用的输入变量范围。研究得出结论，开发的 GBT 模型在预测泡沫混凝土的抗压强度方面提供了可靠的准确性。最后，从 GEP 模型生成的简单预测方程表明了它的重要性，并且可以可靠地用于估计泡沫混凝土的抗压强度。建议将预测模型用于本研究中使用的输入变量范围。研究得出结论，开发的 GBT 模型在预测泡沫混凝土的抗压强度方面提供了可靠的准确性。最后，从 GEP 模型生成的简单预测方程表明了它的重要性，并且可以可靠地用于估计泡沫混凝土的抗压强度。建议将预测模型用于本研究中使用的输入变量范围。